queue_annotate_aj.json

[{"key": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-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_aj", "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": "aj-024", "text": "NER) at the primary level in the\nsurvey year (1996) was 50% greater for the highest expenditure quintile compared to the lowest\nexpenditure quintile. The inequity is even more pronounced in secondary education (lower secondary\neducation is part of basic education but the survey data does not separate the two), where the NER of\nthe highest quintile was more than 420% higher than the NER of the lowest quintile. The income\ndifferences in enrollment are significantly higher than other countries in Africa. The problem in urban\nareas is access - demand exists among all groups but the rationing of school intake ends up benefiting\nthe better off. Any further expansion of places will help the poorer segments of the population. Thus,\npublic expenditure in basic education is justified both on the public good rationale and also on the\nequity rationale.\n\n\nThe ten-year program proposed by the Government will also result in efficiency gains through lowered\nrepetition and drop out rates. This will result in reducing the average number of years to graduate\nfrom the primary and middle school levels, resulting in large potential savings in recurrent and\ninvestment expenditures in the long-run. The net present value of these public expenditure savings was\nfound to be significantly higher than the net present value of the investment costs required by the\nprogram. This is based on the difference between what the Government would have had to spend in\nthe absence of system reforms to educate the same number of students to the basic education level and\nwhat the Government would spend with systemic reforms. These savings yield an internal rate of over\n\n11% which clearly justifies the investment. This rate of return is actually an understatement for the\nprogram because the benefits do not include benefits from economic development, externalities and\nreduction in enrollment differentials between the poor and the better-off.", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:009641:19:1:0", "start": 272, "end": 283, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9509, "luna_label": 1}]}, {"key": "aj-025", "text": "**2.Compensation for Residential Housing Units**\n\nThe 2 <sup>nd</sup> group of PAPs who qualify for compensation under the programme are landowners who also have\nresidential housing units and other assets in their respective villages. These have been presented in the census\ndocument as **PAPs Category 1** . While all claimants to landownership in each village qualify for the “land-for-land”\ncompensation regardless of whether they were residents in their respective villages during the census in 2009,\ncompensation for housing units, i.e. construction of houses at the resettlement site, will cover only those\nlandowner PAPs who had household residential housing units in their respective villages at the time of the census\ncut off date, i.e. 16 <sup>th</sup> September 2009, and confirmed cases of natural growth as presented during the public\ndisclosure meeting on 24 <sup>th</sup> May 2012.\n\nAccording to GiBB, the 2009 census and the May 24 <sup>th</sup> 2012 public disclosure meeting confirmed that a total of 164\nhousing units would be constructed at the resettlement site as compensation for the housing units that the PAPs\nwould leave behind during relocation. Analysis of the census data however show that a total of 161 PAPs qualify\nfor residential housing units. These are as shown in Table 4 below.\n\n**Table 4: Housing units to be constructed at the resettlement site (see Summary Sheet 4 of the census report for**\n\n|details)|Col2|\n|---|---|\n|**Settlement**|**Number of Housing Units**|\n|Oloonongot|43|\n|Oloosinyat|20|\n|Cultural Centre|49|\n|Oloomayana Ndogo|49|\n|**Total**|**161", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:017172:55:0:1", "start": 1189, "end": 1200, "surface": "census data", "probe_tag": "keep", "probe_score": 0.9134, "luna_label": 1}]}, {"key": "aj-026", "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_aj", "spans": [{"key": "fcv_pads_east_africa:020602:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1}, {"key": "fcv_pads_east_africa:020602:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1}]}, {"key": "aj-027", "text": " of books and materials. They prefer to use\ntheir constrained resources for their boys who they feel have a better labor market potential. Finally,\nthe data from the Household Survey, showed that even if girls go to school, their parents pull them\nout at an age when they think they can help around the household.", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:013669:38:2:0", "start": 166, "end": 182, "surface": "Household Survey", "probe_tag": "keep", "probe_score": 0.966, "luna_label": 1}]}, {"key": "aj-028", "text": "3\n\n\n**2. Project development objective** (see Annex 1)\n\nThe objective of the project is to enhance the quality of education and to increase enrollment in\nprimary schools.\n\n\n**3. Key performance indicators:** (see Annex 1)\n\nThe key performance indicator is an increased number of students enrolled in grades 1-9, especially\namong girls.\n\n\nB. **STRATEGIC CONTEXT**\n\n\n**1. Sector-related Country Assistance Strategy (CAS) goal supported by the project** (see Annex 1)\n\n\n**Document number:** P 7403 DJI **Date of latest CAS discussion:** (scheduled for) 12/19/00\n\nThe CAS has been prepared in the context of the country's economic difficulties and deepening\npoverty. Despite Djibouti's relatively high nominal per capita income (US$790 versus an average of\nUS$510 for Sub-Saharan Africa, and US$100 for Ethiopia), Djibouti has one of the poorest social\nindicators in the world (poverty, illiteracy, maternal and infant mortality, and morbidity), according to\nthe UNDP Human Development Index, ranking 157th among 174 countries.\n\n\nThe Republic of Djibouti has very few natural resources and the economy is mainly dependent on the\nport, external financial assistance, the French military and associated services. However, with the\n\ndecreased amount of external assistance, and deepening structural problems, the country has suffered\neconomic stagnation over the past decade and a half. As a result, per capita Gross Domestic Product\n(GDP) declined by 50% in real terms since 1985. The switch of Ethiopia's transit traffic from Assab in\nEritrea to Djibouti in mid-1998 and the consequent four-fold increase in port traffic has opened new,\nas yet not fully exploited, opportunities for investment and growth. Djibouti's open economic policies\nand relative stability, characterized by a liberal trade policy and exchange system, which operates free\n\nof capital or", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:013412:6:0:0", "start": 959, "end": 987, "surface": "UNDP Human Development Index", "probe_tag": "keep", "probe_score": 0.9623, "luna_label": 1}]}, {"key": "aj-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_aj", "spans": [{"key": "fcv_pads_east_africa:018928:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1}, {"key": "fcv_pads_east_africa:018928:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1}]}, {"key": "aj-030", "text": "18. **Unfortunately, lack of adequate budget remains an issue.** Most of the spending at woreda level\ngoes to non‐discretionary spending (salary of teachers, HEWs, DAs, woreda water desk staff and rural road\ndesks). At the same time, the continued investment on woreda basic service infrastructure (construction\nof additional schools/school blocks, health centers/health center blocks, farmer training centers, etc.)\nthrough Government‐financed Sustainable Development Goals Fund require additional budget for\nrecurrent allocations to make them operational. Yet, despite this, the 2020 MoF Cost of Basic Services\nStudy shows that, despite the average historic 60 percent allocation, woredas still exhibit gaps in meeting\ntheir basic requirements and service delivery standards set by the Government.\n\n19. **Additional analytics also point to several binding constraints in how services are planned,**\n**budgeted and delivered.** In coordination with Government and partners, the World Bank has led several\nkey analytical analyses of binding constraints to service delivery in the decentralized system. These\ninclude the Rapid Woreda Institutional Assessment (2020), Review of Local Level Planning and Budgeting\nProcesses in Ethiopia (2020), and Analysis of Human Development Delivery Systems in Lowlands of\nEthiopia (2019). Together, they highlight several factors: lack of multi‐sectoral coordination and\nintegrated planning; lack of human capacity and lack of incentives and accountability systems to ensure\nresults; poor planning, budgeting and resource allocation processes including mismatch between planning\nand budgeting; weak data collection and monitoring and evaluation systems; and shortcomings in the\navailability of supplies (books, drugs, materials, clean water, reliable electricity, etc.) especially in remote\nand pastoral areas.\n\n20. **The ESPES PDO remains very relevant and the case for continued support to Federal block grants**\n**is very strong.** Given the above‐mentioned gaps and challenges to service delivery and in the face", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:020771:4:0:1", "start": 1120, "end": 1157, "surface": "Rapid Woreda Institutional Assessment", "probe_tag": "confusion", "probe_score": 0.5421, "luna_label": 1}]}, {"key": "aj-031", "text": " of a household\nis 6 members. CDD program reviews indicated that each household has a programming influence of at least 5 other\nhouseholds. Using this formula, it can be projected that the total number of individuals engaged in CDD projects, directly\nand indirectly, are 9,762,000. This represents at least 30% of the total population of the country.\n17 Refer annex 3 for details. The PAD included a cost benefit analysis of IFMIS roll out to 2 LGs and 2 MDAs undertaken\nunder EFMP II, but the author did not have access to the background data for this analysis. The analysis had an average\nNPV of 1.7b UGX A (approximately US$646,000) and an average ERR of 31%.\n\n\n12", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:013754:21:2:0", "start": 528, "end": 543, "surface": "background data", "probe_tag": "confusion", "probe_score": 0.2044, "luna_label": 1}]}, {"key": "aj-032", "text": "Public Disclosure Copy\n\n\n**The World Bank** Implementation Status & Results Report\nJudicial Performance Improvement (P105269)\n\n\nProgress on PDO 4 (distance to new courts) is not linear (as predicted in the results framework) throughout the life of the project as the bulk of\nthe new courts are larger in size and thus take longer to construct and therefore only open later on in the project period - when progress on this\nindicator will show.\nRe PDO 5 (user satisfaction), the next court user survey is planned for February 2017, which will allow an update of the user satisfaction data.\n\n\n**Intermediate Results Indicators**\n\n\nPHINDIRITBL\n\n\n\n\n\n\n\nPHINDIRITBL\n\n\n\n\n\n\n\nPHINDIRITBL\n\n\n\n\n\n\n\nPHINDIRITBL\n\n\n\n\n\n1/3/2017 Page 4 of 7\n\nPublic Disclosure Copy", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "sample:fcv_pads_east_africa:009061:3:0:0", "start": 482, "end": 499, "surface": "court user survey", "probe_tag": "confusion", "probe_score": 0.3312, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:009061:3:0:1", "start": 564, "end": 586, "surface": "user satisfaction data", "probe_tag": "confusion", "probe_score": 0.3341, "luna_label": 0}]}, {"key": "aj-033", "text": " and\ncost data collection forms that we have developed and refined in several similar ongoing\nstudies. Using this approach, and taking the perspective of a third-party payer, all relevant\n(non-sunk) labor, materials and supplies, contracted services, and opportunity costs\nrequired to deliver the interventions were captured by key activities. This allowed for\nidentifying the overall costs and which activities drive these costs. This specifically\ntargeted patients with NCDs to determine their cost of care as well as their quality of life.\n\n\n**<u>II. Key Factors Affecting Implementation</u>**\n\n\n**Project Cost**\n\n\n\nPage 4 of 42", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:007462:40:1:0", "start": 5, "end": 14, "surface": "cost data", "probe_tag": "confusion", "probe_score": 0.7026, "luna_label": 0}]}, {"key": "aj-034", "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_aj", "spans": [{"key": "sample:fcv_pads_east_africa:017254:24:0:0", "start": 1670, "end": 1703, "surface": "data on social development issues", "probe_tag": "confusion", "probe_score": 0.333, "luna_label": 0}]}, {"key": "aj-035", "text": " First, the Program’s MDG\ninvestment menu includes infrastructure and services which aim to reduce safety risks for both male and female\ne.g., through financing of streetlights, pedestrian walkways, sanitation facilities in public spaces (e.g., urban parks\nand markets). Second, the Program will develop and help operationalize an urban gender mainstreaming strategy\nwith annexes of clear job descriptions for the GCP and community development officer in gender mainstreaming\nactivities at sub-national levels; additionally, the Program’s I G investment/expenditure menu includes gender\nmainstreaming activities including trainings, especially for the newly hired/relocated staff at 9 sub-national,\nhelping to operationalize the gender mainstreaming strategy. Third, the Program’s result framework will collect\ndisaggregated female beneficiaries’ numbers using the government’s regular monitoring system. As the quarterly\nmonitoring report is currently not collecting gender disaggregated data, the Program will update this template\nto collect gender-disaggregated data.\n\n85. To measure the progress of these actions, the Program’s results framework measures the number of\nfemales who benefited from streetlights, pedestrian walkways, sanitation facilities in urban parks and markets.\nThe development and operationalization of the urban gender mainstreaming strategy will be included in the\nProgram Action Plan (PAP) and respective progress will be monitored during the Program implementation.\nGender disaggregated data will be collected for the indicators in the results framework, where applicable. The\nquarterly monitoring report template will be updated to collect gender disaggregated data for MDG and ISG\nbeneficiaries and annexed in the Program Operational Manual, and the Program will regularly report on this via\nan indicator in its results framework that measures female beneficiaries.\n\n86. **_Climate and Disaster Risk Management –_** Climate and disaster risk screening tool was used to conduct\nhigh-level screening to help consider short- and long-term climate and disaster risks in Program design. Based on\nthe screening which resulted in a moderate", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:009685:49:1:0", "start": 968, "end": 993, "surface": "gender disaggregated data", "probe_tag": "confusion", "probe_score": 0.2255, "luna_label": 0}, {"key": "fcv_pads_east_africa:009685:49:1:1", "start": 1494, "end": 1519, "surface": "Gender disaggregated data", "probe_tag": "confusion", "probe_score": 0.06, "luna_label": 0}]}, {"key": "aj-036", "text": "4. <u>Main statistics agencies have developed and upgraded and put into operation their core</u>\n<u>statistical programs and disseminated the results.</u>\n\n\n\n\n\n\n\n\n\n\n\n\n\n|4.1. Key surveys<br>(e.g., Kenya<br>integrated<br>household<br>budget survey,<br>survey of<br>industrial<br>production,<br>agriculture) and<br>key<br>administrative<br>data from the<br>ministries of<br>health and<br>education are<br>produced and<br>demonstrated<br>according to an<br>agreed schedule.<br>4.2. Suitably-<br>anonym zed<br>public-use micro<br>data household<br>survey files<br>produced and<br>disseminated<br>within 12<br>months of<br>completing<br>household<br>survey data<br>collection<br>activities.|DHS Field work<br>done.<br>NSS working<br>groups<br>established and<br>work plans<br>drafted.|Integrated services<br>survey and<br>NASSEP.<br>NSS working<br>groups to meet<br>regularly.<br>Work plans are<br>implemented with<br>clear results and<br>deliveries.|Not applicable|KIHBS II not<br>completed.<br>Fieldwork of<br>NASSEP V in<br>second of three<br>phases.<br>Work plans are<br>results’ focused<br>with clear outputs<br>in terms of data<br>production.<br>Public-use of<br>", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:020205:12:0:1", "start": 960, "end": 968, "surface": "KIHBS II", "probe_tag": "confusion", "probe_score": 0.7387, "luna_label": 0}]}, {"key": "aj-037", "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": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:013412:40:0:0", "start": 1148, "end": 1181, "surface": "Random surveys of school children", "probe_tag": "confusion", "probe_score": 0.3714, "luna_label": 0}]}, {"key": "aj-038", "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_aj", "spans": [{"key": "sample:fcv_pads_east_africa:014881:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "confusion", "probe_score": 0.5672, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:014881:62:2:1", "start": 758, "end": 796, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.9023, "luna_label": 1}]}, {"key": "aj-039", "text": ", credit have accessed finance and\nstarted running micro-businesses. Livelihood’s beneficiaries have reported saving as a result of their\nparticipation and learning from the livelihood support; and (vii) In EFY 2016, 327,707 hectares of land was\ntreated through area enclosure, rangeland management, soil and water conservations, and forage and\nforestry activities.\n\n\n7. While these achievements are noted, the following issues were observed require GoE’s attention: (i) high\nlevel oversight is critical to implement the increased EFY 2017 budget and targets; (ii) the need for GoE’s\nofficials’ commitment to ensure timely utilization of the increased livelihood budget while ensuring quality\nof implementation; (iii) delays in food procurement; (iv) achievement of Performance-Based Conditions\n(PBCs); and (v) timely initiation of the next phase design.\n\n\n<mark>8.</mark> <mark>The PSNP’s rating, based on World Bank implementation support ratings during this period are as follows:</mark>\n\n\n\n\n\n\n\n|Project Ratings|Previous|Current|\n|---|---|---|\n|Project Development Objective (PDO)|MS|MS|\n|Implementation Progress|MS|MS|\n|Component 1: Adaptive Productive Safety Net|MS|MS|\n|Component 2:<br>Improve Shock Responsiveness of the Rural Safety Net|S|MS|\n|Component 3: Systems, Capacity Development, and<br>Program Management Support|MS|MS|\n|Financial Management|MS|MS|\n|Project Management|MS|MS|\n|Counterpart Financing|MS|MS|\n|Procurement|MU|MS|\n|Environmental and Social|MS|MS|\n|Monitoring and Evaluation|MS|MS|\n\n\n**III. PRIORITY STRATEGIC ISSUES**\n\n\n**Strategic issue 1: High level oversight is critical", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:001304:1:1:0", "start": 907, "end": 948, "surface": "World Bank implementation support ratings", "probe_tag": "confusion", "probe_score": 0.5404, "luna_label": 1}]}, {"key": "aj-040", "text": "Bl.f Project Management Reports In order to prepare quarterly BEPE 31 December 2001\n(PMRs) PMRs, one must be able to print a\nbudget situation on an automatic\nand punctual basis, to conduct\nanalyses on variances, establish\nunit costs for different program\nindicators, and plan commitments\nand expenditures.\n\n\n\nThe BEPE will prepare the first\ndraft PMR for IDA review.\nFollowing comments/approval, the\nfinal report will be prepared and\nused by the BEPE on a reporting\nbasis only.\n**_B.2._** _Procedures manualfor_\n_financial management_\n\nB2.a General Operations Manual Organization guide which would BEPE 30 June 2001\ndefine the roles and responsibilities\nof the BEPE in order to facilitate\ncommunication and exchange of\nproject inforrnation.\nB2.b Formalizing procedures by Description of each operation: BEPE 30 June 2001\nan Organizational Procedures - supporting documents for\nManual. inputs/outputs\n\n                         - information flow\n\n                         - roles of each agency\n\n                         - data needed\n\n                         - control mechanisms\nB2.c Formal presentation of Project indicators (following data BEPE 30 June 2001\nmanagement reports. consolidation): internal, external\naudit, IDA.\n\n**_R3. Audit_**\n\n\nB3.a Appointment of an auditor. Selection of an auditor whose BEPE 31 March 2001\ncontract would be renewable based\non satisfactory performance.\n\n**C. PROCUREMENT**\n\n\n**_C.l._** _BEPEFunction_\nCl .a No procedures manual exist. Prepare a detailed procedures Technical 30 June 2001\nmanual which would outline the unit/\nfunction of the organization, the BEPE\nvarious activities being\n\nundertaken, the steps for each\nactivity and the respective\n_ responsibilities.\nC .b Procurement function lacks a Recruit a qualified and seasoned MEN/ 30 June 2001\nsenior officer. procurement officer, and train BEPE\nexisting junior procurement staff in", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:019800:57:0:0", "start": 1139, "end": 1180, "surface": "data BEPE 30 June 2001\nmanagement reports", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "aj-041", "text": "2 million additional beneficiaries from MDTF. This is 83% of the original<br>target values.|Assuming 3 million beneficiaries from IDA (2 million rural and 1 million urban)<br>and 3.2 million additional beneficiaries from MDTF. This is 83% of the original<br>target values.|Assuming 3 million beneficiaries from IDA (2 million rural and 1 million urban)<br>and 3.2 million additional beneficiaries from MDTF. This is 83% of the original<br>target values.|\n|**Indicator 8 :**|Female beneficiaries|Female beneficiaries|Female beneficiaries|Female beneficiaries|\n|Value<br>quantitative or<br>qualitative)|0|50||50|\n|Date achieved|04/15/2004|10/10/2013||08/31/2013|\n|Comments<br>(incl. %<br>achievement)|Based on national statistics|Based on national statistics|Based on national statistics|Based on national statistics|\n\n\n**(b) Intermediate Outcome Indicator(s)**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Indicator|Baseline Value|Original Target<br>Values (from<br>approval<br>documents)|Formally<br>Revised<br>Target Values|Actual Value<br>Achieved at<br>Completion or<br>Target Years|\n|---|---|---|---|---|\n|**Indicator 1 :**|Number of water utilities that the project is supporting|Number of water utilities that the project is supporting|Number of water utilities that the project is supporting|Number of water utilities that the project is supporting|\n|Value<br>(quantitative<br>or Qualitative)|0|50|86|124|", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:015445:7:2:0", "start": 708, "end": 727, "surface": "national statistics", "probe_tag": "confusion", "probe_score": 0.6214, "luna_label": 1}, {"key": "fcv_pads_east_africa:015445:7:2:3", "start": 708, "end": 727, "surface": "national statistics", "probe_tag": "confusion", "probe_score": 0.3174, "luna_label": 1}]}, {"key": "aj-042", "text": "**The World Bank** Implementation Status & Results Report\nHealth Sustainable Development Goals Program-for-Results (P123531)\n\n\n**Comments** MOH & UN contract register was prepared and shared with to the Bank along with the quarter report.\n\n\n|Action Description|Undertake assessment study and develop coding and categorization system of procurable items|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Source**|**DLI#**|**Responsibility**|**Timing**|**Timing Value**|**Status**|\n|Fiduciary Systems||Client|Recurrent|Yearly|Completed|\n|**Completion Measurement**|Developed and implemented the coding and categorization system of procurable items|Developed and implemented the coding and categorization system of procurable items|Developed and implemented the coding and categorization system of procurable items|Developed and implemented the coding and categorization system of procurable items|Developed and implemented the coding and categorization system of procurable items|\n|**Comments**|Action met.|Action met.|Action met.|Action met.|Action met.|\n\n\n\n\n\n\n\n\n\n\n\n\n|Action Description|Gender based violence strategy for the health sector is prepared and implemented and analysis of gender<br>disaggregated HMIS data is conducted|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Source**|**DLI#**|**Responsibility**|**Timing**|**Timing Value**|", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:008780:4:0:0", "start": 140, "end": 166, "surface": "MOH & UN contract register", "probe_tag": "confusion", "probe_score": 0.1801, "luna_label": 0}]}, {"key": "aj-043", "text": "OCHA Office of Commission for Humanitarian <sup>Assistance</sup>\nPAMC Project Approval and Monitoring Committee\nPETS Public Expenditure Tracking Survey\nPOM Project Operational Manual\nPPA Participatory Poverty Assessment\nQER Quality Enhancement Review\nRUF Revolutionary United Front\nSAPA Social Action and Poverty Alleviation <sup>Program</sup>\nSHARP Sierra Leone HIV/AIDS Response Project\nSLRA Sierra Leone Roads Authority\nSOCAT Social Capital Assessment Tool\nSPP Strategic Planning and Action Process\nTEP Training and Employment Program\nTSS Transitional Support Strategy\nUNAMSIL United Nations Mission for Sierra <sup>Leone</sup>\nUNHCR United Nations High Commission for <sup>Refugees</sup>\nUNICEF United Nations Children's Fund\nUNOPS United National Operations Support\n\n\nVice President: Mr. Callisto Madavo\nCountry Director: Mr. Mats Karlsson\nSector Manager: Mr. Alexandre Abrantes\nTask Team Leader/Task Manager: Ms. Eileen Murray", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:013907:2:0:0", "start": 112, "end": 151, "surface": "PETS Public Expenditure Tracking Survey", "probe_tag": "confusion", "probe_score": 0.0633, "luna_label": 0}]}, {"key": "aj-044", "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_aj", "spans": [{"key": "sample:fcv_pads_east_africa:009595:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "confusion", "probe_score": 0.5672, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:009595:62:2:1", "start": 758, "end": 796, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.9023, "luna_label": 1}]}, {"key": "aj-045", "text": "**SPECIAL** **ACCOUNT** **STATEMENT**\n\nFor period ending 30th **JUNE,** **2016**\nAccount No. **6968520015**\nDepository Bank CBA **BANK-NBI**\nAddress MARA/RAGATI RDS,NAIROBI.\nRelated Loan CBA-NUTRIP-D(KURA)\nCredit Agreement NUTRIP-PRJ-D\nCurrency **USD**\n\nAccount Actiit\n\n\nBeginning balance of 13st July, **2015**\nas per **C.** B. K. Ledger Account\n```\n                               --------------- 21,648.96\n```\n\n**Add:**\n\n\nTotal Amount deposited **by** World Bank\n\nTotal Interest earnings if deposited in account\n\nTotal amount refunded to cover ineligible\nexpenditure\n**0**\n\n\nDeduct:\n\n\nTotal amount withdrawn\n**0.00**\n\n\nTotal service charges if not included above in\namount withdrawn\n5.47\n\nEnding balance on 30th June,2016\n21,643.49\n\n\n**AUTHORISED** REPRESENTATIVE **SIGNATURE:**\n**CENTRAL** BANK OF KENYA\n\n\n**AUTHORISED** REPRESENTATIVE\n\nEXTERNAL **RESOURCES** **SIGNATURE:**\nDEPARTMENT-TREASURY\n\n**DATE**\n\n\nNOTE:The ending balance as per Central Bank of Kenya Ledger Account and the off-shore\nSpecial Account as at 30th June,2016 have been reconciled and a copy of the supporting\nReconciliation Statement is attached.", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:008258:36:0:1", "start": 941, "end": 977, "surface": "Central Bank of Kenya Ledger Account", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "aj-046", "text": " varies by institution:\nKIRDI provided direct, innovation-proximate services (training, incubation, access to Common Manufacturing Facilities\n(CMFs) and testing) that led to market-ready products and Kenya Bureau of Standards (KEBS) certifications, while Kenya\nIndustrial Estates (KIE) delivered upstream entrepreneurship training and industrial mapping/database services—valuable\nfor capability and coordination but indirect for firm-specific innovations. <sup>17</sup>\n\n21. **Despite variation in attribution strength and data quality across tracks, the evidence base is sufficient to confirm**\n**achievement of PDO 1.** The evidence underpinning PDO 1 varies in depth across delivery streams but taken together it\nprovides a credible basis for confirming achievement of the target. The KIEP 250+ subprogram alone contributed 103\nattributable innovations, surpassing the revised PDO 1 target (60) independently. It is also likely that actual innovations\nexceeded those recorded: the PIU adopted a deliberately conservative approach, counting only cases it could comfortably\nvalidate and excluding outputs with any risk of double counting, thereby safeguarding the robustness of the Results\nFramework. Across all streams, triangulated evidence supports a credible causal link between KIEP support and the\ninnovations recorded. While parallel COVID-recovery credit lines may have contributed advisory or liquidity support—\nexplicitly acknowledged in 2023 planning documents as potential confounders—the weight of evidence points to KIEP\nfinancing and TA as the decisive factor. <sup>18</sup> Beneficiary consultations consistently indicated that, without KIEP grants and\nadvisory inputs, most innovations would have been delayed, scaled down, or not undertaken at all. Comparative data\nreinforces this inference: among the 178 firms that underwent diagnostics, those that did not sign grant agreements rarely\ngenerated innovations comparable to financed peers despite attempting partial upgrades. Timing evidence points in the\nsame direction: prior to the May–June 2024 restart, the corporate results system recorded zero innovations, whereas once", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:000637:14:1:0", "start": 1769, "end": 1785, "surface": "Comparative data", "probe_tag": "confusion", "probe_score": 0.2506, "luna_label": 1}]}, {"key": "aj-047", "text": "_\nIsiolo 61,204 _4.6%_ 57,313 _5.4%_\nWajir 89,403 _6.7%_ 69,451 _6.5%_\nHeadquarters - other 84,784 _6.4%_ 77,239 _7.2%_\n\n\n**Transaction expenditure reviewed** **431,537** **_32.5%_** **396,842** **_37.1%_**\n\n\nPayroll 223,577 _16.8%_ 176,769 _16.5%_\n**Analytical expenditure reviewed** **223,577** **_16.8%_** **176,769** **_16.5%_**\n\n\n**Total expenditure reviewed** **655,114** **_49.3%_** **573,611** **_53.7%_**\n\n\nOther districts 673,553 _50.7%_ 494,951 _46.3%_\n\n\n**Total expenditure per FMRs** **1,328,667** _100.0%_ **1,068,562** _100.0%_\n\n\n49. An analysis of the FMRs provided to the Bank indicated that for FY06/07 and FY07/08\nthe expenditure coverage of the audit was 53.7% and 49.3% respectively.  The above\nTable 1 sets out the reported expenditure for each district sampled, and for headquarters\nwith the payroll expense shown separately.  All expenditure from the districts sampled\nand the headquarters (with the exception of payroll) were analyzed on a transactional\nbasis. The payroll was a significant expense, representing 16-17% of total costs of the\nproject for the relevant periods.  Due to the nature of payroll", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:009492:18:1:0", "start": 490, "end": 494, "surface": "FMRs", "probe_tag": "confusion", "probe_score": 0.4696, "luna_label": 0}, {"key": "fcv_pads_east_africa:009492:18:1:1", "start": 490, "end": 494, "surface": "FMRs", "probe_tag": "confusion", "probe_score": 0.8087, "luna_label": 0}]}, {"key": "aj-048", "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_aj", "spans": [{"key": "fcv_pads_east_africa:015217:16:0:1", "start": 1487, "end": 1500, "surface": "random survey", "probe_tag": "drop", "probe_score": 0.0148, "luna_label": 0}]}, {"key": "aj-049", "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_aj", "spans": [{"key": "fcv_pads_east_africa:010355:24:0:0", "start": 1670, "end": 1703, "surface": "data on social development issues", "probe_tag": "drop", "probe_score": 0.0226, "luna_label": 0}]}, {"key": "aj-050", "text": "13\n\n\n**_Monitoring and Evaluation_**\n\n\nMonitoring will be done according to the development indicators given in the attachment to Annex 1.\nThe project will strengthen the capacity of CNOSEGE, and the Planning Unit of the Ministry so that\nmonitoring reports on the implementation of the reform can include key progress and impact\nindicators. Currently the Planning unit generates statistical data on all aspects of the education sector,\nhowever this can be further strengthened to monitor progress on key reform objectives such as access,\nequity and quality. In addition, during the donors round-table UNESCO offered support to develop an\nEducation Management Information System (EMIS). If this is not in place by the end of Phase I of the\nAPL, this would be a priority item for Phase II.\n\n\nEvaluation of the impact of the reforms will be done by CNOSEGE by recruiting experts in this field\n\nand an initial evaluation will be done at the end of Phase I. Particular areas of impact assessment will\nbe student performance and success in reaching out to disadvantaged groups. Normally, student\nperformance would be measured by overall test results but as the pool of students widens to include\nstudents from less advantaged socioeconomic groups, there will be a downward pressure on test\nscores. The Planning Unit of the Ministry will be strengthened to monitor progress in reaching out to\ndisadvantaged groups and test scores of students by socioeconomic background. Staff will carry out a\nrandom survey (5 to 10% sample) of students by socioeconomic background in 2001 to establish a\nbaseline. To keep the survey simple, the socioeconomic background questions will be limited to easily\nidentified categories such as day-laborers, civil servants, shopkeepers etc. The survey will be repeated\nin 2005 and 2110.\n\n\n**D.** PROJECT RATIONALE\n\n\n**1. Project alternatives considered and reasons for rejection**\n\nOriginally, the project was designed as a Sector Investment Loan, however, given the Government's\ncommitment to the education sector, and the", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:020949:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "drop", "probe_score": 0.0402, "luna_label": 0}, {"key": "fcv_pads_east_africa:020949:16:0:1", "start": 1487, "end": 1500, "surface": "random survey", "probe_tag": "drop", "probe_score": 0.0148, "luna_label": 0}]}, {"key": "aj-051", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:008245:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0}]}, {"key": "aj-052", "text": "State Department for Parliamentary Affairs�\nAnnual Report and Financial Statements for the year ended 30th June 2024�\n\n\nSignificant Accounting Policies (Continued)�\n\n\nb) Recognition of payments�\n\n\nThe State Department for ParliamentaryAfairs recognisesall payments when the event ocurs,�\nand the related cash has been paid out by the SDPA.�\n\n\ni)� Compensation of Employees�\n\n\nSalaries and wages, alowances, statutory contribution for employes are recognized in�\nthe period when the compensation is paid.�\n\n\ni1)� Use of Goods and Services�\n\n\nGoods and services are recognized as payments inthe period when the godssrvices�\n1� are paid for. Such expenses, if not paid during the period where goods/services are�\n\nconsumed, shall be disclosed as pending bills.�\n\n\ni1)�\nInterest on Borrowing�\n\n\nThere were no browing costs that include interest a payment in the financial period.�\n\n\niv)� Principal on borrowing�\n\n\n1� There was no repayment of principal amount of brrowing during the recognized.�\n\n\nv)� Acquisition of Fixed Assets�\n\n\nThe payment on acquisition of property plant and equipment items is not capitalized.�\nThe cost of acquiition and proceeds from disposal of these itemsare treated as�\n\nPayments and receipis items respectively. Where an asset is acquired in a non-exchange�\ntransaction for nil or nominal consideration and the fair value of the asset can be�\nreliably established, a contra transaction is recorded as receipt and as a payment. A�\nfixed asset register is maintained and a summary provided\"for \"purposes of�\n\n\nconsolidation.�\n\n\n10�", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:003002:46:0:0", "start": 1456, "end": 1476, "surface": "fixed asset register", "probe_tag": "drop", "probe_score": 0.0176, "luna_label": 0}]}, {"key": "aj-053", "text": "The LVEMP originally included trials to assess the environmental and economic usefulness\nof chemical control of the hyacinth, but these trials were abandoned as being too socially,\neconomically and environmentally risky.\n\n\nWhy was it necessary to use the hyacinth shredding tender as the core of a detailed\nevaluation of the environmental impact _of the shredding/chopping method of control_, instead of\ndoing a detailed EA as part of project preparation? In the absence of sufficient baseline data, and\ndata describing analogous activities in other similar environments, there is virtually no chance of\npreparing a meaningful and useful EA. There are not sufficient data on the water quality or\nphysical limnology of the Lake to be able to accurately describe either the current situation, or\nany large-scale changes in the Lake likely to result from shredding, or any other intervention in\nthe Lake or its catchment. The largest component of the LVEMP is designed to collect sufficient\nwater quality and limnology data from the Lake to create a reasonable scientific baseline, which\nwould enable environmental assessment of development and management actions in the future.\n\n\n_<u>The Scope and Design of the Pilot (as basis of an ongoing EA)</u>_\n\n\nThe LVEMP is designed to collected baseline data, identify and prioritize problems and to\nexperiment with possible solutions to these problems through a series of experimental pilots. The\ntender to shred water hyacinth was prepared in keeping with the experimental approach. It is not\nlarge enough to cause significant impact on the ecology of Lake Victoria, but of sufficient size\n(shredding up to 1500 ha of floating water hyacinth mats) to allow water quality monitoring to\npick up changes in surrounding Biological Oxygen Demand (BOD), phytoplankton\nabundance/species composition, conductivity etc, that might be indicative of the impact of this\nmethod of control, should it have widespread use in the Lake at some point in the future.\n\n\nMore particularly, the water hyacinth shredding pilot is important because traditional\nharvesting/removal of weed to dry land", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "sample:fcv_pads_east_africa:020447:30:0:0", "start": 485, "end": 498, "surface": "baseline data", "probe_tag": "confusion", "probe_score": 0.2217, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:020447:30:0:1", "start": 988, "end": 1020, "surface": "water quality and limnology data", "probe_tag": "drop", "probe_score": 0.0397, "luna_label": 0}]}, {"key": "aj-054", "text": "**_Leased Assets_** _as specified under paragraph 5.10_ of the Procurement\nRegulations: Not Applicable\n\n\n**Procurement of Second Hand Goods** as specified under paragraph 5.11 of\nthe Procurement Regulations – is allowed for those contracts identified in the\nProcurement Plan tables: Not Applicable\n\n\n**_Domestic preference_** _as specified under paragraph 5.51_ of the Procurement\nRegulations **_(Goods and Works)_** .\n\n\nGoods: is applicable for those contracts identified in the Procurement Plan\ntables;\n\n\nWorks: is applicable for those contracts identified in the Procurement Plan\ntables\n\n\n**Other** **_Relevant Procurement Information._**\n\n\n_None_", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:004205:1:0:0", "start": 258, "end": 281, "surface": "Procurement Plan tables", "probe_tag": "drop", "probe_score": 0.0388, "luna_label": 0}]}, {"key": "aj-055", "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_aj", "spans": [{"key": "fcv_pads_east_africa:010084:24:0:0", "start": 1670, "end": 1703, "surface": "data on social development issues", "probe_tag": "drop", "probe_score": 0.0226, "luna_label": 0}]}, {"key": "aj-056", "text": " and Monitoring and Evaluation**|**Project Management and Monitoring and Evaluation**|\n|**Percentage of project-related grievances addressed (Percentage) **|**Percentage of project-related grievances addressed (Percentage) **|\n|Description|This indicator will be tracked by PIU through data collected through the Project Management Information System (MIS).|\n|Frequency|**Quarterly**|\n|Data source|**MSEA’s and KDC’s registry of project benefeciary firms**|\n|Methodology for Data<br>Collection|**Project progress reports**|\n|Responsibility for Data<br>Collection|** PIU**|\n|**of which women (Percentage) **|**of which women (Percentage) **|\n|Description||\n|Frequency|**Quarterly**|\n|Data source|**MSEA’s and KDC’s registry of project benefeciary firms**|\n|Methodology for Data<br>Collection|**Project progress reports**|\n\n\n\nNov 16, 2023 Page 42 of 60", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "sample:fcv_pads_east_africa:005133:46:2:0", "start": 313, "end": 350, "surface": "Project Management Information System", "probe_tag": "confusion", "probe_score": 0.1482, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:005133:46:2:1", "start": 400, "end": 454, "surface": "MSEA’s and KDC’s registry of project benefeciary firms", "probe_tag": "drop", "probe_score": 0.0376, "luna_label": 0}]}, {"key": "aj-057", "text": "Project** **Manaeem2nt** <sup>**and**</sup> <sup>million)</sup> administrative data appropriate, and clear in\n\n\n\ndefining the and\n**Innovative Activities**\n\n\n\nresponsibilities of all parties;\nNaCSA retains competent\n**(a) Capacity Building**\n\n\n\nstaff;\n\n                                             - Other governnent and donor\n**(b)** **Information and**\n\nsupport mobilized for\n\n\n\nsupport mobilized for\n**Sensitization**\ndecentralization to\ncomplement NaCSA efforts;\n(c) **Monitoring and**\n\n\n\n**Evaluation**\n\n\n\n**(d)** **Technical Assistance**\n\n\n(e) **Operating** Expenses\n\n\n\n-28", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:016286:32:1:0", "start": 64, "end": 83, "surface": "administrative data", "probe_tag": "drop", "probe_score": 0.0277, "luna_label": 0}]}, {"key": "aj-058", "text": ". Excluding conflict affected areas of Tigray and BG<br>– 2022/23|\n|IR Indicator 6.3: Average score of<br>composite index of school inspection<br>standards on teaching practi (Text)|54|Jul/2016|70.30|04-Apr-2025|70|Jun/2023|\n|IR Indicator 6.3: Average score of<br>composite index of school inspection<br>standards on teaching practi (Text)|Comments on achieving targets|Comments on achieving targets|Target achieved.|Target achieved.|Target achieved.|Target achieved.|\n|IR Indicator 6.4: % of actual teaching<br>time relative to scheduled<br>instructional time in P1 schools<br>(Text)|Time-on-task survey to<br>be conducted in Year 3<br>and 4|Jul/2016|94%|04-Apr-2025|To be determined|Jul/2023|\n|IR Indicator 6.4: % of actual teaching<br>time relative to scheduled<br>instructional time in P1 schools<br>(Text)|Comments on achieving targets|Comments on achieving targets|Target achieved.|Target achieved.|Target achieved.|Target achieved.|\n|IR Indicator 6.5: % of students having<br>textbooks (Text)|58|Jul/2016|73%|04-Apr-2025|70|Jul/2022|\n|IR Indicator 6.5: % of students having<br>textbooks (Text)|Comments on achieving targets|Comments on achieving targets|End target was exceeded.|End target was exceeded.|End target was exceeded.|End target was exceeded.|\n|IRI 6.6. Decrease in % of Level 1<br>school nation-wide (Text)|", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:001747:19:1:0", "start": 585, "end": 604, "surface": "Time-on-task survey", "probe_tag": "drop", "probe_score": 0.0322, "luna_label": 0}]}, {"key": "aj-059", "text": ".6\n4.0\n6.3\n\n3.0\n6.1\n\n**14.0**\n5.1\n\n\n\n**2005** **2005-08**\n\n\n5.6 5.9\n19 3.6\n4.4 5.6\n\n\n\n**2005**\n\n\n33.5\n20.9\n9.0\n45.6\n\n76.7\n142\n27.7\n\n\n**2005**\n\n\n5.1\n9.1\n6.7\n7.2\n\n5.0\n7.5\n115\n202\n\n\n\nIevelopment diamond.\n\n\nLife expectancy\n\n       \n\nGN i Gross\n\nper\ncapita enrollment\n##### i I\n\n\nAccess to improvedwatersource\n\n\n. . Uganda\n\nLow-incomegmup\n\n\n**Economlc** ratios'\n\n\nTrade\n\n\ni\nDomestic, /$' Capital\nsavings **id,!'** . formation\n\nI\n\n\nIndebtedness\n\n-\"- -Uganda\n\nLow-income group\n\n\nGrowth of capital and GDP (%)\n\n**20** \n\n**02** **03** **_04_** **05**\n\n\n**---GCF**\n#### -GDP \nGrowth of exports and imports **_(Oh)_**\n\n30 T\n\n\n\n(avenge annualgrowfh)\nAgriculture\nIndustry\n\n\n\n**1985.85** **1985-05**\n\n\n4.0 4.0\n9.3 8.4\n9.8 8.5\n6.8 7.2\n\n\n\nManufacturing\n\n\n\nServices\n\n\n\nHousehold final consumption expenditure 5.4 5.2\n\nGeneral gov't final consumption expenditure 4.8 5.5\nGross capital formation 7.6 5.3\n\nImports of goods and services 3.9 5.5\n\n\n\nNote: 2005 data are preliminaryestimates.\nThis table was produced from the Development Economics LDB database.\n'Thediamonds showfourkeyindicators in thecountry(in bo1d)comparedwith its income-groupaverage.", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:020086:29:2:0", "start": 933, "end": 942, "surface": "2005 data", "probe_tag": "keep", "probe_score": 0.9132, "luna_label": 0}, {"key": "fcv_pads_east_africa:020086:29:2:1", "start": 1002, "end": 1036, "surface": "Development Economics LDB database", "probe_tag": "drop", "probe_score": 0.018, "luna_label": 1}]}, {"key": "aj-060", "text": "**Figure 2: Impact of regulatory heterogeneity on firm-level average costs**\n\n\nHigh services trade costs in part reflect regulatory policies that may discriminate against foreign\n\nproviders. Examples include nationality requirements or banning access to markets, as is the case in\n\nmany countries for segments of the transport, communications or professional services sectors.\n\nResearch has shown that barriers to trade and investment in services are often much higher than for\n\ngoods (Jafari and Tarr, 2017). Although information on services trade policy is limited, new data sets\n\nhave been developed recently that characterize the restrictiveness of services trade and investment\n\npolicies (Borchert, Gootiiz, and Mattoo 2014; WTO, 2019). The World Bank’s Services Trade\n\nRestrictiveness Index (STRI) reveals that barriers to trade in services in the late 2000s were substantial\n\n(Figure 3). <sup>3</sup> More recent data on services trade policies for a smaller set of countries in 2016 (Borchert et\n\nal., 2019; Borchert et al. 2020) confirm that barriers to trade in services remain substantial, with\n\nsignificant heterogeneity across countries and sectors (Figure 4). Noteworthy, however, is that the data\n\ncollected by the OECD, World Bank and WTO suggest there has been a trend towards a more open\n\nservices trade policy stance (Figure 5).\n\n\n[3 See Services Trade Restrictions Database. http://iresearch.worldbank.org/servicetrade/aboutData.htm and](about:blank)\n[OECD. Services Trade Restrictiveness Index. http://www.oecd.org/tad/services-trade/services-trade-](about:blank)\n<u>[restrictiveness-index.htm](about:blank)</u>\n\n\n5", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000407:6:0:3", "start": 1478, "end": 1514, "surface": "Services Trade Restrictiveness Index", "probe_tag": "keep", "probe_score": 0.9277, "luna_label": 1}]}, {"key": "aj-061", "text": "856 segments based on the GIS information provided by the\nexpert (self-collected data using a smartphone were compared with government data and since\nconsistency was found, the former were used as the data input). The Haversine formula was used to\ndetermine the length of each segment connecting two points, which was then utilized to calculate the\ncorresponding gradient of the segment. Each segment on average was about 5-10 meters in length. Each\nsegment was then assigned with a numbered ID from east to west. A Python-automated program randomly\nselected a number of road segments (along the East Road) to be affected by a specified rain event and road\nupgrading combination. Under each rainfall scenario, each road design's gradient threshold would result in\ndifferent damage profiles.\n\nBy using the random sampling selection method and identifying the nearest segment towards the Dala end\nof the road that would be hypothetically affected, the maximum affected population within the road buffer\nzones during road disruption was calculated. This calculation was based on the sum of people within the\nbuffer zone to the east of the west-most affected road segment, minus the sum of people within a\n3km/10km (based on the functionality being considered) distance of the west-most affected road segment.\nThe latter was subtracted from the number of affected people because we assume that people within a\n3km/10km distance of the west-most disrupted road segment can simply walk across and access the\nservices required. The program was run 100 times for each combination, and the affected population was\nestimated based on an average of all 100 runs of the analysis.\n\n\n32", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000375:33:3:1", "start": 124, "end": 139, "surface": "government data", "probe_tag": "keep", "probe_score": 0.9102, "luna_label": 1}]}, {"key": "aj-062", "text": "3.2.1 Macro stabilization variables\n\n\nWe include the log of _government consumption_ as a share of GDP, _lkg_, calculated\nas the log of csh_g from the PWT 10.0. This variable is supposed to capture growthreducing effects through distortionary taxation (Afonso and Furceri 2010) or public\ndebt issuance. The negative association with growth is motivated by the fact that\nwe include the positive effects that government consumption may have on growth\nseparately, for example, through spending on infrastructure. As our model describes\nlong-run growth, it is also important not to conflate the short-term positive stimulus\neffect that increased government consumption can have during economic\ndownturns. <sup>7</sup> [^7: For similar reasons, we also do not include fiscal deficit variables, which are highly cyclical and hence tend\nto smooth out over the five-year averages.]\n\n\n_Inflation_ is measured as the log change of _v_c/q_c_ (household consumption in\ncurrent national prices/household consumption in constant national 2017 prices)\nfrom the national accounts module of the PWT 10.0, which has greater availability\nthan inflation data from the World Bank’s World Development Indicators (WDI). <sup>8</sup> [^8: We add 1 to this variable to avoid negative numbers, which cannot be translated into logs.]\n\n\nThe _real exchange rate_, _lrer_, is calculated as the log of the GDP price level (in PPP)\nover the nominal exchange rate: _pl_gdpo/xr_, both taken from the PWT 10.0. Since\n_xr_ is measured as national currency/US$, an increase in _lrer_ reflects a real\nappreciation, which is expected to have a negative effect on output and growth\nthrough various channels (e.g., Rapetti 2019; Levy-Yeyati, Sturzenegger, and\nGluzmann 2013). <sup>9</sup>\n\n\n3.2.2 Financial variables\n\n\nTo measure countries’ _financial development_, we use the log of domestic credit to\nthe private sector", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "sample:prwp:000876:9:0:0", "start": 151, "end": 159, "surface": "PWT 10.0", "probe_tag": "keep", "probe_score": 0.9579, "luna_label": 1}, {"key": "sample:prwp:000876:9:0:1", "start": 1078, "end": 1086, "surface": "PWT 10.0", "probe_tag": "keep", "probe_score": 0.9981, "luna_label": 1}, {"key": "sample:prwp:000876:9:0:2", "start": 1124, "end": 1138, "surface": "inflation data", "probe_tag": "keep", "probe_score": 0.9634, "luna_label": 1}, {"key": "sample:prwp:000876:9:0:3", "start": 1161, "end": 1189, "surface": "World Development Indicators", "probe_tag": "keep", "probe_score": 0.9877, "luna_label": 0}, {"key": "sample:prwp:000876:9:0:4", "start": 1466, "end": 1474, "surface": "PWT 10.0", "probe_tag": "keep", "probe_score": 0.9981, "luna_label": 1}]}, {"key": "aj-063", "text": "EMDEs.\n\n\nData\n\n\nThe estimation relies on bilateral trade costs from the UNESCAP-World Bank Trade\nCosts Database. Following Novy (2013) and Arvis et al. (2013), bilateral trade costs are\nobtained as geometric averages of flows between countries i and j. They are computed\naccording to the formula below:\n\n_(Xii Xjj)/(Xij Xji)_ <sup>1/2 (</sup> <sup>_σ_</sup> <sup>-1)</sup>,\n\nwhere _Xij_ represents trade flows between countries _i_ and _j_ (goods produced in _i_ and sold\nin _j_ ) and _σ_ refers to the elasticity of substitution. This measure captures international\ntrade costs relative to domestic trade costs. Intuitively, trade costs are higher when\ncountries trade more domestically than they trade with each other, i.e., as the ratio ( _Xii_\n_Xjj_ )/( _Xij Xji_ ) increases. Intra-national (that is, domestic) trade is proxied by the difference\nof gross output and total exports.\n\n\nTrade costs thus computed implicitly account for a wide range of frictions associated\nwith international trade, including transport costs, tariffs, and nontariff measures, and\ncosts associated with differences in languages, currencies and import or export\nprocedures. Trade costs are expressed as ad valorem (tariff) equivalents of the value of\ntraded goods and can be computed as an aggregate referring to all sectors of the\neconomy, but also specifically for the manufacturing and agriculture sectors.\n\n\nEstimation\n\n\nGravity equations are widely used as a workhorse to analyze the determinants of\nbilateral trade flows. Chen and Novy (2012) and Arvis et al. (2013) also employ a\ngravity specification to analyze the determinants of bilateral trade costs in a\ncross-sectional", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001145:28:0:0", "start": 72, "end": 111, "surface": "UNESCAP-World Bank Trade\nCosts Database", "probe_tag": "keep", "probe_score": 0.966, "luna_label": 1}]}, {"key": "aj-064", "text": "Policy Research Working Paper 9294\n\n### **Abstract**\n\nNearly one in three children under age five in the Philippines is stunted, a key marker of undernutrition. This\nrate is high for the country’s level of income. This paper\nprovides the first detailed multivariate analysis of potential drivers of stunting in the Philippines, using data from\nthe 2015 National Nutrition Survey. Potential drivers are\nanalyzed individually and grouped in major categories. The\nanalysis finds that stunting between 24–60 months is principally associated with suboptimal prenatal conditions and\ninadequate food security and diversity. If the results are\ngiven a causal interpretation, they imply that if all Filipino\nnewborns had adequate prenatal conditions, the fraction\n\n\n\nstunted at age 24–60 months would fall by 20 percent.\nSimilarly, providing adequate food security and diversity to\nall Filipino children would reduce stunting by 22 percent.\nOther factors—including access to water, sanitation, and\nenvironmental conditions—have less strong associations\nwith stunting. The results point to a series of policy priorities to reduce stunting: supporting the nutrition and health\nof expectant mothers, ensuring access to contraception to\nreduce adolescent pregnancy, and ensuring that children\nconsume a variety of healthy foods, including protein-dense\nfoods such as milk, meat, and eggs.\n\n\n\nThis paper is a product of the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World\n\nBank to provide open access to its research and make a contribution to development policy discussions around the world.\nPolicy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be\ncontacted at gdemombynes@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 if the presentations are less than fully polished. The papers carry the_\n_", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002440:1:0:0", "start": 348, "end": 378, "surface": "2015 National Nutrition Survey", "probe_tag": "keep", "probe_score": 0.9297, "luna_label": 1}]}, {"key": "aj-065", "text": "100\n\n\n80\n\n\n60\n\n\n40\n\n\n20\n\n\n0\n\n\n\n_(c) QGC method_\n\n\n0 20 40 60 80 100\n\nQGC\n\n\n\nSource: own estimates based on SEDLAC data (CEDLAS and the\nWorld Bank) and the Global Economic Prospectus (GEP). Note: The\nfigure shows poverty nowcasts on the horizontal axis and actual poverty\nrates in vertical axis for all countries for which data is available in 20052014. The 45 degree line shows actual poverty. Countries included are:\nArgentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican\nRepublic, Ecuador, Honduras, Mexico, Panama, Peru, Paraguay, El\nSalvador, and Uruguay. Guatemala and Nicaragua are excluded due to\ndata limitations. A simple interpolation was applied when country data\nwere not available for a given year (see table 1 for a description of the\ndata gap and comparability across years). PE refers to Poverty-growth\nElasticity method; NDG refers to Neutral Distribution Growth method;\nand QGC refers to Quantile Growth Contribution method. The figure\nassumes a pass-through θ=1 for the NDG and QGC methods. All past\ninformation is obtained from periods -1 and -2 when nowcasting poverty\nin moment 0 under de PE and QGC methods. The number of quantiles\nis q=20 under the QGC method.\n\n\n30", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "sample:prwp:007087:31:0:0", "start": 107, "end": 118, "surface": "SEDLAC data", "probe_tag": "keep", "probe_score": 0.9985, "luna_label": 1}, {"key": "sample:prwp:007087:31:0:1", "start": 155, "end": 181, "surface": "Global Economic Prospectus", "probe_tag": "keep", "probe_score": 0.9982, "luna_label": 1}, {"key": "sample:prwp:007087:31:0:2", "start": 674, "end": 686, "surface": "country data", "probe_tag": "keep", "probe_score": 0.9726, "luna_label": 1}]}, {"key": "aj-066", "text": " Nino phenomenon caused severe droughts in 1991/92, 2011/12, and 2016/17\nwhich exacerbated preexisting vulnerabilities in the Somali population. Both conflict and drought have led\nto large‐scale internal displacement (World Bank, 2018a). The recent 2016/17 drought led to the\ndisplacement of approximately one million Somalis, adding to an existing population of internally\ndisplaced persons of 1.1 million (UNHCR, 2018).\n\n\nAs is typical for fragile states, Somalia is highly data‐deprived, leaving policy makers to operate in a\nstatistical vacuum (Beegle et al., 2016). Specifically, years of civil war and ongoing conflict have eroded\nSomalia’s statistical infrastructure and capacity, leading to the lack of key macro‐ and micro‐economic\nindicators, including the poverty rate (Hoogeveen and Nguyen, 2017). The government conducted and\npublished the last full population census in 1975, while Somalia Socioeconomic Survey of 2002 was the\nlast country‐wide household survey (UNFPA, 2014). Most recent existing data sources are local FSNAU\nand FAO food and nutrition surveys, while organizations operating within Somalia implemented a range\nof smaller surveys. In 2014, UNFPA implemented the first nationwide Population Estimation Survey (PESS)\nin preparation for a national census, finding the total population to be 12.3 million, of which 42 percent\nare urban, 23 percent rural, 26 percent nomadic, and 9 percent are internally displaced (UNFPA, 2014).\n\n\nFunded by the World Bank, Somaliland carried out a household budget survey (SLHS) in 2013, which\ngenerated much‐needed indicators, including poverty estimates, but the sample was not representative\nespecially for the rural population and did not cover the nomadic and displaced populations. The World\nBank conducted the first wave of the Somali High Frequency Survey (SHFS) in the spring of 2016,\nrepresentative of the accessible urban, rural, and IDP population in 9 of 18 prewar", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:007638:3:1:2", "start": 1045, "end": 1075, "surface": "FAO food and nutrition surveys", "probe_tag": "confusion", "probe_score": 0.8362, "luna_label": 1}]}, {"key": "aj-067", "text": " empirical\nstrategy. Section III presents the empirical results. Section IV explores how this structural\nchange has affected the rate of poverty reduction. Finally, Section V closes.\n\n**II. Data and methods**\n\n\nOur empirical strategy relies on the estimation of models based on variations of\nthe following basic specification:\n\n\nΔ _T_ _g_ _it_ / _Tit_ = α+ βΔ _T_ _yit_ / _Tit_ + _uit_, (1)\n\nwhere _g_ is the log of the gini coefficient, _y_ is the log of per capita income, Δ _T_ is\nthe _T_ -th difference operator so that Δ _T_ _g_ _it_ = _g_ _it_ - _g_ _it_ - _T_, and _Tit_ is the length of the spell\nfor country _i_ and time _t_, which as indicated by the subscript _it_, will vary with the spell. _u_\nis an error term.\n\n\nPanel (a) of figure 1 plots the scatter of changes in the gini coefficient (in logs)\nagainst growth for the 1970-2000 period. The inequality data are from the DKD and the\ngrowth data is derived from the PWT6.1. Inspection of this figure suggests no apparent\nrelationship between growth and changes in inequality. This is confirmed by column (1)\nof table 1 which reports the results of estimating equation (1) for the 1970-2000 period.\n\n\n1 There is different strand on the literature that has focused on the impact of inequality on growth. Among\nothers, see Forbes (2000) and Banerjee and Duflo (2003).\n2 See among others Deininger and Squire (1996), Chen and Ravallion (1997) and Dollar and Kraay (2002).\n\n\n2", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:003144:1:1:2", "start": 898, "end": 909, "surface": "growth data", "probe_tag": "confusion", "probe_score": 0.7267, "luna_label": 1}]}, {"key": "aj-068", "text": "**APPENDIX**\n\n\n**Appendix 1: Constructing the Exporter Dynamics Database Using Customs Data at the**\n\n**Exporter-Level**\n\n\nThe measures included in the Database are computed using customs data from 45 countries at the\nexporter-product-destination-year level covering the universe of export transactions. <sup>35</sup> [^35: The providers of the raw datasets for each country were mostly governmental agencies, mainly customs offices. Appendix 1 in\nCebeci et al. (2012) provides a complete list of the countries included in the Database, the periods for which data is available, and\nthe data sources.] For 11\ncountries (Brazil, the Arab Republic of Egypt, Estonia, Lao PDR, New Zealand, Norway, Portugal,\nSpain, Sweden, and Turkey), we do not have access to the raw data and the statistics were\ncalculated for us by researchers or institutions with access to the raw data. Pooling across the raw\ndatasets for the remaining 34 countries, we obtain a panel with 15 million unique observations at\nthe country-firm-product-destination-year level which is the raw dataset used to construct the\nDatabase. Some of the variables in this cross-country raw dataset were subjected to a series of\ncleaning procedures or had peculiarities that we briefly describe below: firm codes, country of\ndestination, product and export values. <sup>36</sup> [^36: Cebeci et al. (2012) describe the cleaning procedures in further detail. Note that the cross-country raw dataset at country-firmproduct-destination-year level includes also quantities exported.]\n\nRegarding firm codes, exporters are uniquely identified by their actual names, their tax\nidentification number or artificial unique codes randomly created by our data providers. In the case\nof Albania, Burkina Faso, Cambodia, Cameroon, Mexico, and Uganda their firm coding systems\nunderwent changes in 2007 and in the case of Yemen in 2008. Hence, it is not possible to calculate\nexporter dynamics measures in the years when those changes occur.\n\nRegarding the country of destination variable, two cleaning operations are", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:006500:45:0:0", "start": 46, "end": 72, "surface": "Exporter Dynamics Database", "probe_tag": "keep", "probe_score": 0.9338, "luna_label": 0}, {"key": "prwp:006500:45:0:3", "start": 891, "end": 903, "surface": "raw\ndatasets", "probe_tag": "confusion", "probe_score": 0.1355, "luna_label": 1}]}, {"key": "aj-069", "text": " approaches to collect property price data includes Kim’s (2007) study on\nVietnam. The author manually collated over 5,000 observations on property prices and property attributes\ndrawing on classified advertisements in Vietnam’s most prominent newspaper. Applying a hedonic price\nmodel, Kim assesses the price differences between Hanoi and Ho Chi Minh City to investigate the impact\nof social norms on property prices. Over time and with the increased penetration of property listing\nwebsites, private property price collection efforts have transitioned to online listings where data collection\ncan be automated. Anenberg & Laufer (2017), for instance, use listing information to construct a new house\nprice index to monitor house price developments in the US. Using property listings, the authors construct a\n\n\n7", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000949:8:2:1", "start": 767, "end": 784, "surface": "property listings", "probe_tag": "confusion", "probe_score": 0.6996, "luna_label": 1}]}, {"key": "aj-070", "text": "\nhas become the primary focus of attention. In the empirics, we allow for, and attempt\n\n\nto differentiate between, these two forms of profit shifting and the tax effects that operate\n\n\nthrough them.\n\n\nThe paper is structured as follows. Section 2 sets out the broad framework that articulates\n\n\npotential channels for tax spillovers on real investment and guides the empirical work. Section\n\n\n3 elaborates on the fundamental differences between FAI and FDI data and Section 4 then\n\n\ndevelops an estimation strategy. Section 5 sets out baseline results, explores their robustness\n\n\nunderlying investment which just yields the investor their required post-tax return (conventionally expressed\nas a proportion of the former).\n\n\n10As, notably, Mintz (2018) and Bazel and Mintz (2020).\n\n\n5", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000791:6:1:0", "start": 453, "end": 461, "surface": "FDI data", "probe_tag": "confusion", "probe_score": 0.4693, "luna_label": 0}]}, {"key": "aj-071", "text": " IDP data focus only on internally displaced populations to which it extends protection or assistance. IDMC\ncoverage of IDP data is more expansive and in 2015 included additional data on: (a) 26 countries accounting for 4.5\nmillion IDPs including some significant IDP hosting countries (Turkey, India, Ethiopia, Bangladesh and Kenya); and (b)\nIDPs in countries where UNHCR is active who are not protected or assisted by the agency. In 2015, IDMC’s aggregate\nfigure for conflict-induced internal displacement was 3.3 million higher than UNHCR’s aggregate figure for IDPs\nprotected or assisted by the agency.\n\n29 IDMC’s 2016 report presents both data sets alongside each other. In certain contexts, there can be significant\noverlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement\nand natural disasters (e.g. in Afghanistan) leading to possible gaps or double counting if these categories are combined.\n\n30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility.\nIt is designed to regularly and systematically capture, process and disseminate information to provide a better\nunderstanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been\nactive in over 40 countries since its inception in 2004. See http://www.globaldtm.info/.\n\n31 This is typically defined as nomads not having access to their traditional routes, but routes can vary.\n\n32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements\nare reflected in IDMC’s end-2015 data.\n\n33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance\nrather than to determine status.", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:006980:15:2:2", "start": 966, "end": 998, "surface": "IOM Displacement Tracking Matrix", "probe_tag": "confusion", "probe_score": 0.694, "luna_label": 0}, {"key": "prwp:006980:15:2:3", "start": 1646, "end": 1659, "surface": "end-2015 data", "probe_tag": "confusion", "probe_score": 0.854, "luna_label": 1}]}, {"key": "aj-072", "text": "Gibson, J. and McKenzie, D. (2011). Eight questions about brain drain. _Journal of Economic_\n\n\n_Perspectives_, 25(3):107–128.\n\n\nGibson, J., Olivia, S., Boe-Gibson, G., and Li, C. (2021). Which night lights data should we\n\n\nuse in economics, and where? _Journal_ _of_ _Development_ _Economics_, 149:102602.\n\n\nGiuliano, P. and Ruiz-Arranz, M. (2009). Remittances, financial development, and growth.\n\n\n_Journal_ _of_ _Development_ _Economics_, 90(1):144–152.\n\n\nGlobal Forest Watch (2023). Annual tree cover loss 2001–2021.\n\n\nGlobal Sanctions Database (2023). The global sanctions database (gsdb) 2023.\n\n\nGodlonton, S. and Theoharides, C. (2025). Diffusion of reproductive health behavior through\n\n\ninternational migration: Effects on origin-country fertility. _American_ _Economic_ _Review_,\n\n\n115(10):3597–3637.\n\n\nGonz´alez, F., Mart´ınez, L. R., Mu˜noz, P., and Prem, M. (2023). Higher education and mor\n\ntality: Legacies of an authoritarian college contraction. _Journal of the European Economic_\n\n\n_Association_, page jvad066.\n\n\nGoodman, G. L. and Hiskey, J. T. (2008). Exit without leaving: Political disengagement in\n\n\nhigh migration municipalities in Mexico. _Comparative_ _Politics_, 40(2):169–188.\n\n\nGrossmann, J., Jurajda,, and Roesel, F. (2024). Forced migration, staying minorities,\n\n\nand new societies: Evidence from", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001361:47:0:1", "start": 560, "end": 585, "surface": "global sanctions database", "probe_tag": "confusion", "probe_score": 0.198, "luna_label": 0}]}, {"key": "aj-073", "text": " barriers\ninclude the existence of specific provisions which require regulators to recognize\nregulatory measures performed in other countries; to use internationally harmonized\nstandards and certification procedures; or avoid unnecessary trade restrictiveness.\nEngaging in mutual recognition agreements with other countries also helps reducing\nother barriers to trade and investment. In this respect, Romania has in fact achieved best\npractice.\n\n**Figure 18. Regulatory Barriers to Trade and Investment**\n\n\n\n2.0\n\n\n1.5\n\n\n1.0\n\n\n0.5\n\n\n0.0\n\n\n\n\n\n\n|1.6 1.6|Col2|Col3|Col4|Col5|Col6|Col7|\n|---|---|---|---|---|---|---|\n|**1.6**<br>**1.6**|**1.6**<br>**1.6**|**1.6**<br>**1.6**|||||\n||||||||\n|**0.4**|**0.4**|**0.4**|||||\n|**0.0**<br>**0.0**<br>**0.0**<br>**0.0**<br>**0.2**<br>**0.2**|||||||\n\n\n\n_Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, and Turkey. Values refer to 2006 for Romania\nand Bulgaria, 2004 for Brazil, and 2003 for all other countries. Romania’s 2002 score was calculated using\na different methodology so is not strictly comparable. For full data set see appendix I.\n\n\n26", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:003609:30:3:0", "start": 1063, "end": 1076, "surface": "full data set", "probe_tag": "confusion", "probe_score": 0.8415, "luna_label": 1}]}, {"key": "aj-074", "text": " that individual incomes follow an autoregressive lognormal process with individual fixed\n\n\neffects. We show that the limited information embodied in the time-series of these aggregate moments\n\n\nis sufficient to place bounds on the extent of mobility in the income distribution, even though we do not\n\n\nobserve income dynamics at the individual level. An empirical application using data from the PSID\n\n\nconfirms that these bounds generally contain the point estimates that are obtained using the record-level\n\n\npanel data, and moreover are reasonably tight. Encouraged by these findings, we apply our methodology\n\n\nto two large cross-country datasets, namely the WID (including mostly high income countries) and the\n\n\nWorld Bank’s PovcalNet (including mostly developing countries). Some of the cross-country patterns we\n\n\nobserve in estimates of mobility seem quite plausible given our priors. For example, among the high\n\n\nincome countries, the Scandinavian countries and much of Europe show relatively high levels of income\n\n\npersistence, while the United States, Singapore and Taiwan rank among the countries with low levels of\n\n\nincome persistence. When comparing estimates between the WID and PovcalNet, our estimates suggest\n\n\n24", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:007100:25:1:0", "start": 397, "end": 401, "surface": "PSID", "probe_tag": "keep", "probe_score": 0.9514, "luna_label": 1}, {"key": "prwp:007100:25:1:4", "start": 732, "end": 741, "surface": "PovcalNet", "probe_tag": "confusion", "probe_score": 0.8743, "luna_label": 1}]}, {"key": "aj-075", "text": "Figure III\nFraction of Teachers Admitted to the Certification Process, At, or Before the Indicated Year\n\n\n_Notes:_ Teachers were admitted to the certification process at different points in time. The first batch of teachers was\nadmitted in 2006. The intervention took place in 2009, which created a difference between treatment and control schools\nin terms of the fraction of teachers admitted to the certification program. The bars represent fractions of teachers who\nwere admitted to the certification program at, or before the indicated year. For example, around 60% of teachers in\ntreatment schools were admitted to the certification program in the year 2009 or before, against roughly 30% in control.\nWe use baseline data to construct the 2006, 2007, 2008, 2009 bars, Y2 data to construct the 2010 and 2011 bars, and Y3\ndata to construct the 2012 bar.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure IV\nCompleting the Certification Process (Left) and Being Paid the Certification Allowance (Right)\n\n\n_Notes:_ The left panel presents the fraction of teachers who completed the certification process. The right panel presents\nthe fraction of teachers who completed the certification process and were paid the certification allowance.", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:007222:34:0:2", "start": 822, "end": 829, "surface": "Y3\ndata", "probe_tag": "confusion", "probe_score": 0.3396, "luna_label": 1}]}, {"key": "aj-076", "text": " access to justice, making justice more accessible for vulnerable populations\nis particularly relevant in developing countries. Data may be used to identify gaps in access to justice, for example, by comparing legal need surveys to the actual disputes that are brought to courts.\nThis may help policy makers better understand if there are specific disputes that elude the justice\nsystem, where better access to legal means of resolving disputes might be the main gap. For instance,\nif surveys indicate that a territory is deeply affected by domestic violence, but few cases of domestic\nviolence are resolved in courts, this will provide evidence of a gap in cases of domestic violence\nbeing resolved in courts. Data systems may also be used to inform citizens of their prospective out", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002284:32:2:0", "start": 210, "end": 228, "surface": "legal need surveys", "probe_tag": "confusion", "probe_score": 0.6641, "luna_label": 0}]}, {"key": "aj-077", "text": "Through this process, infrastructure spending and policy initiatives can be better informed, industrial\nclusters developed in the correct spatial context, and other bottlenecks and issues can be addressed\nwithin the context of the total national freight‐flow landscape.\n\n\n**<u>Macro Logistics Planning and Policy applications</u>**\n\n\nCommodity‐level data enable an understanding of current modal shares, the sustainability of future\nfreight flows given the current trajectories, and where to target modal shift given commodity\ncharacteristics.\n\n\nZhang et al. (2003) reported on the development of a methodology, based on the CFS data, to\nsystematically estimate state‐wide truck travel demand using the state of Mississippi as a case study.\nThe first step is a detailed commodity flow data analysis including modes and origin‐destinations,\nwhich facilitates conversion of road flows into truck trips by using average density of freight and\naverage load per truck to estimate the composition of different truck types carrying different\ncommodity groups. One of the objectives is to identify and prioritize intermodal infrastructure\nopportunities given that highway networks are experiencing severe congestion ‐ with concomitant\nexternality costs ‐ while other inland transportation modes are underutilized. Feasibility studies for the\ndevelopment of intermodal technologies are enhanced through the availability of commodity‐level\ndata, since specific industries and freight owners can be targeted per route.\n\n\nSimilarly, the State of Alabama used the CFS to develop a state‐wide model that showed the possible\nimpact of new relocated industries and the movement of their goods on the state’s existing\ntransportation infrastructure. Understanding the total character of freight movements along a corridor\n\n- its prevalent commodities and potential safety and operational constraints – facilitates the\nidentification of potential options for shifting commodities to alternate modes of transport to alleviate\ncongestion (Anderson and Harris, 2011).\n\n\nIn South Africa, the segmentation enabled by the country’s disaggregated macroscopic FDM informed\nthe development of a domestic intermodal strategy (Havenga et al., 2012) and allowed for confidential\ninput to the national rail policy to change the thrust of rail", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:007287:16:0:0", "start": 334, "end": 354, "surface": "Commodity‐level data", "probe_tag": "confusion", "probe_score": 0.4299, "luna_label": 1}, {"key": "prwp:007287:16:0:2", "start": 770, "end": 789, "surface": "commodity flow data", "probe_tag": "confusion", "probe_score": 0.4892, "luna_label": 1}]}, {"key": "aj-078", "text": "2014 Boyd and\nCurtis\n\n\n2019 Grover, Iacovone, and\nChakraborty\n\n\n2019 Bloom, Brynjolfsson, Foster, Jarmin,\nPatnaik,\nSaportaEksten, Van\nReenen\n\n\n2019 Bloom,\nIacovone,\nPereiraLopez, Van\nReenen\n\n\n2020 Karplus and\nZhang\n\n\n\nUnited States 2006 to 2007 energy expenditure\nover gross output in\npercentage\n\n\nCroatia 2017 sales per employee,\nprofit\n\n\nUnited States 2010 to 2015 total factor productivity\n\n\nMexico, United States 2014 to 2015 total factor productivity, profit\n\n\n\nChina March and\nApril 2016\n\n\n**47**\n\n\n\nBetter managed firms produce more output and are\nlarger. At best, findings\nshow a modest relationship between management\nand energy efficiency, but\nstandard errors are large.\n\n\nManufacturing firms are\nbetter managed than service firms. Better management is consistently and\nstrongly associated with\nbetter performance.\n\n\nThe study finds large differences in productivity\nassociated with variations\nin management practices.\n\n\nManagement score is\nstrongly and positively\ncorrelated with firm\nproductivity.\n\n\nMoving from the 25th\nto 75th percentile on\nmanagement score reduces\nelectricity intensity by\n40% in value terms and\n<u>by</u> <u>33%</u> <u>in</u> <u>physical</u> <u>terms.</u>\n\n\n\nenergy efficiency, total\nenergy use\n\n\n\nmerging survey of firm\nmanagement practices\nwith US Census data\non manufacturing\n\n\nsurvey on firm capabilities in Croatia, including a module for capturing management capabilities\n\n\nsurvey of structured\nmanagement practices\nin 35,000 manufacturing plants\n\n\nsurvey of management\npractices, combined\nwith data from Mexico’s National Survey\non Productivity and\nCompetitiveness in\n2015\n\n\ntwo-part survey on\nmanagement and energy management;\nelectricity use and\nproduction information\n<u>from</u> <u>SECA</u> <u>survey</u>", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002197:49:0:2", "start": 1488, "end": 1518, "surface": "survey of management\npractices", "probe_tag": "confusion", "probe_score": 0.5352, "luna_label": 1}]}, {"key": "aj-079", "text": " do not report the real\n\n\ncapital stock. Instead, firms report the value of their fixed capital stock at original purchase\n\n\nprices. As these book values are the sum of nominal values for different years, they are not equal\n\n\nto the real capital stock and are not comparable across time and across firms.\n\n\nIdeally, we need information on all past investments of a firm to construct the real capital\n\n\nstock. This, however, we do not have. Roughly following Brandt et al. (2011), we make several\n\n\nassumptions and convert the value of their capital stock at original purchase prices into real values\n\n\nby the following procedures.\n\n\nFirst, we estimate the nominal value of capital stock for each year between firm’s established\n\n\nyear and the first year in which the firm appears in our data set. For simplicity of presentation here,\n\n\nwe assume that it is 1998, the first year of our panel. We assume that the growth rate of nominal\n\n\ncapital stock of each firm equals to the growth rate of nominal capital stock in the corresponding\n\n\n18 China’s Industrial Classification (CIC) system has 30 two-digit manufacturing industries.\n19 The 1997 IO table is used to construct the input deflators of 1998-2000, and the 2002 IO table is used to construct\nthe input deflators of 2001-2005, and the 2007 IO table is used to construct the input deflators in 2006-2007.\n\n\n43", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:006378:44:1:3", "start": 1291, "end": 1304, "surface": "2007 IO table", "probe_tag": "confusion", "probe_score": 0.4535, "luna_label": 1}]}, {"key": "aj-080", "text": " Islamic and (iii) both conventional\n\n\nand Islamic banks. Another, smaller, sample comprises only countries with both conventional\n\n\nand Islamic banks, which allows us to control for any unobserved time-invariant effect by\n\n\nintroducing country dummies. This smaller sample includes 486 banks across 20 countries, out\n\n\nof which 89 are Islamic banks.\n\n\nIn Table 1, we present data on 22 countries with both conventional and Islamic banks. <sup>4</sup>\n\n\nSpecifically, we present the number of Islamic and total banks as well as the share of Islamic\n\n\nbanks’ assets in total banking assets, all for 2007, the latest year in our pre-crisis sample. Further,\n\n\nwe report the number of listed banks, for both Islamic and conventional banks. On average,\n\n3 We use unconsolidated data when available and consolidated if unconsolidated is not available, in order to not\ndouble count subsidiaries of international banks.\n4 Two countries are part of the crisis sample and are used in the analysis in Section 5.\n\n\n9", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:004633:10:1:0", "start": 376, "end": 396, "surface": "data on 22 countries", "probe_tag": "confusion", "probe_score": 0.8571, "luna_label": 1}, {"key": "prwp:004633:10:1:1", "start": 758, "end": 777, "surface": "unconsolidated data", "probe_tag": "confusion", "probe_score": 0.07, "luna_label": 1}]}, {"key": "aj-081", "text": " estimations based on COVID-19 NLPS round 6; _Notes_ : Control variables\nare: household size and climatic shocks such as floods and drought. _AugmLockdown_ takes value\none if the person lives in states where additional lockdown measures were implemented (see\nsection 2.2.3 for more details), and zero otherwise. Respondents whose schools are still closed\nat the time of the survey in October 2020, those who are still on leave or refuse to go to school\nbecause they are afraid of contracting the coronavirus, and those who are waiting for admission\nare excluded from the analysis. Standard errors are clustered at the LGAs level. *** pă0.01, **\npă0.05, * pă0.1\n\n\n36", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000214:37:2:0", "start": 22, "end": 43, "surface": "COVID-19 NLPS round 6", "probe_tag": "confusion", "probe_score": 0.6902, "luna_label": 1}]}, {"key": "aj-082", "text": "Table 4 reports selected coefficients. Longer trips are faster: the elasticity of trip speed\n\n\nwith respect to trip length is 0.24 in columns 1 and 4, and larger for longer trips in the\n\n\nother columns where we introduce a quadratic term. This is a prominent feature of urban\n\ntransportation data in other contexts. <sup>26</sup> Regressing log trip speed on log trip length without\n\nany further control yields an R <sup>2</sup> of 0.40.\n\n\nUnsurprisingly, trips further from the center are also faster. The elasticity of trip speed\n\n\nwith respect to distance from the center of 0.15 is a quite large, implying that a trip at 10\n\n\nkilometers from the center of a city is about 40% faster than one a kilometer away.\n\n\nIn column 1, we find fairly large differences of up to 10% in speed between different types\n\n\nof trips. These differences become mostly insignificant and economically small when controls\n\n\nfor trip location are added in column 2. In the end, amenity trips are slightly faster while\n\n\ncircumferential trips are slower but the speed difference between them is only about 1%. We\n\nalso note that regressing log trip speed solely on trip type indicators yields an R <sup>2</sup> of only\n\n\nabout 0.003. These two results are reassuring, and suggest that the design of our hypothetical\n\n\ntrips is not driving our results. In Appendix C, we report versions of table 4 for each type of\n\n\ntrip. While the non-linearities for the effect of trip length and distance to the center slightly\n\n\ndiffer, the results overall are similar to those in table 4, suggesting that the simple additive\n\n\nspecification of table 4 is not obscuring deeper differences between trip types.\n\n\nWe now turn to the regression coefficients not reported in table 4. Starting with the\n\n\nweather, we find that characteristics associated with bad weather such as rain, high levels of\n\n\nhumidity, high temperatures, and", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:007469:19:0:0", "start": 270, "end": 296, "surface": "urban\n\ntransportation data", "probe_tag": "confusion", "probe_score": 0.3458, "luna_label": 0}]}, {"key": "aj-083", "text": " these gains. We complement these findings with two addi\ntional exercises. The first one quantifies the role of the interaction between\n\n\n7Our data combine census-based information of manufacturing sectors in Latin America,\nSouth Asia, and sub-Saharan Africa with a selected sample of countries from the Amadeus\ndatabase, the selection being driven by the extent to which the size distribution portrayed in\nthese data is comparable with the one reported in Eurostat’s Structural Business Statistics\ndatabase. More details are provided in Section 4.\n\n8Since the Chilean manufacturing data cover the universe of manufacturing plants with\n10 or more workers, we apply the same truncation to the US data to arrive at the average\nsize of 118 workers in the US. We explain data coverage details for all countries in the data\nsection.\n\n9The actual estimate of the elasticity is 0.32. However, we take into account that there\nis correlated misallocation in the US, our benchmark of efficiency. By subtracting the US’\nestimate of the distortion elasticity of 0.15 from the estimate for Chile, we reach the value\nof 0.17. We proceed in this fashion for all of the countries.\n\n\n5", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000362:6:1:1", "start": 468, "end": 507, "surface": "Structural Business Statistics\ndatabase", "probe_tag": "keep", "probe_score": 0.935, "luna_label": 1}, {"key": "prwp:000362:6:1:2", "start": 561, "end": 587, "surface": "Chilean manufacturing data", "probe_tag": "confusion", "probe_score": 0.8245, "luna_label": 1}]}, {"key": "aj-084", "text": "Haelermans, C., M. Jacobs, R. van der Velden, L. van Vugt and S. van Wetten. 2022. \"Inequality\n\nin the Effects of Primary School Closures Due to the COVID-19 Pandemic: Evidence from\nthe Netherlands.\" _AEA Papers and Proceedings_ 112: 303-07.\nHallin, A.E., H. Danielsson, T. Nordström and L. Fälth. 2022. “No learning loss in Sweden during\n\nthe pandemic evidence from primary school reading assessments.” _International Journal_\n_of Educational Research_ 114: 102011;\nHill, C.J., H.S. Bloom, A.R. Black and M.W. Lipsey. 2008. “Empirical benchmarks for\n\ninterpreting effect sizes in research.” _Child development perspectives_ 2: 172-177.\nEngzell, P., A. Frey, D. M. Verhagen. 2021. ‘‘Learning Loss Due to School Closures during the\n\nCOVID-19 Pandemic.’’ _Proceedings of the National Academy of Sciences_ 118 (17): 1-7.\nJack R., C. Halloran, J. Okun and E. Oster. 2023. Pandemic schooling mode and student test scores:\n\nevidence from US school districts. _American Economic Review: Insights_ .\n<mark>Kraft, M.A. 2020. \"Interpreting effect sizes of education interventions.\"</mark> _<mark>Educational</mark>_\n\n_<mark>Researcher</mark>_ <mark>49(4): 241-253.</mark>\n<mark>Lee, J. 2010. “Tripartite growth trajectories of reading and math achievement: Tracking national</mark>\n\n<mark>academic progress at primary, middle, and high school levels.", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000966:14:0:0", "start": 367, "end": 401, "surface": "primary school reading assessments", "probe_tag": "drop", "probe_score": 0.0456, "luna_label": 0}]}, {"key": "aj-085", "text": " supplies required for the study at the KEMRI-Wellcome unit in\nNairobi. It includes photocopying, stationary, communication and printing over the survey period. The estimates are based on\nroutine costs charged at the unit.\n\n_Travel and transportation_ . During the study preparation an investigator will need to meet with local officials and school head\nteachers. Travel and accommodation costs for these trips have been factored in here.\n\nThe education component of the study involves modifying the way Standard 1 and 2 classroom teachers develop their\nstudents’ literacy skills. To do this, the intervention includes an initial residential teacher training over a four-day period\nfollowed by ongoing monitoring and support. To ensure that all of the school’s personnel are informed, the Head Teachers\nand the Senior Teachers will also be invited to the training. This way, they will be available to support the classroom teachers\nduring the implementation of the intervention. The literacy intervention will be implemented in 50 schools involving at least\nthree teachers from each.\n\nThe study has a longitudinal design to investigate the interaction of the malaria intervention and the literacy intervention. To\nunderstand the effects of the interventions we will measure students’ education achievement at the onset and end of the\ninterventions. Student achievement in 100 schools will be measured. While the surveys are going on, a supervisor, three\nlaboratory technicians, one laboratory technician, one interviewer, one nurse plus one driver will need to travel to the field\nsites to undertake school surveys. All costs for travel and accommodation have been estimated using standard KEMRI\nmileage and per diem charges.\n\n_Operating costs_ **.** This includes all costs required to carry out the field studies and include laboratory materials, supplies,\nreagents and disposables. Training workshops will be organized by ESACIPAC to train all the technicians at the DVBD in\nKwale hospitals on parasite microscopy. Also included are laboratory quality control costs.\n\n\n67", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:005912:68:1:0", "start": 1600, "end": 1614, "surface": "school surveys", "probe_tag": "drop", "probe_score": 0.0361, "luna_label": 0}]}, {"key": "aj-086", "text": " Table 1 below summarizes the average\n\nmeasurements found in recent LSMS-ISA surveys. <sup>4</sup> The SR-GPS difference observed in Nigeria is\n\n\nconsiderably larger than in the four other LSMS surveys listed.\n\n\n<<< TABLE 1 HERE >>>\n\n\n3 While the differences observed in Nigeria are quite large nationwide, particular states and enumerators see\nsignificantly larger divergences. The two states with the highest discrepancy in measurements are Osun and Ondo\n(10,469% and 11,534%, respectively). Within those two states, three particular interviewers contribute to the\nmajority of the difference suggesting that the problem may be largely the product of human error such as incorrect\nuse of the GPS device or inaccurate recording of self-reported and/or GPS figures.\n4 One important difference between the GHS-Panel and the other LSMS-ISA surveys presented here is that in the\nGHS-Panel farmers are allowed to use nonstandard area units when estimating plot size while all the other surveys\nonly allow farmers to report in standard units (acres, square meters, or hectares). This may partially explain why the\nself-reported/GPS difference is so much large in Nigeria.", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "sample:prwp:001776:8:1:0", "start": 68, "end": 84, "surface": "LSMS-ISA surveys", "probe_tag": "confusion", "probe_score": 0.6001, "luna_label": 1}, {"key": "sample:prwp:001776:8:1:1", "start": 804, "end": 813, "surface": "GHS-Panel", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1}, {"key": "sample:prwp:001776:8:1:2", "start": 875, "end": 884, "surface": "GHS-Panel", "probe_tag": "drop", "probe_score": 0.0051, "luna_label": 1}]}, {"key": "aj-087", "text": " of yearly protests or organized violence conflicts\n\ndummy equal to 1 if the cell hosts a refugee camp at time t and 0 otherwise. The main hypothesis\n\noccurring in cell _c_ in country _i_ at time _t_ . Our main covariate of interest is 𝑅 **𝑅** 𝑅 **𝑅** 𝑅 𝑅𝑅𝑐 **𝑐** 𝑐𝑐 which is a\n\n\n\n**𝑐**\n\ndummy equal to 1 if the cell hosts a refugee camp at time t and 0 otherwise. The main hypothesis\n\n**𝑅** **𝑅** **𝑐**\n\nwe want to test is whether the presence of a refugee camp in a cell impacts on the number of\n\nprotests or violence events. In alternative specifications we introduce interaction terms to test\n\nwhether some of the geographical characteristics of camps - such as their size, remoteness and\n\nsocio-economic marginality – matter in shaping the links between refugees’ presence and protests\n\n\n\n17 We thank Iacoella, Martorano, Metzger and Sanfilippo for sharing the data employed in their recent analysis on the\nimpact of Chinese investments on protests in Africa (for details see Iacoella et al. 2021).\n18 Note that, in order to be registered in GDELT, an event must be reported in digitalized news (from newspapers, news\nagencies, digital media, web-based news aggregators) implying that although a very large portions of events are likely\nto be registered this source cannot be considered as fully representative of all kinds of protests occurring in the world.\nTo our knowledge, all other database collecting information on conflicts suffer from a similar bias.\n\n15", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002211:16:2:0", "start": 1047, "end": 1052, "surface": "GDELT", "probe_tag": "drop", "probe_score": 0.0438, "luna_label": 1}]}, {"key": "aj-088", "text": "2015) are comparable.\n\nIII.2.3 Evaluating the comparability of poverty rates of EICV3 and EICV4 in NISR (2016)\n\nThe previous section shows that we do not have enough evidence to guarantee the comparability of the\npoverty rates in NISR (2015). Thus, we go on to examine whether the official poverty rates presented in\nNISR (2016) are comparable. NISR (2016) estimates poverty rates using the following inequalities:\n\nFor EICV3 (2010/11) data:\n\n\n<u>𝑒�������</u>\n\n<u>�������</u> <sup>�159,375     �7�</sup>\n\n𝜋�����\n\n\nFor EICV4 (2013/14) data:\n\n\n<u>𝑒�������</u>\n\n<u>�������</u> <sup>�159,375     �8�</sup>\n\n𝜋�����\n\n\n\n10 𝜋���������� based on survey data is not directly available. We therefore estimate it using the following equality 𝜋���������� 𝜋����������/𝜋����������. To estimate the denominator, we use a population weighted average of 5 regional monthly price\n\n\n\n𝜋����������/𝜋����������. To estimate the denominator, we use a population weighted average of 5 regional monthly price\n\nindices, i.e. 𝜋���������� - �𝜋���������������, which equates to 0.955. As 𝜋���������� �1, 𝜋������", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:007505:14:1:1", "start": 518, "end": 523, "surface": "EICV4", "probe_tag": "drop", "probe_score": 0.02, "luna_label": 1}]}, {"key": "aj-089", "text": "- access to credit bureau loan-level data – to analyze borrower characteristics\nand assess risk, particularly for consumer loans <sup>64</sup> .\nLonger-term measures would include:\n\n    - data collection on the financial condition of corporates and households (e.g.\nbalance sheets, leverage ratios, income indicators etc.) – to better understand\nthe connection between developments in the real sector and financing patterns\n(i.e. demand- versus supply-side factors)\n\n    - identification and analysis of alternative financing sources for companies and\nhouseholds outside the formal financial system – to quantify their importance\nand assess their impact on formal financing sources\n\n    - development of a more detailed chart of accounts on bank revenues by loan\nproduct (including both interest income and fees) – to assess bank\nperformance when analyzing competition issues in different credit segments.\n\n\n**Transparency and disclosure** : The development and public disclosure of\nstandardized credit affordability indicators (i.e. interest rate time series by type of loan\nproduct and by provider) as well as of accessibility indicators (i.e. loan volumes by\nproduct, firm size, economic sector and state) would greatly contribute to a more\ntransparent credit market. As has been the experience in other countries, publication of\nthese indicators could provide a further impetus to competition across credit providers <sup>65</sup>\nand help borrowers become aware of credit pricing differences and hence more\nselective <sup>66</sup> ; these indicators could also allow the authorities to better track credit market\ndevelopments and formulate policy in areas such as financial access and competition.\n\n\n**Promotion of SME financing** : As mentioned previously, the prospects for SME\nfinancing growth are less positive relative to other market segments, at least in the short\nterm. Given the importance of SMEs for Mexico’s economy, the authorities will need to\ncontinue to promote SME financing and strengthen the credit infrastructure for this\nmarket segment. In", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:003471:40:0:0", "start": 12, "end": 41, "surface": "credit bureau loan-level data", "probe_tag": "confusion", "probe_score": 0.4173, "luna_label": 1}, {"key": "prwp:003471:40:0:1", "start": 983, "end": 1027, "surface": "standardized credit affordability indicators", "probe_tag": "drop", "probe_score": 0.0434, "luna_label": 0}]}, {"key": "aj-090", "text": "**_Appendix A_**\n\n\n**_Appendix Table A.1: Sectoral Classification of Services_**\n\n\nSub-sectors in KLEMS\nSub-Sector Description\n<u>database: NACE 2-digit</u>\n\n40 Electricity, gas, steam and hot water supply (Non-ICT) [High skill]\n41 Collection, purification and distribution of water (Non-ICT) [High skill]\n50 Sale, maintenance and repair of motor vehicles and motorcycles; retail sale services of automotive fuel (Non-ICT) [Low skill]\n51 Wholesale trade and commission trade, except of motor vehicles and motorcycles (ICT user) [Low skill]\n52 Retail trade, except of motor vehicles and motorcycles; repair of personal and household goods (ICT user) [Low skill]\n55 Hotels and restaurants (Non-ICT) [Low skill]\n60 Land transport; transport via pipelines (Non-ICT) [Low skill]\n61 Water transport (Non-ICT) [Low skill]\n62 Air transport (Non-ICT) [High skill]\n63 Supporting and auxiliary transport activities; activities of travel agencies (Non-ICT) [High skill]\n64 Post and telecommunications (ICT producer) [High skill]\n65 Financial intermediation, except insurance and pension funding (ICT user) [High skill]\n66 Insurance and pension funding, except compulsory social security (ICT user) [High skill]\n67 Activities auxiliary to financial intermediation (ICT user) [High skill]\n70 Real estate activities (Non-ICT) [High skill]\n71 Renting of machinery and equipment without operator and of personal and household goods (ICT user) [High skill]\n72 Computer and related activities (ICT producer) [High skill]\n73 Research and development (ICT user) [High skill]\n74a* Legal, technical, and advertising (ICT user) [High skill]\n<u>74b*</u> <", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:003564:43:0:0", "start": 140, "end": 152, "surface": "NACE 2-digit", "probe_tag": "drop", "probe_score": 0.0087, "luna_label": 0}]}, {"key": "aj-091", "text": "> <u>$116.47</u> <u>$20.16</u> <u>0.000</u>\nDiscrepancy between expert’s Round 1 prediction and mean for $61.43 $44.38 0.000\n\n\n\n_<u>Group 1</u>_\n\n\n\n_<u>mean</u>_\n\n\n\n_<u>mean</u>_\n\n\n\nhis/her country/region\n\n\n\n$61.43 $44.38 0.000\n\n\n\nRatio of expert’s Round 1 prediction to mean for his/her\n\n\n\n2.17 0.27 0.000\n\n\n\ncountry/region\n\n\n\nProximity of Delphi estimate to result of an actual SP survey 6.48 5.78 0.043\nDifficulty of successfully implementing a SP survey on this topic 5.47 5.85 0.230\nNumber of CV surveys carried out 7.22 7.18 0.490\nNumber of CE surveys carried out 3.77 3.63 0.448\nNumber of surveys (CV+CE) carried out about biodiversity and 3.22 3.56 0.363\n\n\n\necosystem services\n\n\n\n3.22 3.56 0.363\n\n\n\nNumber of benefit transfer exercises 2.58 2.20 0.329\nJournals: 1 <sup>st</sup> principal component -0.079 -0.272 0.261\nJournals: 2 <sup>nd</sup> principal component -0.118 0.112 0.166\nJournals: 3 <sup>rd</sup> principal component -0.104 -0.215 0.291\nJournals: 4 <sup>th</sup> principal component 0.072 0.148 0.352\nNumber of CV and CE papers published in national and 4.65 2.72 0.012\n\n\n\ninternational journals in past 5 years\n\n\n\n4.65 2.72 0.012\n\n\n\nNumber of", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000008:27:1:2", "start": 547, "end": 557, "surface": "CE surveys", "probe_tag": "drop", "probe_score": 0.0175, "luna_label": 0}]}, {"key": "aj-092", "text": "**Table A.4.2: Comparing weighted Internet Survey data with GMD for the model variables, Arab**\n\n|Col1|Republic|of Egypt|Col4|Col5|\n|---|---|---|---|---|\n||GMD|RIWI-wave1|RIWI-wave2|RIWI-wave3|\n|Age between 15 and 24|31.71%|30.00%|35.13%|43.59%|\n||(29.91,33.51)|(25.40,34.60)|(27.64,42.63)|(33.30,53.88)|\n|Age between 25 and 54|51.93%|52.14%|56.92%|52.95%|\n||(47.53,56.32)|(35.02,69.27)|(38.75,75.09)|(44.16,61.74)|\n|Age between 55 and 100|16.36%|17.86%|7.95%|3.46%|\n||(10.45,22.27)|(0.25,35.47)|(-3.89,19.78)|(-4.77,11.69)|\n|Female|49.25%|51.31%|43.23%|44.87%|\n||(47.95,50.55)|(40.75,61.87)|(34.20,52.26)|(23.39,66.35)|\n|Urban|66.15%|72.91%|72.10%|72.82%|\n||(", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000811:33:0:0", "start": 25, "end": 54, "surface": "weighted Internet Survey data", "probe_tag": "confusion", "probe_score": 0.628, "luna_label": 0}, {"key": "prwp:000811:33:0:1", "start": 182, "end": 192, "surface": "RIWI-wave3", "probe_tag": "drop", "probe_score": 0.0188, "luna_label": 0}]}, {"key": "aj-093", "text": "###### **Appendix A.1: Sample Selection Comparing Raw, Cross-Sectional, and Panel Samples**\n\n**Table A.1: Descriptive statistics on the 2012 and 2018 estimation samples versus raw data**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Men|Col2|Col3|Col4|Col5|Col6|Women|Col8|Col9|Col10|Col11|Col12|\n|---|---|---|---|---|---|---|---|---|---|---|---|\n|<br>2012<br> <br> <br> <br>|<br>2012<br> <br> <br> <br>|<br>2012<br> <br> <br> <br>|2018<br> <br> <br>|2018<br> <br> <br>|2018<br> <br> <br>|2012<br> <br> <br> <br>|2012<br> <br> <br> <br>|2012<br> <br> <br> <br>|2012<br> <br> <br> <br>|2018<br> <br>|2018<br> <br>|\n|Mean<br>(St. Dev.)<br>Raw<br> <br> <br>|Sample<br> <br>|Panel<br> <br>|Raw<br> <br>|Sample<br> <br>|Panel<br> <br>|Raw<br> <br>|Sample<br> <br>|Panel<br> <br>|Raw<br> <", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000894:26:0:0", "start": 176, "end": 184, "surface": "raw data", "probe_tag": "drop", "probe_score": 0.0311, "luna_label": 0}]}, {"key": "aj-094", "text": "Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data\n\n\nDabla-Norris, Era, Mark Gradstein, and Gabriela Inchuauste. \"What Causes Firms to Hide\nOuput? The Determinants of Informality.\" _Journal of Development Economics_, 2008: vol. 85,\n1-27.\n\n\nDell'Anno, Roberto, Miguel Gomez-Antonio, and Angel Pardo. \"The Shadow Economy in\nThree Mediterranean Countries: France, Spain, and Greece. A MIMIC Approach.\" _Empirical_\n_Economics_, 2007: Vol. 33, 51-84.\n\n\nDollar, David, Mary Hallward-Driemeier, and Taye Mengistae. \"Investment Climate and\nFirm Performance in Developing Economics.\" _Economic Development and Cultural Change_,\n2005: Vol. 54, No. 1, 1-31.\n\n\nErard, Brian, and Jonathan S. Feinstein. \"Hoesty and Evasion in the Tax Compliance Game.\"\n_Rand Journal of Economics_, 1994a: Vol. 25, No. 1, 1-19.\n\n\nFrey, Bruno S., and Hannelore Weck-Hannemann. \"The Hidden Economy as an\n'Unobserved' Variable.\" _European Economic Review_, 1984: Vol. 26, 33-53.\n\n\nGatti, Roberta, and Maddalena Honorati. \"Informality among Formal Firms: Firm-level,\nCross-country Evidence on Tax Compliance and Access to Credit.\" _World Bank Policy_\n_Research Working Paper No. 4476_, 2008: 1-34.\n\n\nGiles, David E.A. \"Measuring the Hidden Economy: Implications for Econometric Modeling.\"\n_The Empirical Journal_, 1999: Vol. 109, 370-380.\n\n\nGraetz, Michael J., and Jennifer F., Wilde, Louis L. Reinganum", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:004810:38:0:1", "start": 75, "end": 90, "surface": "Tax office Data", "probe_tag": "drop", "probe_score": 0.0439, "luna_label": 0}]}, {"key": "aj-095", "text": "br>**Location**<br>India (state of Andhra Pradesh)<br>US<br>Australia<br>US<br>**Sample restrictions/**<br>**Mother characteristics**<br>Sample restricted to households  in rural<br>areas in both periods<br>**Sample**|**Data source**<br>YLS<br>NLSY<br>HILDA<br>ECLS-K<br>**Years**<br>2007 & 2009-2010<br>biannually from 1986-1998<br>2,007<br>fall 1998, spring 1999, fall 1999, spring<br>2000, spring 2002, spring 2004<br>**Sample size**<br>3,725<br>16,650 child-year observations from 6283<br>mothers; sample size for FE ranges from<br>4,159 for child FE to 7919 for sibling FE<br>907 (IV), 430 (mother FE)<br>18,990 person-years for 6,330 indivdiuals<br>**Location**<br>India (state of Andhra Pradesh)<br>US<br>Australia<br>US<br>**Sample restrictions/**<br>**Mother characteristics**<br>Sample restricted to households  in rural<br>areas in both periods<br>**Sample**|**Data source**<br>YLS<br>NLSY<br>HILDA<br>ECLS-K<br>**Years**<br>2007 & 2009-2010<br>biannually from 1986-1998<br>2,007<br>fall 1998,", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001080:25:3:0", "start": 237, "end": 240, "surface": "YLS", "probe_tag": "drop", "probe_score": 0.0126, "luna_label": 0}, {"key": "prwp:001080:25:3:2", "start": 252, "end": 257, "surface": "HILDA", "probe_tag": "drop", "probe_score": 0.0126, "luna_label": 1}]}, {"key": "aj-096", "text": " or low employment level, similar (baseline) or lower\n(conservative) productivity. In the conservative productivity scenario,\nwe assumed approximately 10% lower refugee productivity estimated based\non incomes reported in MSNA Poland 2023 and data from Statistics Poland.\n\n\n**Table 2.** Cumulative changes in main indicators in 2023\n\n\n**SCENARIO 1** **SCENARIO 2** **SCENARIO 3** **SCENARIO 4**\n\n\n\n**Low employment**\n**conservative**\n**productivity**\n\n\n\n**High employment**\n**conservative**\n**productivity**\n\n\n\n**Low employment**\n**baseline**\n**productivity**\n\n\n\n**High employment**\n**baseline**\n**productivity**\n\n\n\nThe main impact of refugees is in growth\nof the economy. Refugees increase both\nsupply as workers and entrepreneurs as\nwell as demand as consumers. Increase\nin GDP is not directly proportional to the\nincrease in population or employment.\nNet benefits are lowered both due to\na decrease in capital to labour ratio as\nwell as an increase in competition on\nthe labour market. Moreover, increase in\ndemand in tight labour market conditions\nresults in higher inflation and lower\nprice competitiveness of Polish products\nwhich lower its overall positive impact.\nNevertheless, the positive impact of\nrefugees on the economy is significant in\nevery scenario considered.\n\n\nIn 2022 it amounts to real GDP\nbeing higher by 0.5-0.8% and in\n2023 cumulatively by 0.7-1.1%.\nThis corresponds to GDP being\nhigher by 24-36.9 billion PLN in\n2023 <sup>43</sup> .\n\n\nIn the long term, total potential GDP should\nbe higher by around 0.9-1.35% due to\nrefugees contributions. <sup>44</sup>\n\n\nOur results are consistent with the\nprevious, similar studies. In estimating\nGDP impacts we take an approach that", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:17:2:0", "start": 242, "end": 269, "surface": "data from Statistics Poland", "probe_tag": "keep", "probe_score": 0.9998, "luna_label": 1}]}, {"key": "aj-097", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Box 2.** Labour productivity of refugees in relation to the rest of population\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nTo gauge the labour productivity of Ukrainian refugees in relation to the rest of the population we **Chart 14.** Distribution of workers (refugees from Ukraine vs. natives and other immigrants) by average earnings in a poviat\nperformed back-of-the-envelope calculations. Unfortunately, publicly available data for the refugee and _Persons registered for social security on 30.09.2023_\ngeneral populations is gathered differently and for different time periods. Assuming that such results\n\nUkrainian refugees\n\nare broadly correct – educational attainment and geographical distribution imply higher earnings (a\nproxy for labour productivity) of refugees than the general population, while their employer firm sizes, 10%\n\n\n9%\n\n\n\nUkrainian refugees\n\n\n\n10%\n\n\n\n9%\n\n\n\n\n|Col1|Col2|Col3|Col4|Warszaw|a|Col7|Col8|\n|---|---|---|---|---|---|---|---|\n|||Nearly**10% of re**<br>work in Warsaw|**  fugees**|||||\n|||||||||\n||||Wrocław|||||\n|||||||||\n|||||Kraków||||\n|||Łodź|Poznań||||R² = 0,20|\n|||Szczecin<br>||Gdańsk||||\n||Poznański|Bydgoszcz||~~Katowice~~||||\n|||||Łęczy", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:16:0:0", "start": 522, "end": 545, "surface": "publicly available data", "probe_tag": "keep", "probe_score": 0.9864, "luna_label": 1}]}, {"key": "aj-098", "text": " banks) further strengthens\nthat effect. In total, general government\nrevenue increased by 2.0% in 2022, 2.75%\nin 2023, and 2.94% in 2024. In monetary\nterms this amounts to PLN 25.0 billion\nin 2022, PLN 39.1 billion in 2023, and\nPLN 47.0 billion in 2024 <sup>22</sup> . In the long term,\nrefugees should increase annual general\ngovernment revenue by around 2.7%.\n\n\n**Ultimately, Ukrainian refugees**\n**generate additional output and**\n**demand.** This results in an increase in real\nGDP, and is especially beneficial for public\nfinance. Although the influx of refugees\nwas costly at the start, the additional\ngeneral government revenue they provided\nwas more than enough to compensate\nfor the expense <sup>23</sup> . Tight labour market\nhelped absorb the increase in labour force,\nmitigating negative impacts of increased\ncompetition on the native workforce. Over\ntime, increased productivity should benefit\nnative workers, as it is the primary driver of\nlong-term wage growth. <sup>24</sup> [^24: For a discussion on the stable long-term relationship between wages and labour productivity, see for example Meager & Speckesser (2011).]\n\n\n\n22 Deloitte own calculations based on <u>Informacja kwartalna o stanie finansów publicznych - Ministerstwo Finansów - Portal Gov.pl for respective years. The general</u>\ngovernment income share in GDP in 2024 was calculated based on the data for Q1–Q3 and based on that total general government income was calculated using\nforecast for GDP from <u>Wytyczne dotyczące wskaźników makroekonomicznych - Ministerstwo Finansów - Portal Gov.pl.</u>\n\n23 Model treats general government sector as a whole, as such cost and income internal structure may differ creating institutions with financial loses while other may\nhave disproportionate increase of income.\n\n24 For", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:12:1:0", "start": 1180, "end": 1230, "surface": "Informacja kwartalna o stanie finansów publicznych", "probe_tag": "keep", "probe_score": 0.955, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:12:1:1", "start": 1376, "end": 1390, "surface": "data for Q1–Q3", "probe_tag": "keep", "probe_score": 0.9504, "luna_label": 1}]}, {"key": "aj-099", "text": " previous\nemployment background, compared to 50% of men. One possible explanation for this discrepancy is the higher\nproportion of men employed in sectors like construction and IT prior to displacement. These fields often\ndemand fewer country-specific qualifications, such as proficiency in the local language, thereby facilitating\neasier integration into similar roles in the host country.\n\n\n\n**<u>DISTRIBUTION OF WORKING AGE POPULATION BY</u>**\n**HIGHEST EDUCATION LEVEL ATTAINED**\n\n\nTechnical or Vocational Bachelor Master's Doctoral\n\n\n\n**<u>WAGE PREMIUMS FOR EDUCATION LEVEL GAINS: HOSTS</u>**\n**VERSUS REFUGEES, %**\n\n\n\nRefugee eduction\nwage premium\n(mean, 2024)\n\n\nBachelor's or\n\nabove\n\n\n\nHosts (2023)\n\n\nRefugees (2024)\n\n\nSource: Survey data, ILO\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1%\n\n\n\nHost education wage\npremium (median,\n2022)\n\n\nTechnical or\n\nvocational\n\n\n\nNote: Wage premiums computed with lower secondary education as\nthe baseline\n\n\nSource: Survey data, Eurostat, SAG estimates\n\n\n18. The difference in median wages by highest education level attained. Weighted equivalently to refugee weights for comparability\n19. The wage gap for refugees was computed based on the difference in weighted means (instead of the difference in medians)\n\n\n**13**", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000010:12:1:0", "start": 734, "end": 745, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9987, "luna_label": 1}]}, {"key": "aj-100", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n#### **Model calibration**\n\n\n\nWe introduce a positive productivity\nshock into the Deloitte D.Climate\nmodel, reflecting empirical findings that\nrefugee inflows coincided with stronger\nlabour-market outcomes, to offset any\npotential adverse effects. Although higher\nemployment rates among Ukrainian\nrefugees correlate with improved job and\nwage outcomes for Polish citizens, we take\na deliberately conservative approach in\nour general equilibrium simulations. While\ndescriptive statistics and econometric\nestimates point in a positive direction, the\navailable data remain too sparse for firm\n\n\n**Chart 31. Labour market activity rates**\n\nWomen in 15-64 age group\n\n\n\nconclusions. <sup>31</sup> [^31: 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.] On the other hand, a default\nDeloitte D.Climate model estimation is\nin line with the previously mentioned\ncanonical model. This yields a 1.35%\ndecrease in wages and a 0.4 percentage\npoint increase in the unemployment rate,\nwhich is implausible. Therefore, a positive\nmarginal productivity of labour shock has\nbeen added to the model and calibrated to\neliminate the impact on the unemployment\nrate throughout the simulation years of\n2022, 2023, and 2024. While it is possible\nthat the unemployment rate could fall\ndespite the influx of Ukrainian refugees due\n\n\n\nto factors other than productivity growth,\nthis is unlikely. Any negative impact could\nonly arise if other workers left the labour\nforce or reduced their working hours. No\nevidence of this is seen in recent Eurostat’s\nLabour Force Survey data for the Polish\neconomy, where activity rates continued\nto grow, while the average number of usual\nweekly hours worked in full- and parttime employment remained fairly stable,\nparticularly for women (who should be\ncloser substitutes for Ukrainian refugees,\nmost of whom are also women; see charts\nbelow).\n\n\n\nAnalysis of the impact of refugees from", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:21:0:0", "start": 1729, "end": 1764, "surface": "Eurostat’s\nLabour Force Survey data", "probe_tag": "keep", "probe_score": 0.9924, "luna_label": 1}]}, {"key": "aj-101", "text": " themselves.\nUkrainian refugees make up the largest\nshares of the population in the city of\nWroclaw (7.4%), Przemysl, a city on the\nUkrainian border (6.5%), and in Pruszkowski\npoviat, a suburban area of Warsaw (6.3%).\nThe city of Warsaw comes seventh with\nUkrainian refugees comprising 5.6% of the\nlocal population.\n\n\n\n**Ukrainian refugees in Poland continue**\n**to get their incomes primarily from**\n**work.** In the SEIS survey conducted in\nMay and June 2024, 80% of the refugee\nhouseholds’ incomes came from work,\nwhich included full-time and part-time\nwork, self-employment, remote work, and\nother forms of employment in Poland, as\nwell as remote employment in Ukraine.\nThis is the same as in the previous\nMSNA survey, conducted in July and\nAugust 2023 (see Deloitte, 2024), despite\n\n\n\nthe fact that the child benefit received\nby 42% of Ukrainian refugee households\nincreased in that time from PLN 500 to\nPLN 800 a month. For a vast majority of\nUkrainian household the „Family 800+”\nchild benefit was the only social benefit that\nthey received from the Polish government.\nOnly 5% of Ukrainian refugee households\nclaimed an accommodation allowance,\n4% – a disability grant and an even smaller\npercentage – other benefits.", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:5:2:0", "start": 418, "end": 429, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.9975, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:5:2:1", "start": 710, "end": 721, "surface": "MSNA survey", "probe_tag": "keep", "probe_score": 0.9784, "luna_label": 1}]}, {"key": "aj-102", "text": ".\nRegionally, the Ukraine refugee NEET rate tends to\nincrease with age, as it becomes more affected by\nunemployment, which is high for the 15 – 24 cohort\n(at 17%).\n\n\n\nOther\n\n\nNo longer\n\nemployed\n\n\nConstruction\n\n\nEducation\n\n\nAdministrative\n\nand\nsupport\n\nservice\nactivities\n\n\n\n16 Likely to be a bit overstated, as the survey did not inquire about activities of 15-year-olds outside of school enrollment. Also,\nsome respondents were not asked about distance learning in Moldova\n\n17 [Reference indicators were taken from the OECD dataset for 2022 with the exception of Moldova, where 2023](https://data.oecd.org/youthinac/youth-not-in-employment-education-or-training-neet.htm) <u>[data reported by](https://statistica.gov.md/en/youth-neet-in-the-republic-of-moldova-for-the-second-9430_60713.html)</u>\n<u>[the national statistics office](https://statistica.gov.md/en/youth-neet-in-the-republic-of-moldova-for-the-second-9430_60713.html)</u> was used\n\n\n**8**", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000000:7:1:0", "start": 521, "end": 533, "surface": "OECD dataset", "probe_tag": "keep", "probe_score": 0.9945, "luna_label": 1}]}, {"key": "aj-103", "text": "# Abstract\n\n\n\nThe beginning of the full-scale\nwar in Ukraine in February\n2022 resulted in a large outflow\nof refugees, reaching more\nthan 6 million globally.\n\n\nMuch of this exodus happened through the\nPolish border. As of October 2023, almost\n1 million Ukrainian refugees were living in\nPoland. While in the past decade Poland\nexperienced large labour migration from\nUkraine, the refugee inflow had a\ndifferent demographic composition.\nIt primarily included working age women\n(41%) and children (40%). These refugees\nfrom Ukraine did not plan to move, and\nmany had special needs. Despite these\ndifficulties, refugees began entering\nthe labour market surprisingly quickly –\nattaining an employment rate of 28% in\nMay 2022 and 65% in November 2022\n(NBP, 2023). By July-August 2023 Ukrainian\nrefugee households supported themselves,\nwith 80% of their incomes coming from work. <sup>1</sup> [^1: Deloitte calculations based on Multi-Sector Needs Assessment Poland 2023 survey data provided by UNHCR.]\n\n\nWe find that refugees from Ukraine who\nremain in Poland as workers, entrepreneurs,\nconsumers, and taxpayers have a positive\nimpact on economic output, which will\nincrease in the long run. Results of our\ngeneral equilibrium Deloitte D.Climate\nmodel show that refugees from Ukraine\ncontributed 0.7-1.1% to the Gross Domestic\nProduct in 2023. In the long-term this effect\nwill grow to 0.9-1.35%. In our model, the\n\n\n\nlong-term is defined as the period over\nwhich the economy fully adjusts to the\nshock of the initial refugee inflow; it does not\ninclude other aspects, e.g. refugee children\ngrowing-up and entering employment.\nThese results are consistent with previous,\nsimilar studies. However, they should be\ntreated as lower-bound estimates, as we do\nnot allow for the possibility of an increase\nin the labour force triggering a positive\nproductivity shock (e.g., due to increased\nspecialisation), because there is little data to\ncredibly estimate its size.\n\n\nA feature of our modelling approach is", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:2:0:0", "start": 923, "end": 976, "surface": "Multi-Sector Needs Assessment Poland 2023 survey data", "probe_tag": "keep", "probe_score": 0.9991, "luna_label": 1}]}, {"key": "aj-104", "text": " enough money to\nmeet their needs, the refugees were nearly\nequally split (around 0.5% more reported\nno difficulties), with an additional 8% not\nknowing or refusing to answer. According to\nthe National Bank of Poland survey carried\nout in November 2022, 28% of refugees\nsaid they spend less than half of their\nincome on daily expenses, most spend\nbetween 50% and 80%, and 19% spend 80100% of their income.\n\n\n###### Currently between 225 and 350 thousand of refugees from Ukraine are working in Poland. The lower bound is the number from social security, while the higher bound is the product of employment rate from the surveys and working age population with PESEL numbers.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Box 1.** Inflow of savings from Ukraine\n\n\n\n**Chart 13.** Income structure of Ukrainian refugee households\n\n\n**5%** **1%**\n\n\n\nWork (regular-, temporary-,and self-employment in Poland,\nremote employment in Ukraine\n\n\nRemittances from friends/relatives\n\n\nPolish government benefits (Family 500+, cash benefits,\ndisability grants)\n\n\nUkrainian government benefits (pensions, disability grants,\nparental benefits)\n\n\nOther (humanitarian organizations, other sources)\n\n\n\n**Source:** Deloitte elaboration based on the MSNA Poland 2023. The work category includes regular employment, temporary work, self-employment\nand remote work in Ukraine.\n\n\n31 <u>[Cudzoziemcy w polskim systemie ubezpieczeń społecznych (zus.pl)](https://www.zus.pl/documents/10182/2322024/Cudzoziemcy+w+polskim+systemie+ubezpiecze%C5%84+spo%C5%82ecznych_2022.pdf/)</", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:14:2:0", "start": 193, "end": 223, "surface": "National Bank of Poland survey", "probe_tag": "keep", "probe_score": 0.9996, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:14:2:1", "start": 620, "end": 673, "surface": "surveys and working age population with PESEL numbers", "probe_tag": "keep", "probe_score": 0.9248, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:14:2:2", "start": 1263, "end": 1279, "surface": "MSNA Poland 2023", "probe_tag": "keep", "probe_score": 0.9021, "luna_label": 1}]}, {"key": "aj-105", "text": " in green. n=681, age individuals aged 18-64.\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey.\n\n\n\nFluent\n\n\n\nAdvanced\n\n\n\nIntermediate\n\n\n\nNone\n\n\n\nBeginner\n\n\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey.\n\n\n**Chart 24. What Ukrainian refugee groups have weakest Polish language fluency?**\n\nOdds ratio of Ukrainian refugees intermediate and below knowledge of Polish language\nLogistic regression model\n\n\n2.38\n\n\n\n\n\n\n\n\n\n**Ukrainian refugees visibly improve**\n**their Polish language fluency over**\n**time.** In the SEIS UNHCR survey, on\naverage, Ukrainian refugees who said\nthey were fluent in Polish had stayed in\nPoland for 29 months, while those with an\nintermediate level – for only 22 months.\nThe results are interesting, especially the\nfact that the average time required to\n\n\n32\n\n\n\n33\n\n\n\nprogress from advanced to fluent language\nlevels was longer than from intermediate\nto advanced, or beginner to intermediate\n(although not from zero to beginner). It\nis likely that the highest level of fluency,\nwhich may be required in some of the most\nattractive occupations, is also the hardest\nto achieve, and such language courses are\nnot as readily available.", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:16:3:0", "start": 89, "end": 106, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9995, "luna_label": 1}]}, {"key": "aj-106", "text": "sup>9</sup> . This figure is almost double that of host country nationals (12%), implying a\nlarge gap in economic vulnerability. Compared to 2023, poverty rates have decreased substantially (from\n36% <sup>10</sup> ), suggesting an overall improvement in the economic well-being of Ukrainian refugees over time.\n\n\n**<u>REFUGEE VERSUS HOST POVERTY RATES BY COUNTRY</u>**\n\n\nUkrainian refugees (2024) Host country nationals (2023)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Hungary Moldova Poland Romania Slovakia Estonia Latvia Lithuania Region\n\n\nNote: Poverty rates for all countries apart from the Republic of Moldova are based on a calculation that follows Eurostat’s at-risk-of-poverty (AROP)\nmethodology with the at-risk-of-poverty threshold set at 50% of the national median disposable income after social transfers. Refugee disposable\nincome has been computed based on survey data. For the Republic of Moldova, the poverty threshold was taken to be the 4Q23 absolute poverty line\nreported by the National Bureau of Statistics of Moldova.\n\n\nSource: Survey data, <u>[Eurostat,](https://ec.europa.eu/eurostat)</u> <u>[National Bureau of Statistics of Moldova, SAG estimates](https://statistica.gov.md/en)</u>\n\n\n7. The MSNA, which ran in 7 countries: Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and Slovakia\n8. Equivalized as per <u>[Eurostat methodology. Essentially income per person, but with household members beyond the first one](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Equivalised_income)</u>\nassigned weights less than", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000010:3:1:0", "start": 872, "end": 883, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9577, "luna_label": 1}]}, {"key": "aj-107", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 15. Ukrainian refugees wages median net wage by age group**\n\n\nMonthly net wage (PLN) Percengate of all workers total economy average\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 16. Median net wages of Ukrainian refugees median net wage by sector**\n\n\nin PLN Percentage of total economy average\n\n\n\n126%\n\n\n\n\n\n\n\n\n\n\n\n15 to 24 25 to 34 35 to 44 45 to 54 55 to 64 15 to 24 25 to 34 35 to 44 45 to 54 55 to 64\n\n\nUkrainian refugees All workers\n\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey and GUS data.\n\n\n\n**The Ukrainian refugee groups to earn**\n**the highest wages compared to the**\n**wages in the economy as a whole are**\n**the younger age groups.** Ukrainian\nrefugee incomes from employment are\nhighest in the 25–34 and 35–44 age\ngroups. However, relative to the economy\nas a whole, the wages of Ukrainian\nrefugees are the highest in the youngest\nage groups and decrease with age. This\ncan be explained by three facts. First,\nwages in the youngest age groups are\nmost compressed, because they diverge\nwith time and accumulated professional\nexperience. Second, younger persons have\nless experience and so lose least from\nmigration. Third, younger persons often\nfind it easier to learn the language of the\nhost country.\n\n\n\n**The earnings of Ukrainian refugees**\n**differ across sectors in nominal**\n**terms and as a percentage of the**\n**economy as a whole – only education**\n**ranked lowest on both measures.**\nUkrainian refugees earn the highest\nwages in manufacturing, health, and\naccommodation and food service activities,\nwhile the lowest in education, other\nservices, and construction. A comparison\nto median earnings in these sectors in the\neconomy as a whole (after re", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:10:0:0", "start": 604, "end": 621, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9906, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:10:0:1", "start": 626, "end": 634, "surface": "GUS data", "probe_tag": "keep", "probe_score": 0.9429, "luna_label": 1}]}, {"key": "aj-108", "text": ", 2024, Ukrainian refugees\ngained an estimated 7% in earnings having\nshifted towards better paid occupations,\npre-war Ukrainians 5%, non-Ukrainian\nforeigners 4%, and Polish citizens 1%.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**Ukrainian refugees have been slowly**\n**closing their wage gap to Polish**\n**citizens across all wage levels.** Once\nZUS administrative data on Ukrainian\nrefugees with employment contracts is\ndivided into employee cells based on\n380 poviats, 2 sexes, 7 age groups, and\n10 main occupational groups (including\n\n\n\nunallocated), the gap in social contributions\nbases towards Polish citizens narrows.\nThe largest group (16%) is positioned\nbetween 90% and 100% of Polish citizens\n(median 93%). This is an improvement over\ntwo years, when the majority (13%) situated\nbetween only 80% and 90% (median 82%). <sup>12</sup>\n\n\n\n12 Unfortunately, the data is available in a format that is not suitable for econometric modelling and thus these are only comparisons between thousands of employee\ncells and average social contributions bases.", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:8:1:0", "start": 368, "end": 391, "surface": "ZUS administrative data", "probe_tag": "keep", "probe_score": 0.9468, "luna_label": 1}]}, {"key": "aj-109", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n\n### **Child health,** **vaccination and** **nutrition**\n\n\n\n\n\n\n\n\n\nTimely initiation\nof breastfeeding\n(within one hour\n<u>of delivery)</u>\n\nExclusive\nbreastfeeding\n<u>under 6 months</u>\n\n\n\n0-23\n153/274 57%\nmonths\n\n\n0-5\n15/38 37%\nmonths\n\n\n\n\n- Excluding Estonia, Hungary\n\n\nBreastfeeding practices for infants and young\nchildren directly influence their nutritional health\nduring the first two years of life and play a crucial\nrole in child survival. From the survey results, the\nproportion of children 0-23 months who had timely\ninitiation of breastfeeding was 57% and the rate of\nexclusive breastfeeding for the first six months of\nlife was 37% in the region. Data need to be\ninterpreted with caution given the very low number\nof respondents.\n\n\nTwo doses of measles vaccine are recommended\nfor optimal protection against measles; the survey\nassessed therefore first and second dose measles\nvaccination coverage in children aged 9 months to\n5 years. In average, 83% of children received at\nleast one measles vaccine, similar to results from\n2023 when 84% of children had received at least\none dose. Coverage was lowest in Romania with\n71% where respondents reported also greater\n\n\n\nconstraints in accessing health services. Vaccine\ncoverage increased notably in Moldova, Czechia\nand Bulgaria compared to 2023. In comparison,\nmeasles vaccination coverage within Ukraine\nreached 92% for the 1st dose of measles vaccine\nand 87% for second dose <sup>8</sup> (WHO, 2023).\n\n\nRegionally, only 54% of all children received the\nrecommended second measles vaccine.\nVaccination coverage is below the 95% target\nrequired to interrupt community transmission of\nmeasles.\n\n\n**<u>% OF CHILDREN RECEIVED AT LEAST ONE MEASLES</u>**\n**VACCINE**\n\n\n2023 2024\n\n\n\nData need to be interpreted", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000004:17:0:0", "start": 527, "end": 541, "surface": "survey results", "probe_tag": "keep", "probe_score": 0.993, "luna_label": 1}]}, {"key": "aj-110", "text": " have relatively better labour\nmarket situations. In countries with lower\nunemployment, refugees fare better in\nthe labour market. Since women make\nup the majority of refugees of working\nage, the situation of women on the labour\nmarket is especially important. As such,\nfemale unemployment rates explain 36%\nof the variation in refugees from Ukraine\nemployment rates in studies from 11 EU\nMember States <sup>22</sup> [^22: After excluding Germany and Switzerland as outliers. 23  Due to possible differences in methodologies data from this surveys should not be directly compared]\n\n\n**Chart 10.** Female unemployment and refugees from Ukraine employment in Europe\n\n\n70%\n\n\n\n**Chart 11.** Education attainment of Poles and Ukrainians\n\n\n**Eurostat** Poland LFS 2022\n\n\n\n**UNHCR**\n\n**(2023)**\n\n\n**NBP**\n**(2022)**\n\n\n**Ukrstat**\n\n\n\n11%\n\n\n56%\n\n\n48%\n\n\n46%\n\n\n\n30%\n\n\n\nRefugees from Ukraine VII-VIII 2023\n\n\nRefugees from Ukraine XI 2022\n\n\nPre-2022 migrants from Ukraine XI 2022\n\n\nUkraine LFS 2020\n\n\n\n29%\n\n\n20%\n\n\n17%\n\n\n29%\n\n\n\n60%\n\n\n14%\n\n\n33%\n\n\n37%\n\n\n\n37%\n\n\n\n60%\n\n\n50%\n\n\n40%\n\n\n30%\n\n\n20%\n\n\n10%\n\n\n0%\n\n\n\n\n|PL XI<br>PL VII-VIII|2022<br>2023 U|K**|Col4|Col5|\n|---|---|---|---|---|\n||~~CZ~~<br> <br>|LT<br>|SE||\n|||DK<br>NL||EE|\n||||FR|**R = 0,36**|\n|||IE<br>|||\n||DE<br>|~~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", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:10:1:0", "start": 747, "end": 762, "surface": "Poland LFS 2022", "probe_tag": "keep", "probe_score": 0.9924, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:10:1:1", "start": 792, "end": 795, "surface": "NBP", "probe_tag": "keep", "probe_score": 0.9496, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:10:1:2", "start": 813, "end": 820, "surface": "Ukrstat", "probe_tag": "keep", "probe_score": 0.9496, "luna_label": 1}]}, {"key": "aj-111", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**<u>HOUSEHOLD INCOME DISTRIBUTION BY SOURCE AND POVERTY CATEGORY</u>**\n\n\n\nEmployment Family support Social\nprotection\n(host country)\n\n\n\nOld age\npension\n(Ukraine)\n\n\n12%\n\n\n67%\n\n\n\nSocial\nprotection\n(Ukraine)\n\n\n35%\n\n\n\nHumanitarian\ncash\n\n\n8%\n\n\n\n13%\n\n\n\n23%\n\n\n\n6%\n\n\n\nOther\n\n\n5%\n\n\n3%\n\n\n\n3%\n\n\n5%\n\n\n\nBelow the poverty line\n\n\nBelow the poverty line after housing\n\ncost correction\n\n\nAbove the poverty line after housing\n\ncost correction\n\n\nSource: Survey data, SAG estimates\n\n\n\n26%\n\n\n\n88%\n\n\n\n**Higher employment earnings were the main driver behind the drop in poverty rates from**\n**2023**\n\n\nThe mean monthly equivalized household income <sup>12</sup> [^12: Household disposable income adjusted for size, as per Eurostat’s methodology] of Ukrainian refugees across the seven countries surveyed\nin both rounds increased by 38% from last year, to an equivalent of EUR 763. This increase was much higher\nthan the 4% rise in the regional poverty threshold over the same time.\n\n\nApproximately 90% of the increase in refugee household income can be attributed to higher employment\nearnings, driven by a combination of rising employment rates and wage growth. A significantly smaller, though\nstill notable, contribution came from increased financial support from families in Ukraine, although this finding\nmay partially be an artifact of adjustments to the survey questionnaire <sup>13</sup> .\n\n\n**<u>WEIGHTED AVERAGE MONTHLY EQUIVALIZED INCOME EVOLUTION, EUR</u>**\n\n\n800\n\n\n600\n\n\n400\n\n\n200\n\n\n\n0\n\n\n\nFamily\nsupport\n\n\n\nHost country\n\nsocial\nprotection\n\n\n\nUkraine\n\nsocial\nprotection\n\n(inc.\npensions)\n\n\n\nHumanitarian\n\ncash\n\n\n\nEquivalized\n\n\n\nEmployment\n\nincome\n\n\n\nOther Equivalized\n\n\n\nincome\n\n\n\n2024\n\n\n\nincome\n\n\n\n2023\n\n\n\nNote: Only includes data from the 7 countries surveyed in both rounds (Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and\nSlovakia)\n\n\nSource", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000010:8:0:0", "start": 544, "end": 555, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9981, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:8:0:1", "start": 1824, "end": 1873, "surface": "data from the 7 countries surveyed in both rounds", "probe_tag": "keep", "probe_score": 0.9846, "luna_label": 1}]}, {"key": "aj-112", "text": "org/en/documents/details/104427 )</u>\n\n\nUrban M. (2022). Refugees will lift economy's potential, but challenges remain,\nResearch Briefing | Poland. Oxford Economics, <u>[https://www.oxfordeconomics.](https://www.oxfordeconomics.com/wp-content/uploads/2022/05/Poland-Refugees-will-lift-economys-potential-but-challenges-remain.pdf)</u>\n<u>[com/wp-content/uploads/2022/05/Poland-Refugees-will-lift-economys-](https://www.oxfordeconomics.com/wp-content/uploads/2022/05/Poland-Refugees-will-lift-economys-potential-but-challenges-remain.pdf)</u>\n<u>[potential-but-challenges-remain.pdf](https://www.oxfordeconomics.com/wp-content/uploads/2022/05/Poland-Refugees-will-lift-economys-potential-but-challenges-remain.pdf)</u>\n\n\n45\n\n\n\nD’Amuri, F., & Peri, G. (2014). Immigration, jobs, and employment protection:\nevidence from Europe before and during the great recession. Journal of the\nEuropean Economic Association, 12(2), 432-464.\n\n\nDeloitte (2023), Ukraine Refugee Pulse report, <u>[https://www2.deloitte.com/pl/pl/](https://www2.deloitte.com/pl/pl/pages/zarzadzania-procesami-i-strategiczne/articles/Ukraine-Refugee-Pulse-report", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:22:3:0", "start": 945, "end": 973, "surface": "Ukraine Refugee Pulse report", "probe_tag": "confusion", "probe_score": 0.0902, "luna_label": 0}]}, {"key": "aj-113", "text": " <u>https://data2.unhcr.org/en/situations/ukraine</u>\n\n2 PESEL UKR is a version of Polish national ID number for Ukrainian citizens in connection with the armed conflict in the territory of that country.\n\n\n06\n\n\n\n**Chart 2. Ukrainians registered for social insurance**\n\n\nNote: Refugees are identified by PESEL UKR status.\n\nSource: Deloitte own elaboration based on ZUS data.\n\n\n\nUkrainian refugees Pre-war Ukrainians\n\n\n\n07", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "jad_paddy_docs:000001:3:2:0", "start": 364, "end": 372, "surface": "ZUS data", "probe_tag": "confusion", "probe_score": 0.6506, "luna_label": 1}]}, {"key": "aj-114", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\n**Table 1.** Ukrainian employment rate in EU.\n\n\n**Employment rate** **Main sectors of employment** **Date** **Source**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**Source:** Deloitte elaboration based on the aggregation in OECD International Migration Outlook 2023. The calculation methods of employment\nrates vary between the countries, the results for Poland, the United Kingdom, Czech Republic and Italy are based on surveys. Additionally, the\ndifferent statistical offices and organisations may use different employment and working age definitions.\n\n\n\nCurrently, DIW Berlin data shows that the\nemployment of refugees from Ukraine is\nmuch lower in Germany, although many\naspire to find employment. In a weekly\nreport from 12th July 2023 the German\nInstitute for Economic Research (DIW\nBerlin), a survey conducted in the first\nquarter of 2023 finds that only 18% of\nworking age refugees were employed\n(Kollman, 2023). Among the non-employed,\nhowever, as many as 93% declare\nwillingness to begin working, of which\n71% as soon as possible or within a year.\nImportantly, many refugees are currently\nattending language courses in Germany\n\n- 65% in early 2023 with a further 10%\nalready having completed a course. 87%\nalso took part in inclusion courses that\nwere offered. Per the report, since the\nbeginning of the full-scale war,, around one\nmillion people have fled to Germany, while\nEurostat data on beneficiaries of temporary\nprotection show that there were around 1.17\nmillion Ukrainians under such schemes in\nGermany <sup>30</sup> [^30: Eurostat data, <u>https://ec.europa.eu/eurostat/databrowser/view/migr_asytpsm/default/table?lang=en</u>] .\n\n\nAmong working refugees there is a\nsignificant number of entrepreneurs.\nThe high employment rate of refugees in\nPoland covers not", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:13:0:0", "start": 344, "end": 385, "surface": "OECD International Migration Outlook 2023", "probe_tag": "keep", "probe_score": 0.9999, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:13:0:1", "start": 688, "end": 703, "surface": "DIW Berlin data", "probe_tag": "keep", "probe_score": 0.9881, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:13:0:2", "start": 920, "end": 965, "surface": "survey conducted in the first\nquarter of 2023", "probe_tag": "keep", "probe_score": 0.9396, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:13:0:3", "start": 1505, "end": 1559, "surface": "Eurostat data on beneficiaries of temporary\nprotection", "probe_tag": "confusion", "probe_score": 0.1036, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:13:0:4", "start": 1662, "end": 1675, "surface": "Eurostat data", "probe_tag": "keep", "probe_score": 0.9991, "luna_label": 1}]}, {"key": "aj-115", "text": "# **HIGH EMPLOYMENT** **RATES, BUT LOW** **WAGES: A POVERTY** **ASSESSMENT OF** **UKRAINIAN REFUGEES** **IN NEIGHBORING** **COUNTRIES**\n## **An inter-agency exploration of** **socio-economic data** March 2025", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000010:15:0:0", "start": 176, "end": 195, "surface": "socio-economic data", "probe_tag": "confusion", "probe_score": 0.3146, "luna_label": 0}]}, {"key": "aj-116", "text": " and highest incomes.\nDetails are available in the Online Technical Appendix.\n\n27 Note that the GUS (2024) data for the host population is not exactly comparable, as it does not include microenterprises, it covers an earlier period and focuses on\ngross and average wages.\n\n\n28\n\n\n\n28 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\n29", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:14:4:0", "start": 96, "end": 111, "surface": "GUS (2024) data", "probe_tag": "confusion", "probe_score": 0.2908, "luna_label": 1}]}, {"key": "aj-117", "text": "## Introduction\n\n##### Nearly two years after the beginning of the full-scale war in Ukraine, the positive impact of the refugees on the Polish economy becomes clearly visible.\n\n\n\nOn their arrival, the impact of refugees\non the economy primarily manifested\nthrough higher consumption, that was\nfinanced mainly by increased governmental\nspending, civil society, international\norganisations, and savings brought\nfrom Ukraine. While these added to a\nsignificant initial increase in consumption,\nmultiple sources of financing and a lack of\naggregated data (particularly on general\ngovernment expenses) make its magnitude\nuncertain. Although in the short term it\nstimulated the economy, drawing just on\nsavings was not sustainable. Over time,\nhowever, refugees started to work as\nemployees and entrepreneurs, adding\nnot only to the demand, but also to the\nsupply of the economy, contributing to its\nlong-term growth. The focus of this report\nis this structural impact of refugees on\nthe economy as consumers, employers,\nentrepreneurs, and taxpayers.\n\n\nThe beginning of the full-scale war in\nUkraine resulted in a large inflow of\nrefugees into Poland outlined in Chapter 1.\nThis cohort differs from the pre-2022\nUkrainian economic migrants, most notably\nin its demographic makeup which primarily\ncomprises children and working age women.\n\n\nThe government quickly granted refugees\nfrom Ukraine access to the labour market,\nhealthcare, and schooling, facilitating the\nprocess of inclusion described in Chapter\n2. Considering their psychological stress\nand needs in terms of child and elderly\ncare, refugees began entering the labour\n\n\n\nmarket surprisingly quickly – attaining\nan employment rate of 28% in May 2022\nand 65% in November 2022 (NBP, 2023).\nBy 30th September 2023 more than\n10 thousand ran their own businesses\naccording to the administrative social\nsecurity ZUS data. Based on the MultiSector Needs Assessment Poland 2023\nsurvey conducted in July-August 2023, we\ncalculate that 80% of the income of refugee\nhouseholds is derived from employment,\nwith an additional 5% coming from\nremittances and 2% from Ukrainian pension", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:5:0:0", "start": 536, "end": 551, "surface": "aggregated data", "probe_tag": "confusion", "probe_score": 0.612, "luna_label": 0}, {"key": "sample:jad_paddy_docs:000007:5:0:1", "start": 1831, "end": 1870, "surface": "administrative social\nsecurity ZUS data", "probe_tag": "keep", "probe_score": 0.9935, "luna_label": 1}]}, {"key": "aj-118", "text": "s fight for its people. Strategies for refugee and\ndiaspora engagement, Ukraine Forum, Chatham House, February.\n\n\nUNHCR (2023). Poland: Multi-Sector Needs Assessment — Results Overview\n(MSNA 2023), October, <u>https://data.unhcr.org/fr/documents/details/104427</u>\n\n\nUNHCR (2025a). Poland: Socio-Economic Insights Survey in Poland - Results\n\n\n\nAnalysis (SEIS 2024). UNHCR, October, <u>https://data.unhcr.org/en/documents/</u>\n<u>details/115045</u>\n\n\nUNHCR (2025b). High employment rates, but low wages: a poverty assessment\nof Ukrainian refugees in neighboring countries, Regional Refugee Response for\nthe Ukraine Situation, Regional Bureau for Europe, UNHCR.\n\n\nUNHCR (2025c). Ukraine Multi-year Strategy 2025 – 2027, UNHCR, November.\n\n\nUrban M. (2022). Refugees will lift economy's potential, but challenges remain,\nResearch Briefing | Poland. Oxford Economics, <u>https://www.oxfordeconomics.</u>\n<u>com/resource/refugees-in-poland-will-lift-economys-potential-but-challenges-</u>\n<u>remain/</u>\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n47", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:23:5:0", "start": 290, "end": 330, "surface": "Socio-Economic Insights Survey in Poland", "probe_tag": "confusion", "probe_score": 0.0851, "luna_label": 0}]}, {"key": "aj-119", "text": "HELPING HANDS\nTHE ROLE OF \nHOUSING \nSUPPORT AND \nEMPLOYMENT \nFACILITATION IN \nECONOMIC \nVULNERABILITY \nOF REFUGEES \nFROM UKRAINE\nAn inter-agency \nexploration of socio-\neconomic data\nApril 2024", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000000:0:0:0", "start": 161, "end": 181, "surface": "socio-\neconomic data", "probe_tag": "confusion", "probe_score": 0.4913, "luna_label": 0}]}, {"key": "aj-120", "text": "’s\nRegulated Professions Database. This can\nbe a problem, as occupational licensing is\ncited in the literature among the reasons\nfor occupational downgrading of migrants.\nCassidy and Dacass (2021) found that in the\nUnited States, immigrants were significantly\nless likely to have a license than similar\n\n\n\n**Chart 21. Share of regulated professions by citizenship and legal status, Q2 2024**\n\n\n\n**The educational premium seems**\n**to be lower for Ukrainian refugees**\n**compared to the general workforce**\n**in Poland.** According to the SEIS survey,\nUkrainian refugees with master’s and\nPhD degrees earn a 22% higher median\nnet wage than those with only secondary\neducation. This appears to be a small\ngain, even accounting for the fact that,\ngenerally, the differences between median\nwages are less pronounced than between\naverage wages (which are pulled higher\nby top incomes) and that our method of\nwage estimation based on SEIS household\nincomes <sup>26</sup> [^26: 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.] flattens the distribution.\nAccording to the most recent estimate in\nOctober 2022, in the economy as a whole,\nthe average gross wage of master’s and\nPhD degree holders was 84% higher than\nthose with only secondary education. <sup>27</sup> [^27: Note that the GUS (2024) data for the host population is not exactly comparable, as it does not include microenterprises, it covers an earlier period and focuses on\ngross and average wages.]\n\n\n\n**Most Ukrainian refugees work in**\n**a different sector than previously**\n**in Ukraine, which also points to**\n**occupational downgrading.** The SEIS\nsurvey indicates that 34% of Ukrainian\nrefugees currently employed in Poland\nwork in the same sector", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:14:2:0", "start": 538, "end": 549, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.9814, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:14:2:1", "start": 928, "end": 950, "surface": "SEIS household\nincomes", "probe_tag": "confusion", "probe_score": 0.8237, "luna_label": 1}]}, {"key": "aj-121", "text": " stronger increase in\nlabour productivity than was assumed. As\na result, the positive impact of Ukrainian\nrefugees on the economy is greater than\npreviously expected.\n\n\n\n**As Ukrainian refugees entered the**\n**labour market, the economy adapted,**\n**resulting in more specialization and**\n**higher productivity.** In a simplistic\nsupply-demand framework, the influx\nof Ukrainian refugees should have\ncaused some Polish workers to become\nunemployed or leave the labour force, or\nreal wages to fall. This has not happened.\nFirst, among Polish citizens employment\nrates have grown, and unemployment\nrates have fallen. Second, poviats in which\nemployment share of Ukrainian refugees\nhas grown by 1 pp., saw 0.5 pp. higher\nemployment rates among Polish citizens,\nand 0.3 pp. lower unemployment rates.\n\n\n\nThird, there is no evidence of lowered\nwages, in fact the limited available data\nsuggests that Ukrainian refugees may have\ncaused higher wage growth in poviats\nwhich they have moved to. These are\ncommon findings well documented in\nacademic literature, that as immigrants\nenter the labour market, native workers\nspecialize in complementary, higher\nvalue tasks, which we see empirically in\nPolish workers moving to more attractive\noccupational groups. This can be seen in\nthe data, as Polish citizens are moving to\nbetter paid occupations. It constitutes a\npositive shock to productivity which is what\ncounterbalances labour market pressures. <sup>17</sup> [^17: For the literature review, underlying empirical evidence, and model calibration refer to the appendix on modelling strategy.]\n\n\n\nSource: Deloitte D.Climate estimates. For details see\nthe Online Technical Appendix.\n\n\n**The main impact of Ukrainian refugees**\n**is expanding the economy and putting**\n**it on a higher growth path.** According\nto the Deloitte D.Climate model, economic\nimpact of Ukrainian refugees amounted\nto a higher real GDP by 1.5% in 2022, as\nthey initially entered the labour market.\nWith more", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:11:2:0", "start": 857, "end": 879, "surface": "limited available data", "probe_tag": "confusion", "probe_score": 0.0747, "luna_label": 1}]}, {"key": "aj-122", "text": "THE ROLE OF HOUSING SUPPORT AND EMPLOYMENT FACILITATION IN ECONOMIC VULNERABILITY OF REFUGEES FROM UKRAINE\n\n\n### **Background**\n\nOver two years have elapsed since the start of the\nfull-scale war in Ukraine leading to a protracted\ndisplacement and a humanitarian crisis. The\nresponse by the refugee-hosting countries\ncontinues to be overall characterized by a spirit of\nwelcomeness and generosity and much has been\ndone to make ensure that those fleeing the war are\nable to meet basic needs and have access to\naccommodation, healthcare, education, social\nassistance, and employment. Despite these efforts,\nthe situation remains a source of deep concern,\nnecessitating a continued and coordinated\nhumanitarian response at the regional level.\n\n\nAs of the end of 2023, 5.9 million refugees from\nUkraine were recorded across Europe, close to 2\nmillion of whom are in the countries covered by the\n<u>[Regional Refugee Response Plan (RRP)](https://data.unhcr.org/en/documents/details/105903)</u> <sup>5</sup> [^5: Belarus, Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, the Republic of Moldova, Poland, Romania, and Slovakia] . To better\nunderstand their evolving situation, unpack risks\nand vulnerabilities and inform planning across\nsectors, Multi-Sectoral Needs Assessments (MSNA)\nwere conducted under the RRP between June and\nSeptember 2023 by UNHCR’s Regional Bureau for\nEurope and its Inter-Agency partners. This\npublication focuses on the results for livelihoods\nand socio-economic inclusion and attempts to draw\nconclusions based on survey data of 11,496\nhouseholds (and 26,857 individuals) living in\nBulgaria, the Czech Republic, Hungary, the Republic\nof Moldova, Poland, Romania, and Slovakia.\n\n\n### **Socio-economic** **inclusion – key** **findings**\n\n**Refugee households demonstrate a high degree**\n**of economic vulnerability**\nThe MSNA survey data demonstrates that refugee", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000000:3:0:0", "start": 1550, "end": 1561, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.7979, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000000:3:0:1", "start": 1855, "end": 1871, "surface": "MSNA survey data", "probe_tag": "confusion", "probe_score": 0.5615, "luna_label": 1}]}, {"key": "aj-123", "text": " 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 database as of 10th October\n2023. According to the registry, 63.7%\nof them are women and 36.3% are men.\nThe database also includes the age of the\nUkrainian PESEL holders, which indicates\nthat over half (56%) are of working age\n(18-64), which amounts to more than 536\nthousand people. More precisely, women\nyounger than 18 make up 19% of the\npopulation, women aged 18-64 account\nfor around 41% of the population, while\nthose aged 65 and above are around 3%\nof total population. The incoming men are\nmostly young, those younger than 18 stand\nfor around 20% of all refugees, men aged\n18-64 make up 15% of the population and\nmen older than 64 account for only 1%.\n\n\nMany of the refugees that settled in Poland\nremain in special needs or otherwise\nprecarious households. According to the\nMSNA Poland 2023 survey, nearly half of all\nrefugee households have a person with a\nchronic illness, while in nearly 10% there is\na disabled person (Washington Group level\n3 disability). In over a third of all households\nis a single parent and over a fifth houses\nan elderly person (10% of households are\ncomprised of exclusively elderly people).\n\n\n\n\n\n\n\n\n\n\n\n\n\nhouseholds with\n\npersons with\n\nchronic ilness\n\n\n\n\n\n\n\nhouseholds\n\nwith a single\n\nparent\n\n\n\nhouseholds with\n\none or more\n\nolder persons\n\n\n\nhouseholds\n\nwith disabled\n\nindividuals\n\n\n\nhouseholds\n\nexclusively\n\nwith elderly\n\n\n\n18  UNHCR data, <u>[https://data2.unhcr.org/en/situations/ukraine](https://data2.unhcr.org/en/situations/ukraine)</u>\n\n\n14\n\n\n\n**Source:*", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "jad_paddy_docs:000007:7:2:0", "start": 58, "end": 68, "surface": "PESEL data", "probe_tag": "keep", "probe_score": 0.9593, "luna_label": 1}, {"key": "jad_paddy_docs:000007:7:2:1", "start": 271, "end": 285, "surface": "PESEL database", "probe_tag": "keep", "probe_score": 0.9804, "luna_label": 1}, {"key": "jad_paddy_docs:000007:7:2:2", "start": 1060, "end": 1083, "surface": "MSNA Poland 2023 survey", "probe_tag": "keep", "probe_score": 0.9526, "luna_label": 1}, {"key": "jad_paddy_docs:000007:7:2:3", "start": 1644, "end": 1654, "surface": "UNHCR data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "aj-124", "text": " circular migration, with Ukrainians\ncoming to Poland for half of the year,\nthen returning to Ukraine for another six\nmonths, and coming back to Poland. The\ndata on employers’ declarations do not\nreveal the actual number of Ukrainian\ncitizens who followed this system – a single\nperson could hold several declarations,\nbecause with every change of employer\nor promotion at the same employer they\nhad to ask for a new declaration. What is\nmore, with stays shorter than one year,\n\n\n\nthose people fell outside the definitions\nof population used by Statistics Poland\n(Główny Urząd Statystyczny, GUS,\nPoland’s statistical office). The National\nBank of Poland estimated that between\n2014 and 2018, there were approximately\none to two million Ukrainian workers\nin Poland at a time (Strzelecki, Growiec\nand Wyszyński, 2022). According to the\n2021 Polish National Census, one year\nbefore the outbreak of the full-scale war\nin Ukraine there were about one million\nUkrainian citizens residing in Poland,\nalmost all of them on a temporary basis.\n\n\n\nAfter the full-scale Russian invasion of\nUkraine, Poland experienced a massive\ninflux of refugees – with more than\n27 million border crossings from Ukraine\nand more than 1.9 million applications for\nprotection submitted by April 7, 2025 <sup>1</sup> . Not\nall of those people stayed in Poland. Many\n\n\n\nof them later returned to Ukraine or moved\nto other countries. By February 14, 2025,\nPESEL UKR <sup>2</sup> [^2: PESEL UKR is a version of Polish national ID number for Ukrainian citizens in connection with the armed conflict in the territory of that country.] holders who remained\nin Poland stood at less than 1 million,\nwhile the border movement balance\nbetween Poland and Ukraine was slightly\nbelow 2 million.\n\n\n\n1 UNHCR data, <u>https://data2.unhcr.org/en/situations/ukraine</u>\n\n2 PESEL UKR is a version of Polish national ID number for Ukrainian citizens in connection with the armed conflict in", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:3:1:0", "start": 157, "end": 188, "surface": "data on employers’ declarations", "probe_tag": "confusion", "probe_score": 0.2361, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:3:1:1", "start": 834, "end": 861, "surface": "2021 Polish National Census", "probe_tag": "confusion", "probe_score": 0.6833, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:3:1:2", "start": 1757, "end": 1767, "surface": "UNHCR data", "probe_tag": "keep", "probe_score": 0.9905, "luna_label": 1}]}, {"key": "aj-125", "text": ", have quickly\nbecome a part of society as **consumers,**\n**employees, entrepreneurs, and**\n**taxpayers** . Currently between <mark>225 and</mark>\n<mark>350 thousand</mark> refugees from Ukraine are\nestimated to be working in Poland. The\nlower bound is the number from social\nsecurity data, while the higher bound is\nthe product of employment rate from the\nsurveys and working age population with\nactive PESEL UKR numbers (Chapter 2).\n\n\nStructural worker shortages, one of\nthe lowest unemployment rates in the\nEuropean Union, record high vacancies,\nand high education attainment of refugees\neased their labour market integration. The\nnumber of Polish citizens aged 20-64 has\ndeclined by 2.6 million from its peak in early\n2010. <sup>7</sup> [^7: According to the Labour Force Survey data from Eurostat.] Despite COVID-19 and geopolitical\nshocks, the unemployment rate oscillated\nin recent years around 3% in Poland, and\nin February 2022 only Czechia exhibited a\nlower rate in the EU. <sup>8</sup> [^8: According to the harmonized unemployment rates from Eurostat.] In Q4‘2021 the share\nof companies reporting vacancies stood at\n49%, the highest level on record, and has\nbeen slowly declining since then. <sup>9</sup> [^9: According to the quarterly NBP survey.]\nIn July-August 2023, 56% of refugees\ndeclared possessing tertiary education and\ntheir employment rate has been almost\none-third higher than for others. <sup>10</sup> [^10: According to the UNHCR (2023) survey.]\n\n\n\n3 As of December 2023, according to UNHCR, based on governmental sources <u>[Situation Ukraine Refugee Situation (unhcr.org)](https://data.unhcr.org/en/situations/ukraine)</u>\n4 According to the active PESEL UKR database.\n5", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:3:3:0", "start": 269, "end": 289, "surface": "social\nsecurity data", "probe_tag": "keep", "probe_score": 0.9971, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:3:3:1", "start": 763, "end": 801, "surface": "Labour Force Survey data from Eurostat", "probe_tag": "keep", "probe_score": 0.999, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:3:3:2", "start": 1239, "end": 1259, "surface": "quarterly NBP survey", "probe_tag": "keep", "probe_score": 0.9762, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:3:3:3", "start": 1451, "end": 1470, "surface": "UNHCR (2023) survey", "probe_tag": "keep", "probe_score": 0.9926, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:3:3:4", "start": 1553, "end": 1588, "surface": "Situation Ukraine Refugee Situation", "probe_tag": "confusion", "probe_score": 0.2281, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:3:3:5", "start": 1671, "end": 1696, "surface": "active PESEL UKR database", "probe_tag": "confusion", "probe_score": 0.735, "luna_label": 1}]}, {"key": "aj-126", "text": " SRH\nservices and one in five did not trust local\nhealthcare providers, in addition to facing language\nbarriers.\n\n\nWomen with a disability reported more barriers with\n11% across the region (N=29) compared to those\nwithout disability (5%).\n\n\nAn in-depth assessment is needed to better\nunderstand sexual and reproductive health (SRH)\nneeds, the role of SRH access barriers in decisions\nto visit Ukraine, and how these barriers vary among\nwomen of different age groups, pregnant and\nbreastfeeding women, and women with disabilities.\n\n\n**Support services for survivors of gender-based**\n**violence**\nServices for survivors of gender-based violence\nencompass a range of functions, including safety\nand security, legal assistance, healthcare, mental\nhealth and psychosocial support. A critical\ncomponent is access to clinical management of\nrape to ensure timely medical treatment and care.\nAs this service is provided by the health care sector,\nas part of SRH, it is included in this analysis.\n\n\nThe SEIS identified gaps in awareness about on\navailable GBV services. In 2024, 38% of\nrespondents were unaware of health services\nproviding support to GBV survivors in their area,\nwhile 58% were unaware of available psychosocial\nsupport services. Respondents were less aware of\nhealth services in rural areas (45%) compared to\nurban areas (37%). Key barriers to accessing\nGBV-related services in general included lack of\nawareness (58%), language and cultural barriers\n(53%) and stigma/ shame (46%). This indicates that\nadditional coordinated efforts between the health,\nprotection and GBV working groups and partners\nare required to enable access to all lifesaving\nGBV-related services including clinical management\nof rape.\n\n\n**17**", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000004:16:1:0", "start": 994, "end": 998, "surface": "SEIS", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 1}]}, {"key": "aj-127", "text": "<u>11,530</u>** **<u>27,302</u>** **<u>Jun-Sep</u>**\n\n\n\nSample distribution by geographical strata,\nfollowed by random selection of districts, and\n<u>convenience sampling for household selection.</u>\n\nSample distribution by geographical strata, and\nsimple random selection of households from cash\n<u>enrolment lists.</u>\n\nTwo strata (collective sites vs. private\naccommodation); random selection of districts and\n<u>convenience sampling for household selection.</u>\n\n\n### **Limitations**\n\nThe statistical significance of the MSNA results is\nlimited by the non-probabilistic selection of\nrespondents. Moreover, the use of convenience\nsampling likely led to a larger share of data being\ncollected from more vulnerable households.\n\n\nThere was also a notably high non-response rate\nregarding questions related to income and\nexpenditure, which likely resulted in non-response\nbias. The income module of the MSNA was also\nmaterially different from the one employed by EU\nSILC, which may limit comparability of this data to\nthat of host populations.\n\n\n\nIt is also important to highlight that there were slight\ndifferences in the questionnaire across countries.\nNot all questions were consistently included in all\ncountry-level surveys, and some answer options\nwere individually adjusted. To mitigate the impact of\nthese differences, the regional analysis focused on\ndata that could be matched. Certain indicators that\nmay have been available by country have thus been\nexcluded from this assessment.\n\n\nLastly, the survey was conducted during the\nsummer months, coinciding with both host country\nand Ukraine school holidays. This period often sees\nmany households temporarily visiting Ukraine,\nwhich impacted the accessibility of households and\nposed challenges in meeting targets, particularly in\ncertain countries and geographic locations.\n\n\n**11**", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000000:10:1:0", "start": 300, "end": 315, "surface": "enrolment lists", "probe_tag": "confusion", "probe_score": 0.5108, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000000:10:1:1", "start": 1206, "end": 1227, "surface": "country-level surveys", "probe_tag": "drop", "probe_score": 0.0037, "luna_label": 1}]}, {"key": "aj-128", "text": "**The World Bank**\nSouth Sudan Enhancing Community Resilience and Local Governance Project (P169949)\n\n\nan accelerating pace of return since the agreement was signed. According to recent intention surveys by\nboth the United Nations High Commission for Refugees and IOM, the main pull factors for return are\nimproved security, family reunification, access to basic services, and livelihood opportunities. About 30\npercent of refugees in neighboring countries consider returning, with IDPs being slightly more willing to\nreturn over the next 12 months. Yet, illegal land occupations remain an issue for the displaced, who\nsometimes will illegally occupy land in place of their own inaccessible properties during return processes.\nIntercommunal conflict continues to create new displacement in locations such as Unity, Warrap, Lakes,\nWestern Bahr-el-Ghazal, Central Equatoria, and Jonglei. <sup>30</sup> [^30: United Nations Mission in South Sudan (UNMISS) (August 2019) ( _Mimeo_ ).] Such factors created at least 167,979 new\ndisplacements in 2019, for instance. <sup>31</sup> [^31: UNOCHA Humanitarian Needs Overview, p.13 and IOM DTM, p.4] Up until 2019, 67 percent of displacements were estimated to have\nbeen driven by state-level conflict actors. In 2019, this dropped to 30 percent, with over half citing\ncommunal violence over property, livestock, and access to resources as drivers of their displacement.\n\n\n11. **Returns in South Sudan reflect pre-conflict patterns of population movement, with returnees**\n**being more likely to return to areas within or near their original villages/towns.** An IOM assessment\ncommissioned by the World Bank found <sup>32</sup> [^32: IOM “Draft Population Movement Analysis October 2019.”] that a large majority (87 percent) of IDPs and refugee returns\nare to areas of habitual residence, with relocation to third areas accounting for just 6 percent. <sup>33</sup> Most\nreturns", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000049:18:0:0", "start": 186, "end": 203, "surface": "intention surveys", "probe_tag": "keep", "probe_score": 0.9137, "luna_label": 1}]}, {"key": "aj-129", "text": "**The World Bank**\nGreater Beirut Public Transport Project (P160224)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Col1|Indicator Name|Core|Unit of<br>Measure|Baseline|End Target|Frequency|Data Source/Methodology|Responsibility for<br>Data Collection|Col10|\n|---|---|---|---|---|---|---|---|---|---|\n||**Name:**Number of<br>passengers per weekday<br>using the formal public bus<br>(BRT and regular buses).||Number<br>(Thousand)|0.00|300.00|Biannual<br>|The RPTA will collect the<br>number of passengers from<br>bus operators and from the<br>mirror system and provide<br>the information to CDR<br>|CDR/the RPTA<br> <br> <br> <br>Private operators<br>|CDR/the RPTA<br> <br> <br> <br>Private operators<br>|\n||Percentage of female<br>ridership in the formal<br>public bus system (BRT and<br>regular buses) per weekday <br>||Percentage|0.00|40.00|Annual<br>|Information about female<br>PT users will be obtained<br>through surveys by RPTA<br>and operators.<br>|CDR/the RPTA<br>|CDR/the RPTA<br>|\n||<br>Description:This indicator measures the daily average passenger ridership of the system (all BRT and regular bus services). This indicator will reflect the number of direct<br>", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000022:46:0:0", "start": 898, "end": 913, "surface": "surveys by RPTA", "probe_tag": "keep", "probe_score": 0.9142, "luna_label": 0}]}, {"key": "aj-130", "text": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\nowned enterprises without children. <sup>19</sup> [^19: Delecourt, S. and Fitzpatrick, A. 2021. “Childcare Matters: Female Business Owners and the Baby-Profit Gap.” Management Science, Vol, 67, No.\n7. May 13.] With total fertility rates in Uganda still very high at 4.7 children per woman, care\nburdens are compounded for women.\n\n13. **Social norms and risks of violence against women also influence the choices of Ugandan women for businesses**\n**sectors and sizes.** Women can feel discouraged from entering or expanding in more profitable (male-dominated) sectors,\nas doing so may signal their transgression of gender norms about men being the main income providers in households.\nRisk of violence also constitutes a significant barrier to women’s entrepreneurship in Uganda. A 2020 national survey of\nviolence against women reports that almost all (95 percent) of Ugandan women between 15–49 years old have\nexperienced physical or sexual violence from either an intimate partner or a non-partner during their lifetime. <sup>20</sup> [^20: Uganda Bureau of Statistics (2021). Uganda Violence Against Women and Girls Survey 2020. Uganda Bureau of Statics. Kampala, Uganda. This\nsurvey was designed as part of the UNHS and drew from UNHS samples which are nationally representative.] This is\nmore than three times the global average (27 percent lifetime,) and the averages for Sub-Saharan Africa (33 percent\nlifetime). <sup>_21_</sup> [^21: World Health Organization (2021). Violence against women prevalence estimates, 2018: global, regional and national prevalence estimates for\nintimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women. Geneva:\nWorld Health Organization.] More than half reported that their partners insisted on knowing where they were", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000025:14:0:0", "start": 898, "end": 944, "surface": "2020 national survey of\nviolence against women", "probe_tag": "keep", "probe_score": 0.9435, "luna_label": 1}, {"key": "jdc_operational:000025:14:0:1", "start": 1196, "end": 1247, "surface": "Uganda Violence Against Women and Girls Survey 2020", "probe_tag": "keep", "probe_score": 0.9281, "luna_label": 1}]}, {"key": "aj-131", "text": ".011 0.020 0.989 0.980 0.979 0.958\n45‐49 0.0030 0.0059 45 0.015 0.029 0.985 0.971 0.969 0.939\n50‐54 0.0058 0.0109 50 0.029 0.055 0.971 0.945 0.954 0.909\n55‐59 0.0081 0.0170 55 0.040 0.085 0.960 0.915 0.924 0.855\n<u>60‐64</u> <u>0.0171 0.0304</u> <u>60 0.086</u> <u>0.152</u> <u>0.914</u> <u>0.848</u> <u>0.884</u> <u>0.770</u>\nSource: Own calculations based on the Population and Housing Census 2015 and following\nthe methodology provided in United Nations (2002).\n\n_Estimation of expected economic costs_\n\n6. Estimating the expected economic costs basically entails predicting the direct and indirect cost of\nschooling at each education‐completion level. These costs include: (i) the household’s school‐related\nexpenses; (ii) the Government’s spending on the education of each child; and (iii) the opportunity cost of\nschooling, i.e., the child’s forgone earnings.\n\n7. The PDV of the expected economic costs that a representative child aged 5 incurs from going to\nschool during the ages of 5 to 17 is given by:\n\n\n\n16\n\n\n\n3\n#  e pri  e pub   1  dk  a\n\n\n\n3\n\n\n\n\n\n\n\n\n\n\n\n\n# 1  g 1  a  6", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000041:63:1:0", "start": 365, "end": 399, "surface": "Population and Housing Census 2015", "probe_tag": "keep", "probe_score": 0.9996, "luna_label": 1}]}, {"key": "aj-132", "text": "**The World Bank**\nChad - Refugees and Host Communities Support Project (P164748)\n\n\nimplementation. The Government is working to address governance challenges in what is currently\nconsidered a weak system. Chad is ranked 162 <sup>nd</sup> out of 180 on the 2017 Economic Freedom Index,\nwhich measures governance and economic freedom based on 12 indicators categorized in four\ngroups. <sup>37</sup> [^37: These indicators include, under _rule of law,_ protection of private property, level of integrity of the government and the fight\nagainst corruption, efficiency of the judicial system; under _size of the state_, government expenditure, the weight of taxes and taxes,\nthe situation of the tax system; under _regulatory efficiency,_ freedom to do business, degree of labor liberalization, state of monetary\npolicy; and under _open markets_, free trade, freedom of investment and financial deregulation.] Moreover, the persistently high threat of terrorism in the sub-region introduces an\nadditional layer of uncertainty. For all of these reasons, the political and governance risk is\nconsidered **_High._**\n\n89. **The country’s macroeconomic context is characterized by low diversification of**\n**revenue streams and limited capacity for resource mobilization** . The oil sector, the country’s\nmain source of fiscal revenue, has been severely impacted by low prices in the international\nmarket, further impacting the national debt situation. Severe fiscal constraints have led to increased\nlevels of poverty and vulnerability throughout the country, and have impaired the Government’s\nability to provide basic social services. As the sustainability of the project relies on the\nGovernment’s commitment to long-term investment in basic service delivery, the macroeconomic\nrisk to the project is **_High._**\n\n90. **Sector strategies and policies are in place to support the broader social protection**\n**agenda.** Although there are limited social protection programs at the national level, the National", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000035:40:0:0", "start": 257, "end": 284, "surface": "2017 Economic Freedom Index", "probe_tag": "keep", "probe_score": 0.9621, "luna_label": 1}]}, {"key": "aj-133", "text": "**The World Bank**\nSouth Sudan Enhancing Community Resilience and Local Governance Project (P169949)\n\n\n10. **While the situation remains fluid, the recent reduction in armed conflict may facilitate**\n**substantial population movement.** About 1.3 million people have returned from displacement within or\noutside South Sudan since 2016. Of these, 644,174 returned in the short period between the signing of\nthe revitalized peace agreement in September 2018 until June 2019. <sup>26</sup> [^26: IOM DTM, Round 6 (op cit.).] The largest number of returns were\nto Jonglei (191,052), Upper Nile (164,068), and Western Bahr Ghazal (162,770). <sup>27</sup> [^27: Ibid.] These figures indicate\nan accelerating pace of return since the agreement was signed. According to recent intention surveys by\nboth the United Nations High Commission for Refugees and IOM, the main pull factors for return are\nimproved security, family reunification, access to basic services, and livelihood opportunities. About 30\npercent of refugees in neighboring countries consider returning, with IDPs being slightly more willing to\nreturn over the next 12 months. Yet, illegal land occupations remain an issue for the displaced, who\nsometimes will illegally occupy land in place of their own inaccessible properties during return processes.\nIntercommunal conflict continues to create new displacement in locations such as Unity, Warrap, Lakes,\nWestern Bahr-el-Ghazal, Central Equatoria, and Jonglei. <sup>28</sup> [^28: United Nations Mission in South Sudan (UNMISS) (August 2019) ( _Mimeo_ ).] Such factors created at least 167,979 new\ndisplacements in 2019, for instance. <sup>29</sup> [^29: UNOCHA Humanitarian Needs Overview, p.13 and IOM DTM, p.4] Up until 2019, 67 percent of displacements were estimated to have\nbeen driven by", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000058:6:0:0", "start": 493, "end": 509, "surface": "IOM DTM, Round 6", "probe_tag": "keep", "probe_score": 0.901, "luna_label": 1}, {"key": "jdc_operational:000058:6:0:1", "start": 769, "end": 786, "surface": "intention surveys", "probe_tag": "keep", "probe_score": 0.9156, "luna_label": 1}]}, {"key": "aj-134", "text": "5,036 48.9 4,883\nData on educational level missing 173,136 37.8 1,145 371,596 45.1 2,060\n<u>Total</u> <u>194,926</u> <u>46.1</u> <u>1,057</u> <u>300,625</u> <u>51.9</u> <u>1,448</u>\n<u>Source: Tsimpo and Wodon (2020b) using UNHS 2012/13 and 2016/17.</u>\n\n4. **Earnings gains from higher educational attainment are confirmed through regression analysis.** Table 5.3\nprovides results from regression analysis of both monthly and hourly earnings using two different models\n(Ordinary Least Squares for workers with positive wages and Heckman model taking into account sample selection\nand the probability of having positive earnings). The marginal effects measure earnings gains in percentage terms\nversus no education or less than P1. For example, with lower secondary education completed, gains range from\n35.9 percent to 58.8 percent versus no education depending on the year and model considered. The Table also\nprovides the gains from completing lower secondary versus completing primary. In 2016/17 across the four\nmodels, the average gain is at 15.8 percent (average differential effect between primary and lower secondary for\nthe four model specifications). For monthly earnings, the average gain from a lower secondary education is 19.2\npercent versus completing primary.\n\n\n\nwage\n<u>(USh)</u>\n\n\n\n<u>(Hrs.)</u>\n\n\n\n<u>(Hrs.)</u>\n\n\n\nPage 88 of 94", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000018:93:2:0", "start": 224, "end": 228, "surface": "UNHS", "probe_tag": "confusion", "probe_score": 0.8416, "luna_label": 1}]}, {"key": "aj-135", "text": "**I.** **STRATEGIC CONTEXT**\n\n\n**A.** **Country Context**\n\n\n1. Chad is a large, land-locked country with a population of 12.5 million. It ranks 184 <sup>th</sup> out\nof 187 countries in the Human Development Index <sup>1</sup> [^1: _The Rise of the South: Human Progress in a Diverse World. Human Development Report 2013._ New York: United\nNations Development Programme, 2013.] and 73 <sup>rd</sup> out of 78 countries in the Global\nHunger Index. <sup>2</sup> [^2: _The Challenge of Hunger: Building Resilience to Achieve Food and Nutrition Security. Global Hunger Index 2013_ .\nWashington DC, Bonn, and Dublin: International Food Policy Research Institute, Concern Worldwide, and\nWelthungerhilfe, 2013.] The population is highly vulnerable to shocks and exposed to crises and\ndisasters. Chad’s rural areas have 78 percent of the national population; 80 percent of rural\npeople support themselves through subsistence farming and livestock activities.\n\n\n2. Chad’s location in an unstable geopolitical neighborhood makes it vulnerable to the\neffects of crises erupting across its borders. Since independence in 1960, Chad has suffered from\ninstability and conflict arising from tensions between religious and ethnic factions, further\nfuelled by interference from neighboring states. The severe humanitarian crises in neighboring\nSudan and Central African Republic (CAR) have driven hundreds of thousands of refugees\nacross the border into Chad, compounding the internal displacement of native Chadians and\nfurther draining the country’s fiscal and economic resources. Conflicts in Darfur beginning in\n2003 and the 2012 military coup in CAR have sent 450,000 refugees to eastern and southern\nChad, heightening pressure on the limited resources of an already highly vulnerable local\npopulation.\n\n\n3. Petroleum exploration has profoundly altered the structure of production and exports in\nthe Chadian economy. Until 2004, the agricultural sector, including subsistence", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000007:10:0:0", "start": 190, "end": 213, "surface": "Human Development Index", "probe_tag": "confusion", "probe_score": 0.8824, "luna_label": 1}]}, {"key": "aj-136", "text": "**The World Bank**\nLebanon: Wheat supply emergency response project (P178866)\n\n\nCrescent Societies, which will confirm that the bakeries service areas that include poor and vulnerable groups are\nreceiving the flour as per Framework Agreement. In addition, the project will finance high frequency ‘Listening to\nPoor and Vulnerable Household Surveys’, entailing data collection on bread prices and consumption for the poor\nand vulnerable households, by collecting random sampling and surveying (biweekly) using UNHCR and WFP\nbeneficiary lists. This information will be triangulated at MOET level with information consolidated from the\nconsumer protection agency, GM, and price monitoring system, and used to adopt appropriate remedies, such as\nincluding in the Framework Agreement a preferential distribution clause for bakeries located in areas where most\nof the poor and vulnerable groups are located. Additional social risks are associated with the consultancy services\nand technical assistance under Component 2 that will help MOET’s planned transition from the current wheat\nsubsidy system to a more market-oriented system. Such risks will be mitigated through recommendations in the\nstudy to ensure linkages with the social safety net programs like ESSN, a clear communication campaign, and an\neffective and widespread dissemination of the grievance mechanism. All the mitigation measures will be covered\nin the Environmental and Social Management Plan (ESMP), which will be prepared before signing Framework\nAgreements with local importers and as a disbursement condition.\n\n80. The Environmental risk rating is Moderate. The project is limited to procurement of wheat to maintain\nthe supply during the market disruptions caused by the war in Ukraine. The project finance will not involve any\ncivil works nor any other activities after the vessels deliver the shipment at the ports of Beirut and/or Tripoli.\nThere are some associated activities, which are not directly financed by the project but are directly and\nsignificantly related to the project; will be carried out contemporaneously with the project; and necessary for the\nproject to be viable and would not have been conducted if the project did", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000019:33:0:0", "start": 297, "end": 347, "surface": "Listening to\nPoor and Vulnerable Household Surveys", "probe_tag": "confusion", "probe_score": 0.3834, "luna_label": 0}, {"key": "jdc_operational:000019:33:0:1", "start": 509, "end": 540, "surface": "UNHCR and WFP\nbeneficiary lists", "probe_tag": "confusion", "probe_score": 0.7232, "luna_label": 1}]}, {"key": "aj-137", "text": "**The World Bank**\nUganda Digital Acceleration Program (P171305)\n\n\n(see Annex 3). Universal access funds available through the Rural Communications Development Fund\n(RCDF) could possibly be repurposed for this. The mobilization of private sector investment could be\nmade on the basis of competitive tenders to increase the accessibility, quality and affordability of\nInternet services.\n\n\n**Figure 7: Mobile telecommunications coverage maps (3G)**\n\n\n_Source: GSMA_\n**Figure 8. New players, technologies, and business models to crack the internet challenge**\n\n\n<u>Subcomponent 1.3: Digital inclusion, especially for host communities and refugees</u>\n\n  - Development and deployment of digital solutions to facilitate hosting communities in their interactions\n\nand integration efforts of refugees including for instance the establishment of local networks with\nhotspots and integrated digital datacenters, pre-purchasing internet bandwidth, providing subsidies for\nuser’s equipment and installing community power charging stations. These solutions will cater for the\n\n\nJul 22, 2019 Page 18 of 28", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000063:17:0:0", "start": 400, "end": 439, "surface": "Mobile telecommunications coverage maps", "probe_tag": "confusion", "probe_score": 0.0991, "luna_label": 0}]}, {"key": "aj-138", "text": " appeals and\nclaims received by households.\n\n42. With respect to the implementation arrangements of the e-card food voucher, the\nfollowing arrangements have been agreed upon: (i) WFP will conduct training for NPTP field\nwork coordinators and social workers, including on assessments, distribution, monitoring and\nhousehold visits; (ii) NPTP will be responsible for distribution and training of beneficiaries on\nthe use of the e-card, as well as assessing and monitoring of food security indicators; (iii) WFP\nwill provide the _Banque Libano-Française_ (BLF) with the necessary information/data based on\nthe NPTP database and operations for the production, activation and loading of e-cards; (iv)\nWFP will in turn share reports from the bank on transactions and spending patterns; and (v) WFP\nand its identified partner(s) will continue to support NPTP through joint reporting and\nmonitoring in the field. (For more details on the business processes and implementation, see\nAnnex II).\n\n\n**B.** **Results Monitoring and Verification**\n\n\n43. The results monitoring framework assesses progress towards the PDO through key\nindicators, focusing on expanding the coverage and social assistance of the NPTP. Specifically,\nthe project will monitor the number of direct project beneficiaries of education, health and e-card\nfood vouchers. All data will be collected disaggregating by gender to be able to monitor\nparticipation by women and girls. In addition, intermediate indicators will monitor program\nawareness and efficiency in terms of timing between application and eligibility notification, over\nthe life of the project.\n\n44. A computerized modular MIS, developed under the first phase of the NPTP, is the central\npiece of the monitoring and evaluation (M&E) system and includes a module to register\napplicant households in the NPTP database, record the results of their eligibility assessment\n\n\n14", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000047:24:1:0", "start": 607, "end": 620, "surface": "NPTP database", "probe_tag": "confusion", "probe_score": 0.073, "luna_label": 1}]}, {"key": "aj-139", "text": "<br>supervisors. MOE will then evaluate teachers against this<br>framework to identify teacher professional development needs<br>and to determine teachers’ placement on the career path<br>(certification level). Data should be disaggregated by gender.|MOE Teacher<br>evaluation records|Third Party|The verification agency will verify<br>teacher evaluation records.|\n|**DLR#4.4.**Number of K‐12<br>teachers trained and certified <br>|In‐service training for grade 4 to 10 teachers to be delivered<br>with the aim of aligning teacher skills and competencies with<br>the new Teacher Standards. The training would also place<br>emphasis on the application of targeted instruction strategies<br>and the use of student assessments (formative and<br>summative). Instructional support materials, whether paper or<br>technology based, that are closely aligned with the curriculum<br>and that teachers will be trained on, will be introduced as<br>supplementary means for fostering improved teaching<br>practices. Successful completion of the training by the<br>awarding of a teacher certificate will serve ", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000041:45:2:0", "start": 266, "end": 284, "surface": "evaluation records", "probe_tag": "confusion", "probe_score": 0.5041, "luna_label": 1}, {"key": "jdc_operational:000041:45:2:1", "start": 336, "end": 362, "surface": "teacher evaluation records", "probe_tag": "confusion", "probe_score": 0.5041, "luna_label": 0}]}, {"key": "aj-140", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\n\n|Col1|Col2|Col3|Team estimat<br>ions.|Col5|Col6|\n|---|---|---|---|---|---|\n|Of which, are members of female<br>headed households|Number of females who<br>receive technical and<br>financial support through<br>the project in the<br>reconstruction of their<br>residential unit from the<br>PoB explosion.|Annual<br>|Progress<br>Reports, Mon<br>itoring and<br>Evaluation<br>Reports.<br>Third-Party<br>Monitoring<br>Agent report<br>s. Project<br>Management<br>Team<br>estimations.<br>|Number of women will<br>be determined by<br>disaggregating the<br>beneficiary data of the<br>progress reports<br>|Project Management<br>Team/UN-Habitat<br>|\n|Owners benefiting from resilient,<br>rehabilitated residential units|Number of owners who<br>receive technical and<br>financial support through<br>the project in the<br>reconstruction of their<br>residential unit from the<br>PoB explosion.|Annual<br>|Progress<br>Reports, Mon<br>itoring and<br>Evaluation<br>Reports.<br>Third-Party<br>Monitoring<br>Agent<br>reports. Proj<br>ect<br>Management<br>Team<br>estimations.<br>|Number", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000012:39:0:0", "start": 653, "end": 669, "surface": "beneficiary data", "probe_tag": "confusion", "probe_score": 0.2134, "luna_label": 0}]}, {"key": "aj-141", "text": "**The World Bank**\nLebanon: Wheat supply emergency response project (P178866)\n\n\nBased on these agreements, MOET will pay suppliers directly. MOET will receive and review all supporting\ndocuments from the importers after signing the contracts with the suppliers and will clear them before processing\nand payment.\n\n\n10. MOET will recruit a Technical Auditor, with TORs acceptable to the Bank, to validate that the purchase of\nthe wheat has been made according to the legal agreement, POM, Framework Agreements (to be signed between\nMOET and importers) and the contracts to be signed between the importers and suppliers and that the wheat has\nbeen discharged at the Beirut and Tripoli ports. The Technical Auditor will also verify that the wheat has been\ndischarged and distributed to the mills according to the agreement with MOET and the mills. Specifically, they will\nconduct the following:\n\n\n    - Validate the stock count of the wheat upon delivery at the designated ports (verify the quantities as per\nthe framework and contracts signed, this will include declared prices/quantity at loading port to compare\nwith declared price/quantity arriving in Beirut or Tripoli).\n\n    - Verify that the wheat has been discharged and moved to delivery at the initial destination and then verify\nthat the wheat is received by the mills and securely stored according to the Framework Agreement.\n\n    - Verify that the flour produced by the mills and subsequently distributed to the retail sellers (bakeries).\nThis is based on data collection and data analysis.\n\n    - Validate the quantities distributed as bread and flour to the retail sellers (supermarkets, stores, etc.)\nacross all governorates by sampling and analyzing data.\n\n11. The Technical Auditor will keep detailed monthly data records related to the above process and will\nproduce weekly reports about the progress made in distribution and will submit the reports simultaneously to the\nMOET and the World Bank. MOET will maintain a detailed inventory of the wheat stock from purchase to discharge\nto distribution along the chain and will add this", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000019:46:0:0", "start": 1765, "end": 1785, "surface": "monthly data records", "probe_tag": "confusion", "probe_score": 0.2308, "luna_label": 0}]}, {"key": "aj-142", "text": "**The World Bank**\nStrengthening Lebanon’s Covid-19 Response (P178587)\n\n\n79 **. The World Bank will support various efforts to actively engage with citizens to collect feedback on the NDVP and**\n**project performance, through social media surveys, the TPM mechanism, and through the use of Iterative**\n**Beneficiary Monitoring (IBM).** In February 2021, the World Bank team administered a Facebook survey to assess\nbeliefs and attitudes towards COVID-19 vaccination. Responses from more than 15,000 participants showed that\nonly 28 percent of them intended to take the vaccine when it becomes available, and 48 percent were still unsure\nat the time of the survey. Moreover, as part of the TPM mechanism, feedback from vaccine recipients and health\nproviders was collected and shared regularly with the MoPH, the National Vaccination Committee, and the Vaccine\nExecutive Committee for corrective action as needed. Additionally, performance score cards were produced and\nshared with vaccination sites to monitor performance and identify challenges. An IBM approach is also being\nplanned as an iterative feedback loop that collects information directly from beneficiaries and identifies challenges\nat the local level that can be addressed by project teams. In addition to improving project efficiency, this approach\nincreases beneficiary engagement and satisfaction by creating positive, self-reinforcing cycles of improvement.\nFindings will be used to improve the communication campaign and citizen engagement. Through the IBM as well\nas social media surveys, engagement with community, especially in remote areas, will ensure the inclusion of their\nongoing feedback in the rollout and implementation of the COVID-19 vaccination campaign to strengthen targeting\naccuracy and increase uptake. To ensure citizen engagement, the project will: (a) target messages to areas where\nvulnerable groups, including refugees and IDPs, reside to inform them about safety measures and benefits; (b)\ntailor messages to the elderly and those with medical risks including their target family members and health care", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000000:39:0:0", "start": 226, "end": 246, "surface": "social media surveys", "probe_tag": "confusion", "probe_score": 0.6578, "luna_label": 0}, {"key": "jdc_operational:000000:39:0:2", "start": 226, "end": 246, "surface": "social media surveys", "probe_tag": "confusion", "probe_score": 0.6606, "luna_label": 0}, {"key": "jdc_operational:000000:39:0:1", "start": 389, "end": 404, "surface": "Facebook survey", "probe_tag": "keep", "probe_score": 0.9452, "luna_label": 1}]}, {"key": "aj-143", "text": "<br>|\n|Grievances responded and/or resolved<br>within the stipulated service standards for<br>response times (dis-aggregated by<br>gender, refugees, hosts)|<br>Percentage of grievances of<br>Project Affected People<br>responded and/or resolved<br>within the stipulated service<br>standards|Semi-<br>annually<br>|Environment<br>al and Social<br>Management<br>System<br>|Data compilation<br>|Monitoring and<br>evaluation consultants,<br>UNRA<br>|\n|Percentage increase in employment of<br>women (those in refugee camps of Bidi<br>Bidi, Lobule, and Palorinya)|Percentage increase in<br>women employment (those<br>in selected refugee camps)|six-<br>monthly<br>|Monitoring<br>reports<br>|Government data, M&E<br>reports<br>|Monitoring and<br>evaluation consultants,<br>UNRA<br>|\n|Number of women employed though<br>cooperatives supported by the project|Number of women<br>employed though<br>cooperatives supported by<br>the project|Semi-<br>annually<br>|Data<br>collected by<br>monitoring<br>and<br>evaluation<br>consultants<br>|surveys<br>|Monitoring and<br>evaluation consultants,<br>UNRA<br>|\n\n\nPage 57 of 80", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000050:61:1:0", "start": 682, "end": 697, "surface": "Government data", "probe_tag": "confusion", "probe_score": 0.6913, "luna_label": 0}]}, {"key": "aj-144", "text": "\nnew areas to examine staffing, equipment, jurisdictional issues, core function capacities, local service mapping capability,\nand GBV referral capacities and (b) the provision of technical assistance for county government officials on service delivery\nplanning; positive engagement with communities through participatory development planning, subproject\nimplementation monitoring, BDCs/PDCs’ performance monitoring, and periodic reporting on ECRP-II implementation. The\nlevels of technical assistance will be tailored to each county based on the findings of the ECRP functionality assessments\nfor previously engaged counties and new functionality assessments for new counties engaged under ECRP-II. This\nassistance will also facilitate the county government officials’ visiting subproject sites and participating in the BDCs/PDCs’\nplanning workshops as needed. Given the unknowns with regard to the level of functionality of county governments,\nespecially in conflict-affected areas, this subcomponent does not have an explicit result indicator. Where functionality\nassessments determine that some county governments are not functional, the project will focus on supporting community\ninstitutions.\n\n38. **Subcomponent 2.3 National Government Strengthening** . This sub-component will support the capacity building\nof the PMU based on an assessment of their technical competencies in the areas of financial management, procurement,\nproject planning, monitoring and evaluation, community engagement methods, and safeguards. The Project will also\ndevelop standards and training for fiduciary and technical supervision capacities. ECRP-II will facilitate linkages between\nthe PMU and the CCTs at the county level to establish protocols and regularized support for constructive county\nengagement in local resource management and service delivery improvement and maintenance activity.\n\n\n39. GIZ, who has long engaged in local governance strengthening support in South Sudan with LGB, has shown interest\nin collaborating with the Bank on the implementation of ECRP-II. While discussions are still underway, GIZ may provide\nparallel financing to all or part of the ECRP-II activities while utilizing the same government agencies as the implementing", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000027:15:1:0", "start": 562, "end": 592, "surface": "ECRP functionality assessments", "probe_tag": "confusion", "probe_score": 0.4898, "luna_label": 1}]}, {"key": "aj-145", "text": " inequalities persist. School enrollment and retention rates\namong girls in the refugees hosting districts are exceptionally low, a result of their domestic\nresponsibilities, child marriage, teenage pregnancy, long distances to schools, and lack of sanitation\n\n\n9 This index reflects gender-based inequalities in three dimensions – reproductive health, empowerment, and economic\n[activity. http://hdr.undp.org/sites/all/themes/hdr_theme/country-notes/UGA.pdf.](http://hdr.undp.org/sites/all/themes/hdr_theme/country-notes/UGA.pdf)\n10 Uganda Demographic and Health Survey (2016).\n11 Uganda Violence Against Children Survey (2015).\n12 UNICEF: Situation Analysis of Children in Uganda, 2015\n13 Government of Uganda: Violence Against Children Survey (VACS) Report, 2018\n14 UDHS, 2016\n15 UNHCR, 2016, 5-Year Interagency SGBV Strategy, Uganda\n_16_ <u>http://ug.one.un.org/sites/default/files/documents/UNAC-</u>\n<u>[Northern%20Uganda%20and%20West%20Nile%20Humanitarian%20and%20Development%20Report%20-January-](http://ug.one.un.org/sites/default/files/documents/UNAC-Northern%20Uganda%20and%20West%20Nile%20Humanitarian%20and%20Development%20Report%20-January-February%202018.pdf)</u>\n<u>[February%202018.pdf](http://ug.one.un.org/sites/default/files/documents/UNAC-Northern%20Uganda%20and%20West%20N", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000001:5:2:0", "start": 534, "end": 570, "surface": "Uganda Demographic and Health Survey", "probe_tag": "confusion", "probe_score": 0.8688, "luna_label": 1}, {"key": "jdc_operational:000001:5:2:1", "start": 582, "end": 621, "surface": "Uganda Violence Against Children Survey", "probe_tag": "confusion", "probe_score": 0.8202, "luna_label": 1}]}, {"key": "aj-146", "text": ">20</sup> and women’s empowerment. <sup>21</sup> [^21: UNDP. 2018. _Human Development Indices and Indicators: 2018 Statistical Update - South Sudan._] Local governance and access to services in South Sudan\npossess gendered dimensions, such that women and girls are affected disproportionately compared to men and boys. A\nsteeped patriarchal structure underpins male authority and decision-making in local community leadership, customary\nlaw, restorative justice, the police and security forces, and within the household. One survey shows the civic and political\nparticipation of men at 84 percent compared to women at 15 percent. <sup>22</sup> [^22: Kenwill International Limited. 2015 _. Fortifying Equality and Economic Diversification (FEED): Improved Livelihoods in South Sudan._ Gender Assessment Report, World\nVision, July 2015.] The percentage of women in leadership roles was\nhighest in Western Bahr el Ghazal state (30.3 percent) and lowest in Warrap State (4.9 percent). <sup>23</sup> [^23: Kenwill International Limited. 2015 _._] In addition, there are\nlimited income-generating opportunities for women. When women do generate income, decisions on its use are often\n\n\n9 World Bank 2021.\n10 IMF, March 2021\n11 World Bank. 2019. _The World Bank Group Action Plan on Climate Change Adaptation and Resilience._\n12 United Nations Development Programme 2020 Human Development Reports.\n13 UNOCHA May 2021.\n14 UNOCHA. May 2021.\n15 International Organization for Migration (IOM), Displacement Tracking Matrix (DTM), Round 9 (op cit.)\n16 Checchi, F., et. al. 2018. _Estimates of Crisis-attributable Mortality in South Sudan, December 2013–April 2018: A Statistical Analysis._ London School of Hygiene and\nTropical Medicine.\n17 IPC = Integrated Food Security Phase Classification.\n18 IPC. South Sudan IPC Results October 2020 – July 2021.\n19 OCHA, 2021.", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000027:4:2:0", "start": 1483, "end": 1511, "surface": "Displacement Tracking Matrix", "probe_tag": "confusion", "probe_score": 0.7963, "luna_label": 1}]}, {"key": "aj-147", "text": "*\n\n\nThe barley value chain segments are organized as follows:\n\n\n**8.** **Barley imported into Jordan arrives CIF to port of Aqaba** . The barley makes its way from the port of\nAqaba to either one of four silo sites (Aqaba, Juwaidah, Russeifa, and Irbid) or to the several bunkers sites\ncurrently used for strategic reserves (used for both wheat and barley). The average barley import supply\nchain cost until storage is around JOD24-30, depending on the storage location.\n\n\n**9.** **Barley is then distributed to sale centers of MoITS in the governorates** **by MoITS**  - a network of\noutlets for distributing subsidized animal feed (along with bran, a side-product of wheat milling).\n\n\n**10.** **From there, barley is sold to registered sheep owners, according to the number of livestock heads**\n**registered on the livestock vaccination card issued by the Ministry of Agriculture** . Each head of sheep is\nallocated 20kg of barley per month at the subsidized price of JOD178 per ton. Over the past 10 years, the\nprice of barley has been subsidized and set at a constant level of around JOD175 per ton, while wheat bran\nused for feed (byproduct of wheat milling) is being sold at JOD140 per ton.\n\n\n**11** . The private sector participates in the animal feed value chain, importing small quantities of barley,\nsoybeans, maize and fortification mixes, sold at market rates, and geared towards cattle farmers.\n\n\nPage 54 of 54", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000024:58:1:0", "start": 817, "end": 843, "surface": "livestock vaccination card", "probe_tag": "confusion", "probe_score": 0.6496, "luna_label": 1}]}, {"key": "aj-148", "text": "|Intermediate Results Indicators|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|\n|---|---|---|---|---|---|---|---|---|---|\n|||||Cumulative Target Values|Cumulative Target Values|Cumulative Target Values|Frequency|Data<br>Source/Methodology|Responsibility for Data<br>Collection|\n|Indicator Name|Core|Unit of<br>Measure|Baseline<br>2013|2014-15<br>(Year 1)|2015-16<br>(Year 2)|2016-17<br>(Year 3)||||\n|Number of NPTP Applicants||Number|480,000|550,000|700,000|800,000|Quarterly|- NPTP database|NPTP Program|\n|Time lapse between application<br>and eligibility notification||Months|3|1|1|1|Quarterly|- NPTP database|NPTP Program|\n|Household awareness of NPTP||Percentage|40|60|80|90|Two time during the life<br>of the program|- Opinion Poll surveys<br>(Y2, Y3)|NPTP Program|\n|Proportion of assisted people<br>informed about the e-card food<br>program||Percentage|0||100||Once after the first year<br>of the program|- NPTP database|NPTP Program<br>|\n\n\n23", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000047:33:0:0", "start": 478, "end": 491, "surface": "NPTP database", "probe_tag": "confusion", "probe_score": 0.8819, "luna_label": 0}, {"key": "sample:jdc_operational:000047:33:0:1", "start": 724, "end": 744, "surface": "Opinion Poll surveys", "probe_tag": "keep", "probe_score": 0.9834, "luna_label": 0}]}, {"key": "aj-149", "text": "\npeople (both because of the fighting itself as well as fear of the violence). Available statistics indicate that\nover 2 million persons are now internally displaced including over 200,000 civilians who have sought\nprotection in the United Nations (UN) Protection of Civilian (PoC) sites across the country. Further, an\nestimated 1.4 million persons have sought refuge in neighboring countries.\n\n\n3. **Conflict has resulted in a near collapse of the economy.** Currently, the country exhibits all the signs\nof macroeconomic collapse. There have been sharp declines in output, and a spike in the parallel exchange\nmarket premium. The economy is expected to further contract by about 11 percent with both the oil and\nnon‐oil sectors expected to decrease. The fiscal deficit remains wide, although real magnitudes are\ndifficult to estimate given the hyperinflation and lack of real time data. Based on the 2016/17 budget, the\nfiscal deficit is estimated at about 14 percent of Gross Domestic Production (GDP). Export revenues\ndecreased due to declining oil prices and lower oil production. Oil production is expected to decrease to\nabout 120,000 barrels per day this fiscal year down from 165,000 barrels per day in 2014, itself less than\nhalf of peak production before independence in 2011. There is an accelerated depreciation of the pound,\nwith the South Sudan Pound (SSP) depreciating on the parallel market from SSP 18.5 per US dollar in\nDecember 2015 to reach SSP 110 per US dollar in March, 2017. This follows the move to a managed\nfloating exchange rate from a fixed exchange rate.\n\n\n4. **Poverty levels remain high and are on the upward spiral.** Estimates for 2015, indicated that 66\npercent of the population lives below the poverty line (US$32 per month), a proportion that rises to 71\npercent in the rural areas. However, mainly because of displacement, poverty is now more endemic with\nestimates of at least 80 percent of the", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000038:12:1:0", "start": 874, "end": 888, "surface": "real time data", "probe_tag": "confusion", "probe_score": 0.5249, "luna_label": 1}]}, {"key": "aj-150", "text": "\nUgandan research institutions will be a key partner in implementing the M&E framework building on existing data\nreports and in partnership with local research institutions. The PSFU PIU will work closely with Ugandan research\ninstitutions for three reasons. First, to coordinate the various data collections so as to be more efficient in utilizing\nall existing firm-level data. Second to identify and collect additional data with respect to jobs and firm productivity.\nThird, to use the data collected on an ongoing basis make suggestions to the PSC to improve the project impact. <sup>48</sup> [^48: The objective is to develop an actional M&E system that is used as instrument to monitor and improve project effectiveness along\nimplementation rather than just a system for ex-post accountability.]\nAll data will be disaggregated by gender, refugee, host community, and non-host community nations to ensure\nadequate targeting and collection of results for targeted populations.\n\n\n**C. Sustainability**\n\n\n79. In principle, the project components are based on implementing interventions that are fully financially\nsustainable while generating the largest possible impact. Although the rebates offered to borrowers under\nWindow 1 of Component 1 are not recoverable, from a cost-benefit perspective the extension of the amortization\nperiod of MSME loans is consistent with the BoU policy and will contribute to a reduced probability of default of\nMSMEs and to the stability of the financial sector by reducing NPLs and also to preserving functioning MSMEs and\njobs that might otherwise get impaired. It will also support revival of otherwise viable enterprises and therefore,\ncontribute to growth and to protecting institutional, social and financial capital, that might otherwise have been\nlost. Windows 2 and 3 of Component 1 are financially self-sustaining: Tier 3 and 4 institutions (MFDs, MFIs, and\nSACCOs) will be provided with access to concessional credit lines. These credit lines will be limited in volume and\nhave an 18-month maturity so as to be available over the COVID-19 period only and so as not to distort the market\nfor loan capital. All participants along the chain", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000021:36:1:0", "start": 362, "end": 377, "surface": "firm-level data", "probe_tag": "confusion", "probe_score": 0.6154, "luna_label": 1}]}, {"key": "aj-151", "text": "**The World Bank**\nUganda: Investment for Industrial Transformation and Employment (P171607)\n\n\n\n\n\n\n\n\n\n\n\n\n|Indicator Name|PBC|Baseline|Intermediate Targets|Col5|Col6|Col7|End Target|\n|---|---|---|---|---|---|---|---|\n|<br>|||**1 **|**2 **|**3 **|**4 **||\n|manufacturing sectors<br>(Number)||||||||\n|**Firm Acess to Finance**|**Firm Acess to Finance**|**Firm Acess to Finance**|**Firm Acess to Finance**|**Firm Acess to Finance**|**Firm Acess to Finance**|**Firm Acess to Finance**|**Firm Acess to Finance**|\n|Beneficiaries reached with<br>financial services (CRI,<br>Number)||0.00|50,000.00|100,000.00|130,000,000.00<br>|170,000,000.00|200,000.00|\n|Number of SMEs with a loan<br>or line of credit (CRI,<br>Number)|<br>|0.00|2,700.00||||5,300.00|\n|**Number of formally employed in the manufacturing sector according to PAYE data collected by URA TIN**|**Number of formally employed in the manufacturing sector according to PAYE data collected by URA TIN**|**Number of formally employed in the manufacturing sector according to PAYE data collected by URA TIN**", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000021:56:0:0", "start": 817, "end": 847, "surface": "PAYE data collected by URA TIN", "probe_tag": "confusion", "probe_score": 0.8746, "luna_label": 1}, {"key": "sample:jdc_operational:000021:56:0:1", "start": 921, "end": 951, "surface": "PAYE data collected by URA TIN", "probe_tag": "keep", "probe_score": 0.9709, "luna_label": 1}]}, {"key": "aj-152", "text": " earn their livelihoods, is key to achieving the\ngoals. Fertility rates remain high (at five births per woman), as does the population growth rate, meaning that most\nhouseholds have few income earners and several dependents, and much of the care responsibilities fall on women. In this\ncontext, supporting women micro-entrepreneurs to move further up the value chain or into other, more profitable\nsectors, will help harness women's contributions to the economy and contribute to greater growth overall.\n\n\n25. **This project is also aligned with Bank-financed operations aimed at promoting gender inclusion and WEE.** These\ninclude the Skills Development Project (P145309); the Agricultural, Technological, and Advisory Services Project\n(P109224); the Reproductive, Maternal and Child Health Services Improvement project, and NUSAF. The project’s work\nwith refugees will build on and partner closely with DRDIP project in refugee-hosting districts.\n\n\n**C. Proposed Development Objective(s)**\n\n\nTo enhance the economic and social empowerment of women entrepreneurs in Uganda\n\n\n26. **The proposed outcome indicators to measure achievement of the PDO are** :\n\n\n    - Increase in household income, including the contribution of women\n\n    - Increase in productive assets\n\n    - Increase in the number of women-led enterprises in project locations\n\n    - Increase in household welfare\n\n    - Increase in women’s decision-making.\n\n\n27. Potential outcome indicators will be explored during preparation to ensure that baseline data exist, that they are\nmeasurable, and that approaches to measure them are available to provide accurate results at reasonable cost.\n\n\nJun 15, 2021 Page 9 of 13", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000005:8:1:0", "start": 1510, "end": 1523, "surface": "baseline data", "probe_tag": "drop", "probe_score": 0.0001, "luna_label": 0}]}, {"key": "aj-153", "text": ">days generated by SNSOP|This<br>indicator<br>will be<br>measured<br>at<br>minimum<br>on a<br>quarterly<br>basis<br>including<br>through<br>missions<br>and ISR's<br>|SNSOP MIS<br>which hosts<br>information<br>on LIPW<br>work days<br>used to<br>generate<br>payment<br>schedules.<br>|The number of LIPW<br>work days will be<br>documented at LIPW<br>work sites and collected<br>by field-based staff.<br>This data is inputted<br>into the SNSOP MIS to<br>generate payment<br>schedules<br>|Implementing partner<br>|\n|Number of beneficiaries receiving cash for <br>performing labor intensive public works<br>who are refugees or host communities|<br>Number of total<br>beneficiaries that directly<br>receive cash transfer for|This<br>indicator<br>will be|Registration<br>and payment<br>data in the|Beneficiary data is<br>collected during<br>registration and|Implementing Partner<br>|\n\n\nPage 57 of 74", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000057:61:1:0", "start": 790, "end": 806, "surface": "Beneficiary data", "probe_tag": "drop", "probe_score": 0.025, "luna_label": 0}]}, {"key": "aj-154", "text": "\n36. The second Result Area aims to achieve **_improved teaching and learning conditions._** The\nmechanism to achieve this goal is a focus on improving the school physical environment and the\ncapacity of teachers and school leaders, and fostering positive student and teacher behavior and civic\nawareness toward schools and their communities. The focus of this Result Area is both on the physical\nschool infrastructure and on the softer environmental factors that create a school climate that is\nconducive for learning, such as peer and teacher modes of communication and school values.\n\n\n37. The third Result Area is **_a reformed student assessment and certification system_** that will focus\non strengthening the MOE’s ability to measure and monitor student learning at all grade levels and to\nbridge the gap between learning and certification. This notably includes the reform of _Tawjihi_ and the\ninstitutionalization of an early grade diagnostic learning assessment.\n\n\n38. The fourth Result Area is **_strengthened education system_** **_management_** by focusing on\nsupporting MOE and strengthening its capacity to manage an increasing number of schools and\nstudents, notably due to the expansion of early childhood education and to the enrollment of a large\nnumber of refugee children in Jordanian schools. The focus of this Result Area is to provide and\nenhance the tools and resources available to the MOE for decision making and implementation. These\ntools include information systems such as the operationalization of the GIS, which will allow the MOE\nto map school construction, expansion, and rehabilitation needs, and the strengthening of the existing\nOpenEMIS to allow MOE to analyze and make use of disaggregated and gender‐sensitive data for\ndecision making. This Result Area will also support the MOE in securing budget additionality to the\nsector in an efficient and effective manner to ensure that resources are available for undertaking the\nnecessary reforms.\n\n\n19 In an effort to shed light on gender dynamics in the education sector in Jordan, the impact evaluation will assess\nheterogeneous effects", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000041:18:1:0", "start": 1667, "end": 1675, "surface": "OpenEMIS", "probe_tag": "drop", "probe_score": 0.0023, "luna_label": 1}, {"key": "sample:jdc_operational:000041:18:1:1", "start": 1716, "end": 1755, "surface": "disaggregated and gender‐sensitive data", "probe_tag": "confusion", "probe_score": 0.303, "luna_label": 0}]}, {"key": "aj-155", "text": "\nprovided the bulk of assistance to refugees, IDPs, and to a lesser extent to host communities. The\nUS Government and ECHO are also providing substantial additional funding for humanitarian\ninterventions through NGOs.\n\n\n17. **To provide targeted support to poor and vulnerable Chadians, the Government has**\n**taken steps to develop a safety net system that is also suitable for the inclusion of refugees.**\nUnder the World Bank/Multi-Donor Trust Fund (MDTF)- funded PFS, the Government of Chad\nestablished the _Cellule Filets Sociaux_ (CFS) in 2016 to manage its safety net programs, particularly\ncash transfers and cash-for-work schemes. A Unified Social Registry (USR) is also being\ndeveloped, with the aim of combining information from selected social programs funded by the\nGovernment and external partners into a single database. The CFS is implementing the project\nusing a flexible approach to identification, targeting and registration of poor and vulnerable\nhouseholds. The objective is to have in place a highly adaptable system that can be scaled up to\nrespond to urgent situations, such a sudden inflow of refugees, that impacts host communities. <sup>18</sup> [^18: As part of the combined efforts to assist the Government in building a shock-responsive social protection system, many WFP,\nECHO and UNHCR partners (NGOs) are using the harmonized questionnaire during the lean season. The harmonized questionnaire\nwas introduced by Government Decree 038/PR/PM/MEPD/SE/SG/DGEP/2017 dated September 23, 2017 and it is the first step\ntoward building a Unified Social Registry (USR). Currently the Government, through the _Cellule Filets Sociaux_, is moving towards\nfinalizing the USR manual and procuring all necessary hardware (servers, mainframes) and software to establish the registry. It is\nenvisaged that a USR unit will eventually be created within the Government.]\n\n\n17 The 2017-2021 National Development Plan (NDP) is the Government of Chad’s first five-year strategy. It aims at supporting the\nGovernment’s longer-term development", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000035:17:1:0", "start": 642, "end": 665, "surface": "Unified Social Registry", "probe_tag": "drop", "probe_score": 0.009, "luna_label": 0}]}, {"key": "aj-156", "text": "**The World Bank**\nUganda: Roads and Bridges in the Refugee Hosting Districts Project (P171339)\n\n\nmanagement; (iv) training on FIDIC contracts and contract management in general; (v) recruitment and\ndeployment of adequate staff <sup>61</sup> [^61: hiring of specialists including an environmental specialist, a social development specialist, a valuer, and two road inspectors] to ensure sufficient inhouse technical capacity to support the bidding\nprocess and to supervise the contractors and consultants; (vi) to enhance the Environment and Social\nSafeguards compliance on site, the Resident Engineer shall have knowledge on Environment and Social\nsafeguards and the Contractors must present Environmental and social safeguards staff that meet minimum\nrequirements before works are allowed to commence; (vii) preparation of contract management plans; (viii)\ntimely provision of funds for compensating PAPs and for maintenance; (ix) at least 40 percent of the\ncontinuous section of the corridor to be acquired before award of works; (x) engagement of a short term\nConsultant Procurement Specialist proficient in the World Bank procedures and with ToR acceptable to the\nWorld Bank to support the processing of these contracts; (xi) UNRA has put in place a peer review\nmechanism to ensure completeness of the evaluation criteria; and (xii) put in place a procurement\ncomplaints records system.\n\n\n~~.~~ **<u>C. Legal Operational Policies</u>**\n\n\n\n\n\n**D. Environmental and Social**\n\n\n108. **Management of Environmental and Social Aspects.** The proposed 105 km-long Koboko-Moyo-Yumbe road\nupgrade will entail construction of a 7.0 m carriageway (2 x 3.5m lanes), including key features such as shoulders,\nservice and parking lanes, raised paved walkways (including the covered drain) in the major towns of Koboko,\nYumbe and Moyo; and in the minor towns of Lodonga and Kuru, bus lay bays in larger locations with high levels\nof public activity, replacement of existing cross drainage structures", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000050:45:0:0", "start": 1353, "end": 1390, "surface": "procurement\ncomplaints records system", "probe_tag": "drop", "probe_score": 0.0489, "luna_label": 0}]}, {"key": "aj-157", "text": "**The World Bank**\nUganda: Roads and Bridges in the Refugee Hosting Districts Project (P171339)\n\n\nbuses, motorbikes, cars, and trucks (modes used by refugees/hosts and for trade)\n(iii) Time of closure of Project road corridor in a year for movement of trucks (days)\n(iv) Percentage increase in trade volumes using the Project road (dis-aggregated by refugees, hosts)\n\n(b) enhance the capacity of UNRA to manage environmental, social and road safety risks\n\n(v) Fully operational Environmental and Social Management System in place\n(vi) Crash data entered into system, publicly reported, and used in decision making (yes/no)\n(vii) Annual Number of fatalities or serious injuries involving construction vehicles or at construction\n\nsites\n\n\n**B. Project Components**\n\n\n43. The project will have the following components as detailed below.\n\n\n44. **Component 1: Road Upgrading Works (Total US$145.8 million; IDA: US$125.8 million equivalent, GoU:**\n**US$20 million).**\n\n\n1(a) Upgrading - to bituminous paved road standard - and widening of about 105 km of KobokoYumbe-Moyo road corridor.\n1(b) Carrying out supervision of civil works under part 1(a) of the Project.\n1(c) Carrying out environmental and social risks management (including implementation of action\nplans to address among others gender-based violence, sexual exploitation and abuse, violence\nagainst children and HIV/AIDS), monitoring and evaluation, third party integrated performance\naudits and road user satisfaction surveys.\n1(d) Preparation of Project’s environmental and social risk management documents as well as\ndetailed engineering designs of civil works and carrying out land acquisition, and resettlement\nand rehabilitation associated with upgrading works under Part 1(a) and maintenance of the road\ncorridor for five years post-construction.\n\n45. This", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000050:25:0:0", "start": 535, "end": 545, "surface": "Crash data", "probe_tag": "drop", "probe_score": 0.0446, "luna_label": 0}, {"key": "jdc_operational:000050:25:0:1", "start": 1453, "end": 1483, "surface": "road user satisfaction surveys", "probe_tag": "drop", "probe_score": 0.0463, "luna_label": 0}]}, {"key": "aj-158", "text": " tools being used. MoITS will utilize\nthe functionalities available in their Oracle database and accounting module when recording transactions\nrelated to the Project operations, by creating a separate cost center for the Project which is used for\nrecording the day-to-day transactions and large contract purchases under both components, the FO might\nalso use excel sheet in support of the Oracle system to prepare Bank required reports such as the WA-IFRs\nand the Semiannual IFRs. Furthermore, the assigned FO will work on improving the automated linkage\nbetween the MoITS’ Oracle based accounting system and the IFRs to be produced for the Bank purposes on\nexcel spread sheets.\n\n\n**9.** **Financial Section of the POM** : MoITS will develop the FM section of the POM used in the project\nwhich will cover all administrative, financial, and accounting, budgetary, and human resources procedures\nrelevant to the additional activities to be financed under the project. The POM should describe the payment\nprocedures, including controls and oversight arrangements. A POM acceptable to the Bank should be\nsubmitted within two months after project effectiveness date.\n\n**10.** **Training and Implementation Support.** The World Bank team will intensively supervise the project,\nparticularly early in implementation, and will provide adequate training to the FO to ensure full\nunderstanding and application of the World Bank financial management and disbursement Policies and\nProcedures.\n\n**11.** **Financial Reporting and Monitoring** : MoITS will work very closely with MOPIC and MOF, although\nfunds disbursements and WA will be vested with MOF and MOPIC, MoITS will be solely responsible for: (i)\nconsolidating the loan financial data; (ii) preparing activity budgets (disbursement plan) quarterly as well as\nannually, monthly DA reconciliation statements, and periodic Statements of Expenditures (SOEs) (if needed),\nwithdrawal schedule for approval, semi-annual IFRs (", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000024:51:1:0", "start": 1711, "end": 1730, "surface": "loan financial data", "probe_tag": "drop", "probe_score": 0.0326, "luna_label": 0}]}, {"key": "aj-159", "text": "**The World Bank**\nRoads and Employment Project (P160223)\n\n\n**ANNEX 2: IMPLEMENTATION ARRANGEMENTS**\n\n\n**COUNTRY : Lebanon**\n**Roads and Employment Project**\n\n**Project Institutional and Implementation Arrangements**\n\n\n1. The project implementation entity is CDR. All technical, fiduciary, safeguards, and monitoring\naspects will be executed directly by CDR therefore avoiding the complication of multiple agencies’\nimplementation. CDR has a long and well established cooperation with the World Bank and other\ndonors, and its performance at project implementation has been generally satisfactory. CDR will,\nhowever, ensure coordination with the relevant government agencies, particularly the MPWT,\nregarding the priorities, technical aspects, and project requirements. The selection of priority\nroads for the project will be undertaken in consultation with MPWT based on the results of the\nvisual survey and the agreed criteria. MPWT will also identify and submit its needs for emergency\nequipment and desired technical specifications to CDR who will undertake the procurement of\nsuch equipment. The SNRSC will inform CDR about its capacity building needs and will draft and\nreview terms of Reference for the required services, with Bank support, before CDR proceeds with\nthe procurement of such services. The same road asset management system will also be installed\nwithin both CDR and MPWT and will involve the joint participation and training of MPWT and CDR\nstaff on the utilization of the new system, therefore reinforcing sustainability given both MPWT\nand CDR active responsibilities in the road sector in Lebanon.\n\n\n2. To ensure further coordination and capacity building, two road engineers from MPWT (one from\nplanning and one from maintenance) will be primarily dedicated to support CDR in the\nimplementation of the project, providing day‐to‐day on‐the‐job learning. The project will also\nfund one road engineer, through the PIU, who will be housed within MPWT working with the\nGeneral Director for Roads and Buildings. World Bank experts", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000008:59:0:0", "start": 890, "end": 903, "surface": "visual survey", "probe_tag": "drop", "probe_score": 0.0211, "luna_label": 1}]}, {"key": "aj-160", "text": "**The World Bank**\nLebanon Health Resilience Project (P163476)\n\n\n48. **The MoPH, through the PMU’s two coordinators (PHCC and hospital), will be responsible for**\n**monitoring the daily progress of the project,** focusing on improved accessibility of beneficiaries to the\npackage of services, proper procurement, and capacity building of hospitals. The PMU will be\nresponsible for preparing and submitting semiannual progress reports that, among other things, provide\ndetailed reporting on services, procurement, and expenditures. It will also conduct mid-term and postcompletion evaluations to gauge progress toward the PDO and assess the impact of the project on\ntargeted beneficiaries.\n\n49. **The HIS system developed by the MoPH will be further refined and expanded under the**\n**project to all newly enrolled PHCCs to support the implementation and monitoring of the program** .\nData will be collected and used to: (i) supervise the performance of PHCCs; (ii) monitor the progress of\nbeneficiary accessibility; (iii) monitor hospital improvements; and (iv) improve the provision of services\non the basis of intermediate output and outcome data. The data will be verified directly by MoPH\nsupervisory systems and external evaluation, and indirectly through triangulation with other data\nsources such as hospital claims.\n\n50. **Beneficiary feedback and grievance redress mechanisms will also play an important role in**\n**monitoring the project.** The EPHRP made significant progress toward establishing grievance redress\nmechanisms at the central and facility levels. This project will continue to strengthen the system by\nsupporting the MoPH hotline and finalizing the automated Grievance Module to create one platform\nthat integrates registration databases from the different sources to track and manage grievances. This\nwill provide the MoPH with timely access to grievance data to address grievances.\n\n\n51. **The WB will conduct regular implementation support missions** during which implementation\nprogress,", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000032:28:0:0", "start": 1112, "end": 1148, "surface": "intermediate output and outcome data", "probe_tag": "drop", "probe_score": 0.0463, "luna_label": 0}, {"key": "jdc_operational:000032:28:0:1", "start": 1307, "end": 1322, "surface": "hospital claims", "probe_tag": "confusion", "probe_score": 0.2578, "luna_label": 1}, {"key": "jdc_operational:000032:28:0:2", "start": 1740, "end": 1762, "surface": "registration databases", "probe_tag": "confusion", "probe_score": 0.42, "luna_label": 1}]}, {"key": "aj-161", "text": "prolonged destruction and devastation caused by the civil war and prolonged conflict. The system\nfaced major bottlenecks, both technical and institutional in nature as well as large gaps and\nweaknesses in the quality of data. Even when data exists, they are not shared and are not conducive\nto policy analysis and evidence based decision making. As a result, the SMP concluded among\nother things, that there is a need to strengthen the capacities of the line ministries to provide better\ndata for informed policy on the key economic and poverty analysis issues by improving the\ninformation database and the staff analytical skills.\n\nToday, the weak statistical system continues to persist, both at the central level as well as at sector\nministries➢❨ level. The Ministry of Public Health (MoPH) is no exception. The MoPH statistical\nsystem has limited capacity to generate adequate flow of data to support decision making towards\nachieving equity and efficiency in the health sector, largely due to lack of personnel and a\nfragmented information system. As such, the MoPH is requesting Bank➢❨ s assistance to\nstrengthen the capacity of the Statistics Department (SD) by putting in place an effective and\nefficient statistical system that will foster evidence-based policy making and effective monitoring of\nthe health sector.\n\n\n**Relationship to CAS/CPS/CPF**\nData availability and access to information were identified in the 2015 SCD as foundational\nconstraints that impact evidence-based policy, and stands in the way of an informed population in\nLebanon. The SCD identifies deficiencies in the timeliness of data, its reliability due to weak overall\nstatistical capacity, and inadequate data coverage in key areas such as poverty, income distribution,\nand economic measurements. The SCD further defines data availability as one of the cross-cutting\nareas for the program.\n\nConsequently, the Lebanon CPF (FY2016-2021) recognizes the need for strengthening data\ncollection and analysis to improve the debate on policy reforms. Accordingly, the objectives of the\nCPF are underpinned by the cross-cutting theme of governance", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000046:1:0:0", "start": 578, "end": 598, "surface": "information database", "probe_tag": "drop", "probe_score": 0.0057, "luna_label": 0}]}, {"key": "aj-162", "text": "147,500 (indirect)<br>|\n|<br>**Sub-component 1A: **<br>**Supporting creation**<br>**and strengthening of **<br>**women platforms,**<br>**community**<br>**mobilization, and **<br>**mindset change**(IDA <br>US$5 million, including<br>WHR US$450,000) <br>|<br>• Mobilization of women and girls in<br>target districts to establish<br>platforms at district level of new<br>and existing women entrepreneurs.<br>• Setting up a national digital<br>platform for women entrepreneurs<br>• Setting up a database of women-<br>owned/managed businesses<br>• Communication and outreach<br>campaign<br>• Service provider is contracted to<br>conduct sessions on social<br>norms/women safety<br>• Advocacy on policy issues impacting<br>women entrepreneurs|• Women and adolescent<br>girls<br>• Existing women<br>entrepreneurs<br>• Refugee women<br>• Men, male partners,<br>community leaders<br>benefiting from<br>participating in behavior<br>change interventions.<br>•  Women business leaders|150,000 women and<br>adolescent girls<br> <br>Estimated 1,147,500 men,<br>male partners, communities,<br>and household members<br>indirectly benefit from<br>platform and communication<br>campaign<br>|\n\n\n\nPage 54 of 77", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000025:58:2:0", "start": 491, "end": 508, "surface": "database of women", "probe_tag": "drop", "probe_score": 0.0219, "luna_label": 0}]}, {"key": "aj-163", "text": "br>Partner<br>|\n|Number of beneficiary households<br>receiving cash transfer for participating in<br>the behavioral change communication<br>training|The number of beneficiary<br>households that participate<br>in behavioral change<br>communication training<br>activities to receive their<br>cash transfer.|This<br>indicator<br>will be<br>measured,<br>at a<br>minimum,<br>on a<br>quarterly<br>basis<br>|SNSOP<br>Management<br>Information<br>System<br>|Attendance data<br>collected during each<br>training session<br>|Implementing Partner<br>|\n|Number of beneficiary households<br>receiving Direct Income Support|The number of total<br>beneficiary HHs that are<br>selected to participate in DIS<br>under sub-component 1.2,<br>in accordance with the<br>Project Operations Manual,|<br>This<br>indicator<br>will be<br>measured,<br>at a<br>minimum,|Registration<br>and payment<br>data from<br>the SNSOP<br>MIS<br>|Beneficiary data will be<br>collected during<br>registration and<br>updated over the<br>course of project<br>implementation.|Selected Implementing<br>Partner<br>|\n\n\nPage 56 of 74", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000057:60:1:0", "start": 450, "end": 465, "surface": "Attendance data", "probe_tag": "drop", "probe_score": 0.0093, "luna_label": 0}, {"key": "jdc_operational:000057:60:1:1", "start": 907, "end": 923, "surface": "Beneficiary data", "probe_tag": "confusion", "probe_score": 0.0994, "luna_label": 0}]}, {"key": "aj-164", "text": " has ratified the 1951 Refugee Convention\nand the 1967 Protocol Relating to the Status of Refugees and nine international and regional human rights\ninstruments relevant to refugee protection. These are domesticated into Uganda’s legal system through\n\n\n14 Based on the Uganda Refugee Protection Assessment Update August 4-22, 2022.\n\n\nPage 11 of 81", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000001:16:2:0", "start": 268, "end": 311, "surface": "Uganda Refugee Protection Assessment Update", "probe_tag": "keep", "probe_score": 0.9477, "luna_label": 1}]}, {"key": "aj-165", "text": "3\n\n\n**2. Project development objective** (see Annex 1)\n\nThe objective of the project is to enhance the quality of education and to increase enrollment in\nprimary schools.\n\n\n**3. Key performance indicators:** (see Annex 1)\n\nThe key performance indicator is an increased number of students enrolled in grades 1-9, especially\namong girls.\n\n\nB. **STRATEGIC CONTEXT**\n\n\n**1. Sector-related Country Assistance Strategy (CAS) goal supported by the project** (see Annex 1)\n\n\n**Document number:** P 7403 DJI **Date of latest CAS discussion:** (scheduled for) 12/19/00\n\nThe CAS has been prepared in the context of the country's economic difficulties and deepening\npoverty. Despite Djibouti's relatively high nominal per capita income (US$790 versus an average of\nUS$510 for Sub-Saharan Africa, and US$100 for Ethiopia), Djibouti has one of the poorest social\nindicators in the world (poverty, illiteracy, maternal and infant mortality, and morbidity), according to\nthe UNDP Human Development Index, ranking 157th among 174 countries.\n\n\nThe Republic of Djibouti has very few natural resources and the economy is mainly dependent on the\nport, external financial assistance, the French military and associated services. However, with the\n\ndecreased amount of external assistance, and deepening structural problems, the country has suffered\neconomic stagnation over the past decade and a half. As a result, per capita Gross Domestic Product\n(GDP) declined by 50% in real terms since 1985. The switch of Ethiopia's transit traffic from Assab in\nEritrea to Djibouti in mid-1998 and the consequent four-fold increase in port traffic has opened new,\nas yet not fully exploited, opportunities for investment and growth. Djibouti's open economic policies\nand relative stability, characterized by a liberal trade policy and exchange system, which operates free\n\nof capital or", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000093:6:0:0", "start": 959, "end": 987, "surface": "UNDP Human Development Index", "probe_tag": "keep", "probe_score": 0.9623, "luna_label": 1}]}, {"key": "aj-166", "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_aj", "spans": [{"key": "refugee_pads:000041:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1}, {"key": "refugee_pads:000041:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1}, {"key": "refugee_pads:000041:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1}]}, {"key": "aj-167", "text": "provement in service level has been spatially unequal across the urban and rural continuum**\n**and regions.** According to the Tajikistan JMP data, <sup>18</sup> [^18: JMP and TajStat data (2020). Assuming a total population of 9,314 million, of which 3,672 million are urban and 5,643 million\nare rural.] 95.00 percent of the urban population, and 76.64\npercent of the rural population, have access to basic water supply services. However, this level of service\nis not recognized by the GoT as safe and compliant with standards for drinking water. In rural Tajikistan,\naccess to piped water supply services remains extremely low at 55.50 percent, with only 48.77 percent of\nrural population qualifying their services as safely managed. Households with access to piped water also\nsuffer outages and are unsure of the quality of the supplied drinking water. The gap between the urban\nand rural areas has been narrowing since 2010, with the role of groundwater becoming more prominent\nas many communities continue investing in development of private wells. Reliable data on water wells\nused for water supply to population are not available, despite the large pressure on underground water\nresources, as these wells are not appropriately registered and regulated. No consolidated information\nexists on distribution of population by water use from different water sources at the national level,\nresulting in underestimation of the share of population relying on springs, rivers, canals and ditches,\nshallow wells, rainwater harvesting, and unregulated water trucking services. The household survey data\nseries <sup>19</sup> [^19: World Bank. 2017.. _Glass Half Full: Poverty Diagnostic of Water Supply, Sanitation, and Hygiene Conditions in Tajikistan._] demonstrate significant variations across regions and across time. In 2017, the gap was at 32\npercent—with Dushanbe at the top and Khatlon province at the bottom of the list. Except for Khatlon\nprovince, surface water has", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000162:12:1:1", "start": 1577, "end": 1605, "surface": "household survey data\nseries", "probe_tag": "keep", "probe_score": 0.9074, "luna_label": 1}]}, {"key": "aj-168", "text": " of books and materials. They prefer to use\ntheir constrained resources for their boys who they feel have a better labor market potential. Finally,\nthe data from the Household Survey, showed that even if girls go to school, their parents pull them\nout at an age when they think they can help around the household.", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000062:38:2:0", "start": 166, "end": 182, "surface": "Household Survey", "probe_tag": "keep", "probe_score": 0.966, "luna_label": 1}]}, {"key": "aj-169", "text": " surroundings are safe; the Southern\nstates are considered safer than the north. In addition, a lack of clarity regarding responsibilities and governance\nstructures for the design and implementation of protocols for responding to sexual harassment in public transport\nlimits the possibility of appropriately responding to survivors. Sexual harassment constitutes a barrier to gender\nequality, and directly impacts women’s access to economic opportunities. According to data from the International\nLabor Organization, a lack of safe transport reduces the probability of women accessing economic opportunities by\n16.5 percent.\n\n\n15 In 2020, the regional rate was 18.7 fatalities per 100,000 inhabitants, which is higher than the national rate (15.5) (DATASUS data).\n16 Looking at the formal jobs of the Region, 69 percent of the people that receive more than ten minimum wages, on average, are men. Meanwhile,\n94 percent of indigenous or black women receive less than 3 minimum wages. Data Source: RAIS, 2019.\n17 Gender-disaggregated mobility data for Foz do Río Itajaí were collected during the preparation of the Stakeholder Engagement Plan and other\nproject-related documents.\n18 Data derived from PNAD-Contínua (2019) State of Santa Catarina.\n19 Haydée Svab, Marina Kohler Harkot, and Beatriz Moura Dos Santos, _A Baseline Study of Gender and Transport in Sao Paulo, Brazil: Present Initiatives_\n_to Improve Women’s Mobility (English)_ (Washington, DC: World Bank, 2021).\n20 _Brazil - Improving Mobility and Urban Inclusion in the Amazonas Corridor in Belo Horizonte Project_ (Washington, DC: World Bank).\n21 Data reflect cases of harassment in public spaces without disaggregation. However, given the trend in Brazil and the Region, it can be inferred\n<u>that in the Santa Catarina Region, more women are also survivors of sexual harassment.</u>\n\n\nPage 11 of 77", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000182:15:2:0", "start": 469, "end": 515, "surface": "data from the International\nLabor Organization", "probe_tag": "keep", "probe_score": 0.9798, "luna_label": 1}, {"key": "sample:refugee_pads:000182:15:2:1", "start": 749, "end": 761, "surface": "DATASUS data", "probe_tag": "keep", "probe_score": 0.9555, "luna_label": 1}, {"key": "sample:refugee_pads:000182:15:2:2", "start": 996, "end": 1000, "surface": "RAIS", "probe_tag": "keep", "probe_score": 0.9978, "luna_label": 1}, {"key": "sample:refugee_pads:000182:15:2:3", "start": 1011, "end": 1067, "surface": "Gender-disaggregated mobility data for Foz do Río Itajaí", "probe_tag": "keep", "probe_score": 0.9579, "luna_label": 0}, {"key": "sample:refugee_pads:000182:15:2:4", "start": 1199, "end": 1212, "surface": "PNAD-Contínua", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1}]}, {"key": "aj-170", "text": "**The World Bank**\nAfghanistan: Eshteghal Zaiee - Karmondena (EZ-Kar) (P166127)\n\n\n\n|Col1|Col2|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|% of CDC members who are women|Number of women in the<br>CDCs established under the<br>project, divided by the total<br>number of members of the<br>CDCs established under the<br>project.|Semi‐<br>annually<br>|CDC profiles<br>|Data recorded in CDC<br>profiles.<br>|IDLG‐CCAP PIU with FPs.<br>|\n|Number of municipalities where IDLG<br>holds EZ‐Kar coordination meetings with<br>government authorities, Gozar assemblies<br>and Business Gozar assemblies|Number of cities that have<br>held meetings with EZ‐Kar<br>stakeholders in the cities<br>where Gozar and Business<br>Gozar Grants are<br>implemented.|Semi‐<br>annually<br>|Minutes of<br>meetings<br>|Aggregated number of<br>meetings reported by<br>the municipalities,<br>verified with minutes of<br>meetings.<br>|ID", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000123:44:0:0", "start": 351, "end": 363, "surface": "CDC profiles", "probe_tag": "confusion", "probe_score": 0.876, "luna_label": 0}]}, {"key": "aj-171", "text": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n\n74. **Safeguards Management Approach and Capacity** : An Environmental and Social Commitment Plan (ESCP)\nhas been prepared and disclosed on April 23, 2020. Since this project is prepared under emergency procedures\n(Investment Project Financing Policy Paragraph 12), the ESCP outlines the commitment by the Client to update\nthe ESMF of the Sudan Basic Education Support Project and finalize after project approval. A labor management\nplan has been prepared and disclosed on April 3 2020. The MoE will continue to serve as the implementation\nagency for this project. Within the MoE, there is an existing PCU which will hold responsibility for carrying day-today implementation of project activities. The PCU is supported a social mobilization and grass-roots capacity\nbuilding/school grant coordinator and a safeguards specialist to carry out environmental and social safeguards\nimplementation, monitoring and reporting respectively. National institutional capacity is thus strong. The PCU has\na history of engaging with State Ministries and local communities to build capacity, and there is a component in\nthe project dedicated to funding this, especially for the new States being added.\n\n75. **Stakeholder Engagement and Information Disclosure -** The project has prepared a stakeholder\nengagement plan based on the findings of a stakeholder mapping. This plan was disclosed in country on April 23,\n2020 and on the World Bank website on April 30, 2020. There will be continuous stakeholder engagement by the\nMoE, State Education Offices, implementing entities; such as, partner non-government organizations. The project\nsocial mobilizers will closely work with the school level Parent Teacher Association (PTA) in the process of\nstakeholder engagement and community consultation.\n\n76. **Grievance Redress Mechanism:** in Sudan, customary institutions including community development\ncommittees are responsible for managing community grievances. In case of grievances and disputes the\ncommunities/tribes typically settle", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000024:29:0:0", "start": 1409, "end": 1428, "surface": "stakeholder mapping", "probe_tag": "confusion", "probe_score": 0.1062, "luna_label": 1}]}, {"key": "aj-172", "text": " Institutional Context**\n\n4. **Costa Rica’s spending on education as percentage of GDP has consistently been high compared to other**\n**countries, and educational indicators are amongst the best in LAC – but can still be improved** . The literacy rate for adults\naged 15 and older is 98 percent, and the share of adults aged 15 and above who had no formal education has declined\nfrom 19.2 percent in 1950 to a projected 1.8 percent in 2020. <sup>8</sup> [^8: Source: https://ourworldindata.org/] Younger cohorts are also attaining more years of education;\n\n\n[1 Source: Macro Poverty Outlook for Costa Rica : April 2024;](https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099614304042426555/idu19b4ebfe510a6314e0a1a84e11572f3e0cd22)\n2 Source: World Development Indicators (WDI) <u>[https://data.worldbank.org/indicator/NE.TRD.GNFS.ZS?locations=CR](https://data.worldbank.org/indicator/NE.TRD.GNFS.ZS?locations=CR)</u>\n3 Source: World Economic Outlook (WEO), October 2023, <u>[https://www.imf.org/external/datamapper/LUR@WEO/CRI?zoom=CRI&highlight=CRI](https://www.imf.org/external/datamapper/LUR@WEO/CRI?zoom=CRI&highlight=CRI)</u>\n4 World Bank estimates using administrative records and annual statistical reports from the Directorate General of Migrants and Foreigners.\n<u>[https://www.", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000189:10:2:0", "start": 569, "end": 605, "surface": "Macro Poverty Outlook for Costa Rica", "probe_tag": "keep", "probe_score": 0.9369, "luna_label": 1}, {"key": "refugee_pads:000189:10:2:1", "start": 1190, "end": 1243, "surface": "administrative records and annual statistical reports", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}]}, {"key": "aj-173", "text": " OP 4.12 will be laid down to ensure that the different communities and caste groups\nrelocated within new housing schemes will have equal access to the community infrastructure and public\nutilities.\n\n\n_Concerns on Social Impacts and Mitigation Measures and Mitigation Measures_\nIn addition to the safeguards issues, a number - f concerns were raised during the pilot Social Impact\nAssessment. Areas of concern included targeting, land, role o f Village Rehabilitation Committees and\nPartner Organizations, Grievance Redressal, Communications Campaign, Continued Social Impact\nAssessment, Gender Equity and Village Social Profile. These concerns have been largely addressed in\nthe design o f the program and are detailed in Annexes 3, 4 and **6** - except for the Village Social Profile\nwhich i s detailed at the end o f this annex.\n\n\nA land survey was completed in relation to the housing reconstruction program. The objective o f the land\nsurvey was to identify major issues that might constrain legitimate beneficiaries accessing the program.\nThe survey was intended in part to offer guidelines to resolve war related property disputes and formulate\nan action plan to deal with administratively andor legally disputed properties o f the IDPs.\n\n\nThe ownership o f land i s contested for several reasons that include:\n\n\nLoss o f deeds and other land transaction documents in the district land registry due to war\nUnauthorised occupancy o f state and private land due to displacement\nThe issue o f forged deeds\nPeople contesting a deed transfer sold under duress\n##### 9 .\n\n\n_73_", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000115:76:1:1", "start": 935, "end": 946, "surface": "land\nsurvey", "probe_tag": "confusion", "probe_score": 0.4041, "luna_label": 1}]}, {"key": "aj-174", "text": " mitigation, and sustainability of investments in infrastructure. Procedures and\nprotocols for the new schools will integrate consideration of safe egress for vulnerable students and\nothers who may require additional assistance. Civil works that might cause land acquisition or involuntary\nresettlement will not be eligible for financing under the project.\n\n38. Infrastructure development will focus on safe, inclusive, sustainable, and resilient facilities design,\naligned with the learning process, and developed based on applicable best practices, with a focus on\ngreater energy and water efficiency, use of renewable energy, water recycling and harvesting, waste\nminimization, durability, resilience to climate-induced hazards, and adherence to the national and\nregional circumstances and climate objectives communicated through Moldova’s NDC. Activities under\nthis subcomponent will support Pillar 3 of the GCRF (Strengthening Resilience) through incorporating\nclimate-smart and resilient physical and digital infrastructure and providing support to strengthening\nsector resilience to withstand pandemic-like shocks. It will also finance activities aligned with Pillar 4\nfocusing on strengthening policies, institutions, and investments for rebuilding better.\n\n**Component 3: Strengthening the Capacity for Education Sector Management and Refugee Response**\n**(US$6.7 million IBRD)**\n\n39. This component will finance activities aimed to strengthen capacity of the MoER for education\nsector management and refugee response in Moldova. This component will support improving planning,\nmanagement, and evaluation of education reforms through, inter alia: (i) upgrading and expanding the\nexisting EMIS for making better use of data to support management decisions at all levels; (ii)\n\n\n33 The childcare services will be expanded by extending the preschool day or in accordance with other government\nmechanisms/modalities that will allow the ECEC services to fulfill a childcare function.\n34 The inter-ministerial working group comprised of the members of the relevant line ministries, as well as with participation of\nthe development partners including the World Bank, has started its work in 2022 and coordinates the action plans and donors’", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000185:24:1:0", "start": 1697, "end": 1701, "surface": "EMIS", "probe_tag": "confusion", "probe_score": 0.1494, "luna_label": 0}]}, {"key": "aj-175", "text": "**_0_**\n\n\n**_0_**\n\n\n\nNet change in anemia levels (as measured through blood test indicators) of children 0-5 years old\n\n(target: 2-15%).\nBaseline data for children brought regularly to health centers for preventive care will be collected\nthrough the application process. M O H and UNRWA still have to confirm that the children\nbrought to health clinics to check nutritional status will have a general checkup; this i s not yet\nguaranteed but i s feasible should both agencies agree.\n\n\n\nI t can be presumed that the last three years of _intifada_ and closures have had a negative effect on the\nvaccination rates, though there has been no reliable data since 2001. Therefore, the only possibility\nfor the baseline would be to collect data from the beneficiary households. The risk i s that they might\nnot be able to provide exact records for their children. One possible solution would be to design a\nform for each child and let the households collect the missing data from the primary health care\n(PHC) clinics where immunization occurred. For this to occur, agreement would have to be reached\nbetween the PHC departments in M O H and UNRWA. The households also must be made aware that\nthey have to submit a vaccination booklet every time their child visits a health clinic. In so doing,\nvaccination monitoring would be possible during implementation vis-&vis nutrition checkups.\n(ii) <sup>**Education Grants.**</sup> <sup>The following outcome indicators will be used:</sup>\n\n\n\n**_0_**\n\n\n**_0_**\n\n\n**_0_**\n\n\n\nNet change in school attendance (target: 2-5%)\nNet change in school dropout (target: 24%)\nNet change in school enrollment (target: 1-5%)\nNet change in transition rate, especially grades TBD (target: > or", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000047:31:0:0", "start": 137, "end": 150, "surface": "Baseline data", "probe_tag": "confusion", "probe_score": 0.0529, "luna_label": 0}, {"key": "sample:refugee_pads:000047:31:0:1", "start": 732, "end": 768, "surface": "data from the beneficiary households", "probe_tag": "confusion", "probe_score": 0.313, "luna_label": 0}]}, {"key": "aj-176", "text": "Financial Management**\n\n69. **The project will be implemented by the** **_Cellule Filets Sociaux_** **, which is currently**\n**implementing the PFS** . Fiduciary compliance for that project is deemed satisfactory, and the\nunaudited interim financial reports (IFRs) for the project have been submitted on time and found\nacceptable. In addition, CFS has adequate and qualified staff to handle the financial management\n(FM) tasks of the proposed project, although some additional support will be required. Its current\nfinancial management team consists of a specialist on administrative and financial transactions\nand a senior accountant at the central level, as well as two assistant accountants at the regional\nlevel. CFS is also in the process of hiring an internal auditor.\n\n70. **For the proposed project, CFS will hire an additional accountant to be based in**\n**N’djamena.** She or he will perform day-to-day accounting activities and record transactions in the\ncentral MIS and database. In the new regional offices, assistant accountants will be hired to\nperform day-to-day financial transactions.\n\n\n33 A review of the emergency cash transfer project in Niger (Safety Nets Project, Niger P123399) shows that cash transfers were\nused primarily for better and more diverse food (67–90 percent), drinkable water (urban settings), reinvestment (animals) and debt\nrelief.\n34 Data collected in Sri Lanka on start-up costs for 2,019 urban microenterprises show that 10 percent of firms started with US$100\nor less and 23 percent with US$500 or less. Previous work on Mexican microenterprises, find a similar range of starting capital\nlevels for Mexican microenterprises, with many starting with low amounts. Several studies, including randomized experiments\nthat give one-time grants of US$100 or US$200 to microenterprise owners, find high", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000112:35:1:0", "start": 1375, "end": 1402, "surface": "Data collected in Sri Lanka", "probe_tag": "confusion", "probe_score": 0.8442, "luna_label": 1}]}, {"key": "aj-177", "text": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\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|Student enrollment in targeted schools|The enrollment will be<br>monitored through the<br>annual school census|Annual<br>|Annual<br>School<br>Census<br>|Census of schools key<br>data collected yearly<br>|Ministry of Education<br>and PCU<br>|\n|Girls enrolment in targeted schools<br>|Number of girls enrolled in<br>targeted schools|Annual<br>|Annual<br>School<br>Census<br>|Census of school key<br>data collected annually<br>|Ministry of Education<br>and PCU<br>|\n\n\n|Monitoring & Evaluation Plan: Intermediate Results Indicators|Col2|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Indicator Name **|**Definition/Description **|**Frequency **|**Datasource **|**Methodology for Data**<br>**Collection **|**Responsibility for Data", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000024:33:0:2", "start": 730, "end": 750, "surface": "Census of school key", "probe_tag": "confusion", "probe_score": 0.1335, "luna_label": 0}]}, {"key": "aj-178", "text": "\n(42). The two Hodhs together make up 50 percent of the needs. Following this initial list, a feasibility study was\nconducted as part of project preparation to verify parameters such as population size and technical feasibility\nand from this basis, the final list of infrastructures per region was determined.\n\n3. As for urban and semi-urban centers, SNDE expressed its specific needs of infrastructure rehabilitation\ninvestments which cover 19 semi-urban centers (localities of more than 5,000 inhabitants), formerly managed\nby ONSER but which were transferred to SNDE, following the multiple failures of ONSER management. Seven\nof these centers will benefit from the project through rehabilitation and expansion, after which they will be\ndelegated to private operators.\n\n4. The activities related to sanitation were developed based on the needs expressed by the DA, in\nconsultation with the Ministries of Health and Education. According to the data presented by DA, 1,746 of the\n2,283 schools surveyed do not currently have latrines (256 in Assaba, 303 in Gorgol, 193 in Guidimakha, 500 in\nHodh Ech Echargui and 494 in Hodh El Gharbi). With regard to health centers, the DA indicated the latrine\nrequirements for 25 centers in Assaba, none in Gorgol, 20 in Guidimakha, 86 in Hodh Ech Echargui and 53 in\nHodh El Gharbi.\n\n5. The selection of water and sanitation activities in the M’Bera refugee camp is based on assessments of the\nexisting facilities. A diagnostic report of water and sanitation infrastructure, commissioned by UNHCR,\nprovided useful background information on the profile of the camp, its infrastructure and the surrounding\ncommunities. In addition, a World Bank team visited the refugee camp in March 2019 and in November 2019\nfor field assessments as part of project preparation. Both the report and the field visits helped to inform the\ndesign of the water and sanitation interventions in the", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000106:52:1:0", "start": 946, "end": 966, "surface": "data presented by DA", "probe_tag": "confusion", "probe_score": 0.3635, "luna_label": 1}, {"key": "refugee_pads:000106:52:1:1", "start": 1455, "end": 1511, "surface": "diagnostic report of water and sanitation infrastructure", "probe_tag": "confusion", "probe_score": 0.6706, "luna_label": 1}]}, {"key": "aj-179", "text": "\nUgandan research institutions will be a key partner in implementing the M&E framework building on existing data\nreports and in partnership with local research institutions. The PSFU PIU will work closely with Ugandan research\ninstitutions for three reasons. First, to coordinate the various data collections so as to be more efficient in utilizing\nall existing firm-level data. Second to identify and collect additional data with respect to jobs and firm productivity.\nThird, to use the data collected on an ongoing basis make suggestions to the PSC to improve the project impact. <sup>48</sup> [^48: The objective is to develop an actional M&E system that is used as instrument to monitor and improve project effectiveness along\nimplementation rather than just a system for ex-post accountability.]\nAll data will be disaggregated by gender, refugee, host community, and non-host community nations to ensure\nadequate targeting and collection of results for targeted populations.\n\n\n**C. Sustainability**\n\n\n79. In principle, the project components are based on implementing interventions that are fully financially\nsustainable while generating the largest possible impact. Although the rebates offered to borrowers under\nWindow 1 of Component 1 are not recoverable, from a cost-benefit perspective the extension of the amortization\nperiod of MSME loans is consistent with the BoU policy and will contribute to a reduced probability of default of\nMSMEs and to the stability of the financial sector by reducing NPLs and also to preserving functioning MSMEs and\njobs that might otherwise get impaired. It will also support revival of otherwise viable enterprises and therefore,\ncontribute to growth and to protecting institutional, social and financial capital, that might otherwise have been\nlost. Windows 2 and 3 of Component 1 are financially self-sustaining: Tier 3 and 4 institutions (MFDs, MFIs, and\nSACCOs) will be provided with access to concessional credit lines. These credit lines will be limited in volume and\nhave an 18-month maturity so as to be available over the COVID-19 period only and so as not to distort the market\nfor loan capital. All participants along the chain", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000073:36:1:0", "start": 362, "end": 377, "surface": "firm-level data", "probe_tag": "confusion", "probe_score": 0.3305, "luna_label": 1}]}, {"key": "aj-180", "text": "\noffices; (iii) pre-existing Beneficiary Development Program activities; and (iv) existence of available\nprivate, NGO, CSO and public training providers. In addition, some districts of these governorates have\nsignificant numbers of beneficiaries in groups E&F. Selecting these governorates would provide an\nopportunity to pilot the “phasing out strategy” should the government so choose.\n\n\n149. _Beneficiary Profiles in Pilot Governorates._ The PMT was applied to survey data gathered from\nexisting beneficiaries as well as new applicants. Based on the PMT, the beneficiary categories in the pilot\nareas are shown in **Table** **1.** Groups E&F make up 38 percent of the total, with the largest percentage (52\npercent) in Aden. Group A coverage is very small (approximately 1 percent) with Group B coverage for\nthe pilot area about 13 percent, with only 5 percent coverage of this group in Aden. Groups C&D make\nup the largest percentage (48 percent) of beneficiaries in the pilot area, comprising approximately 50\npercent of beneficiaries in both Hodeida (49 percent) and Mukalla (51 percent). The percentage of\nbeneficiaries in each group will be assessed over the duration of the pilot project to determine the impact\nof the SWF targeting policy implementation as well as the rolling out of the public information campaign.\n\n\n\n**Governorate**\n\n\n**Aden**\n\n\n**Hodeida**\n\n\n\n**cases** **total** **total** **total** **total** **total** **total**\n32908 43 1502 3646 10404 9443 7870\n\n’ (negligible) (5%) (11%) (32%) (28%) (24%)\n45605 605 7798 10502 11953 11476 327 1\n\n\n\n**Total** #", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000080:48:1:0", "start": 464, "end": 475, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.7517, "luna_label": 1}]}, {"key": "aj-181", "text": "|4,513<br>5,028|28,601<br>104,972|\n\n\n2. **The economic activity slow down caused by COVID-19 has affected Uganda’s ability to generate**\n**jobs for those living in vulnerable situations, including refugees and host communities.** Despite the\nconcerted efforts to integrate refugees within the ecosystems of their host communities, refugeehosting districts (RHDs) remain less developed areas. Low levels of disposable incomes have resulted\nin low demand and limited access to labor markets, leaving those residents with some access to land\nwith no alternative but to live off subsistence agriculture and humanitarian aid. These areas were less\ndeveloped even before the inflow of refugees and remain decoupled from resilient and viable supply\nchains in the economy. For example, the average value of assets among all households (both refugee\nand host) in the district of Arua <sup>64</sup> is 560,000 Ugandan shillings (US$ 144), which is only 10 percent of\ncomparable asset values in the Kampala region.\n\n\n62 Uganda Comprehensive Refugee Response Portal ( _[https://data2.unhcr.org/en/country/uga](https://data2.unhcr.org/en/country/uga)_ ) 31 October 2021\n63 Calculation based on district-level firm data from Census of Business Establishments (COBE), and refugee and host\ncommunity household data from the Refugee and Host Community Household Survey\n64 Arua was until recent sub-divisions of the district considered a refugee hosting district.\n\n\nPage 71 of 92", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000073:76:3:0", "start": 1211, "end": 1244, "surface": "Census of Business Establishments", "probe_tag": "keep", "probe_score": 0.9193, "luna_label": 1}, {"key": "refugee_pads:000073:76:3:1", "start": 1257, "end": 1298, "surface": "refugee and host\ncommunity household data", "probe_tag": "confusion", "probe_score": 0.8897, "luna_label": 1}]}, {"key": "aj-182", "text": "\nactivities which cover the running and human resources costs for CCI. <sup>27</sup> [^27: Data collected during technical sessions held virtually between World Bank task team and UNESCO team between October and December\n2021.] As a result, the livelihoods of these cultural\nworkers have been affected and there has been a decline in cultural production. Emergency support to CCI is urgently\nrequired to sustain the livelihoods of cultural actors and help reverse their exodus to recover and retain the vibrancy\nand creative identity of the affected neighborhoods.\n\n**9.** **The humanitarian phase has subsided, but recovery has not occurred proportional to the human and financial**\n**resources that were invested.** Commendable efforts have been put in place by different institutions (e.g.,\nGovernorate/Municipality; Forward Emergency Room, FER; Directorate General of Antiquities, DGA; and Public\nCorporation for Housing, PCH) to collaborate, support, and organize and the Civil Society responded extraordinarily in\nthe emergency phase. While domestic and national organizations have provided support to the housing and cultural\nsectors in the last year, severely damaged residential buildings have largely been left out of the rehabilitation process.\nIn addition, the multifaceted political, economic, and social challenges, coupled with market failures, have resulted in\nthe inability to address most vulnerable households and cultural entities to recover without support from the\ninternational community.\n\n**10.** **The lack of coordination has led to a fragmented response, reducing the overall effectiveness of interventions.**\nInstitutions were empowered to procure help directly from international organizations, but data sharing was not\nincentivized, so there was no systematic tracking of interventions. With no central body to coordinate, the recovery\nprocess was marred with a duplication of efforts and a piecemeal response. As such, the impact of the recovery effort\ndoes not equal the amount of resources, both human and financial that have been put into the various activities.\nMoreover, there has been no ownership", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000034:14:1:0", "start": 91, "end": 131, "surface": "Data collected during technical sessions", "probe_tag": "confusion", "probe_score": 0.3672, "luna_label": 0}]}, {"key": "aj-183", "text": "climate-informed local<br>development planning|The CDD app will furnish community mobilizers with local climate diagnostics at the commune<br>level. The climate diagnostic will consolidate existing data on socio-economic vulnerability,<br>climate fragility and hazard risks from the KMP - presented and/or visualized in a manner that<br>is adapted/simplified for local actors.  Beyond diagnostics, the app would also provide key<br>tasks to orient local actions that can support climate planning (i.e. mapping community<br>experiences with climate risks, facilitating climate screening of sub-project proposals,<br>providing illustrative list of potential investments given existing climate risks, etc.)<br>Based on the diagnostic and communities’ own knowledge and risk management strategies,<br>communities will prioritize climate smart investments or local climate actions with facilitation<br>and technical support of community facilitators. This will help incorporate climate<br>considerations into LDPs, which could include prioritizing adaptation activities (like flood<br>control structures) for expected changes in temperature, rainfall, storm surge, and sea level<br>rise.|\n|10.Engaging<br>communities in<br>collecting climate data<br>and monitoring climate<br>risks with the CDD<br>application|The CDD application will advance regional knowledge on climate change by supporting the<br>collection of local data on climate risks and indicators which will feed back into the KMP<br>database, to inform regional dialogue. With the support of community facilitators, the<br>community could be involved in participatory climate risk assessments to help identify,<br>estimate, map, and monitor climate change risks and other hazards. Community<br>representatives will also have the opportunity share local knowledge and experiences.|\n\n\n\nPage 69 of 72", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000116:73:2:0", "start": 1410, "end": 1452, "surface": "local data on climate risks and indicators", "probe_tag": "confusion", "probe_score": 0.2428, "luna_label": 0}]}, {"key": "aj-184", "text": "\naccordance with agreed financing percentages.\n\n54. The primary disbursement method will be advances. The Project will be also able to process reimbursements and\ndirect payments if required. Fund flow will rely on existing Consortium (i.e., Country) systems: the CIM-AMFRI will make\nall payments using the Bertha System once payment obligations have been committed and verified.\n\n\na. Funds will be transferred to a specific segregated bank account (designated account [DA]), which is to be\n\nopened specifically for the Project and administered by the CIM-AMFRI. This account will be opened at a\ncommercial bank acceptable to the World Bank (Banco do Brasil). The account will be denominated in Brazilian\nreais (BRL). Project payments to beneficiaries’ accounts will be transferred from the DA.\nb. The CIM-AMFRI will register payment processes in the Bertha System. The records will be reconciled at the\n\nend of each month.\n**c.** The CIM-AMFRI will prepare Statements of Expenditures (SOEs) using information available in the Bertha\n\nSystem. The SOEs will be supported by the accounting records. <sup>56</sup>\n\n55. The DA ceiling will be variable. The Minimum Application Size (MAS) for direct payment Withdrawal Applications\n(WAs) will be US$1,000,000 equivalent. The Eligible expenditures paid from the DA are to be presented at least once every\nsix months.\n\n56. The Project will report on the use of advances and process reimbursement requests through WAs supported by\nSOEs. Direct payments will be documented by records. The CIM-AMFRI will approve WAs documenting expenditures based\n\n\n56 The General Conditions require the Borrower to retain all records (contracts, orders, invoices, bills, receipts, and other documents) evidencing\neligible expenditures and to enable the Bank’s representatives to examine such records. They also require the records to be retained for at", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000182:56:1:0", "start": 306, "end": 319, "surface": "Bertha System", "probe_tag": "confusion", "probe_score": 0.5826, "luna_label": 0}, {"key": "sample:refugee_pads:000182:56:1:1", "start": 850, "end": 863, "surface": "Bertha System", "probe_tag": "confusion", "probe_score": 0.3259, "luna_label": 0}, {"key": "sample:refugee_pads:000182:56:1:2", "start": 1026, "end": 1040, "surface": "Bertha\n\nSystem", "probe_tag": "confusion", "probe_score": 0.3015, "luna_label": 0}, {"key": "sample:refugee_pads:000182:56:1:3", "start": 1076, "end": 1094, "surface": "accounting records", "probe_tag": "confusion", "probe_score": 0.3695, "luna_label": 0}]}, {"key": "aj-185", "text": "decentralized \"partnership\" approach to governance that involves line ministnes, NGOs, CBOs, <sup>and</sup> <sup>civil</sup>\nsociety. NCRRR/NaCSA has been supported by the African Development Bank, UNDP, DflD, and IDA.\nIt has provided assistance for more than 500,000 displaced persons, shelter rehabilitation, vocational\ntraining, trauma healing, and micro-finance programs. It has financed 275 community-based sub-projects\nin the areas of health, education, water and sanitation, agriculture and capacity building. These <sup>tasks</sup>\nhave been carried out with considerable support from the 260 NGOs registered in Sierra Leone, of which\n68 are international. These NGOs have been instrumental in ensuring service delivery to remote <sup>areas</sup>\nwhere public services were absent.\n\n\n**3.** **Sector** **issues** **to be** **addressed** **by the** **project and strategic choices:**\n\n**Poverty in a Post-Conflict** **Environment.** Section 2 above outlines the principal characteristics\nof poverty in Sierra Leone and the condition of the country at the close of the civil war. <sup>Access</sup> <sup>of the</sup>\npoor majority to food, shelter, employment opportunities and social services are among the <sup>principal</sup>\n\n\n\nconstraints to post-conflict reconstruction, economic recovery and poverty reduction. District recovery\nassessments reveal that over 340,000 houses were destroyed dunng the war and only 10,000 have been\nrebuilt so far. Government is particularly concerned with the shelter needs of returnees, IDPs <sup>and</sup>\n\n\n\ngovernment employees such as health workers and", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000176:9:0:0", "start": 1323, "end": 1352, "surface": "District recovery\nassessments", "probe_tag": "confusion", "probe_score": 0.2697, "luna_label": 1}]}, {"key": "aj-186", "text": "\nComponent 2).\n\n10. Quality of Care will be monitored and maintained through the MoPH PHCC accreditation\nprogram. The PHC accreditation program was initiated in 2009 by the MoPH in collaboration\nwith Accreditation Canada International (ACI). With support of local experts, the program was\ndeveloped, piloted and implemented in a phased approach in PHC network facilities. Currently,\n34 out of the 75 PHCCs are accredited and the other 42 are preparing for accreditation in 2015.\nMoreover, quality of clinical care will be monitored by the MoPH through the EHCP clinical\nindicators listed in Annex V.\n\n**Enrollment**\n\n11. Contracted PHCCs will receive from CMU of PCM through the MoPH the list of\nbeneficiaries in their catchment area and will be responsible for enrolling those beneficiaries\nthrough marketing and outreach campaigns. Once enrolled, individuals will be exempted from\npayment for EHCP services as they will be fully subsidized by the government. A nominal fee of\naround US$12 will be paid by each household for registration. Upon enrollment, each member of\nthe household will receive a photo identification enrollment card which will be saved in the\nsystem for proper verification of beneficiaries.\n\n\n37", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000020:36:1:0", "start": 556, "end": 580, "surface": "EHCP clinical\nindicators", "probe_tag": "confusion", "probe_score": 0.1875, "luna_label": 0}]}, {"key": "aj-187", "text": " the private sector, <sup>4</sup> and the\nGoC. <sup>5</sup>\n\n\n3 Transparency International (2013), Global Corruption Barometer, Afrobarometer (2011–2013 and 2014–2015).\n4 World Bank (2009) Cameroon Enterprise Surveys, INS (2009) firm census, GoC (2011) Business Climate Survey;\n<mark>[WEF (World Economic Forum). 2015.](https://www.weforum.org/)</mark> _<mark>Global Competitiveness Report 2014–2015.</mark>_\n5 DSCE 2010–2020; President Paul Biya’s New Year Speeches in December 2003, 2005, 2006, 2007, 2014, 2015 or official\ncommunication to Cabinet in December 2004, September 2006, September 2007, March 2008, July 2009, and October 2015.\n\n\nPage 10 of 93", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000044:13:2:2", "start": 189, "end": 216, "surface": "Cameroon Enterprise Surveys", "probe_tag": "confusion", "probe_score": 0.8704, "luna_label": 1}, {"key": "refugee_pads:000044:13:2:4", "start": 253, "end": 276, "surface": "Business Climate Survey", "probe_tag": "keep", "probe_score": 0.9227, "luna_label": 1}]}, {"key": "aj-188", "text": "**The World Bank**\nBalochistan Human Capital Investment Project (P166308)\n\n\n|IA|Implementing Agency|\n|---|---|\n|IMF|International Monetary Fund|\n|IRR|Internal Rate of Return|\n|IUFR|Interim Unaudited Financial Report|\n|LEC|Local Education Council|\n|LHW|Lady Health Worker|\n|M&E|Monitoring and Evaluation|\n|MNCH|Maternal, Newborn, and Child Health|\n|MUC|Marginal Utility of Consumption|\n|NIPS|National Institute of Population Studies|\n|NPV|Net Present Value|\n|OECD|Organisation for Economic Co‐operation and Development|\n|PCC|Project Coordination Committee|\n|PDHS|Pakistan Demographic and Health Survey|\n|PDO|Project Development Objective|\n|PHC|Primary Health Care|\n|PITE|Provincial Institute for Teacher’s Education|\n|PMU|Project Management Unit|\n|PoR|Proof of Registration|\n|PPHI|People's Primary Healthcare Initiative|\n|PPSD|Project Procurement Strategy for Development|\n|PSC|Project Steering Committee|\n|PTSMC|Parent‐Teacher School Management Committee|\n|RHC|Rural Health Center|\n|RMNCHN|Reproductive, Maternal, Newborn, Child Health, and Nutrition|\n|RMP|Repatriation and Management Policy for Afghan Refugees|\n|RPF|Resettlement Policy ", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000085:3:0:0", "start": 562, "end": 600, "surface": "Pakistan Demographic and Health Survey", "probe_tag": "drop", "probe_score": 0.036, "luna_label": 0}]}, {"key": "aj-189", "text": "*|**Result Area 3 on Transparency and Accountability through Digitalization**|**Result Area 3 on Transparency and Accountability through Digitalization**|**Result Area 3 on Transparency and Accountability through Digitalization**|\n|DLI8 on enhancing e-<br>information|30.00|-|30.00|a. <br>Weak enforcement of the 2007 Access to Information Law on proactive<br>disclosure of information and response to requests for information.<br>b. <br>Opportunity to enhance government reporting to the public online about<br>progress achieved towards economic and public sector modernization.|\n|DLI9 on interactive<br>statistical<br>information|30.00|-|30.00|Important gaps in open data coverage and openness to be mitigated by<br>establishing a national data repository with an interactive interface and<br>protocols to allow access to the data for policy analysis and research purposes.|\n|DLI10 on<br>institutionalizing<br>effective health data<br>use|8|18.00|26.00|a. <br>Weak health data management.<br>b. <br>Need to institutionalize the data quality assurance mechanism in place,<br>establishing data quality standards and conducting routine assessments.<br>c. <br>Opportunity to better utilize quality data for more effective and timely<br>decision-making.|\n|Front-end Fees|0.8025|TBD||d. <br>|\n|**Total**|**350**|**54.34**|**404.34**||\n\n\n\nPage | 13", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000181:22:3:0", "start": 733, "end": 757, "surface": "national data repository", "probe_tag": "drop", "probe_score": 0.0057, "luna_label": 0}]}, {"key": "aj-190", "text": " SEP, in their\ncapacity of coordinators and regulators for social protection. Based on the needs identified under the Merankabandi\nProject, training and technical support will be provided, as well as assistance with coordination costs. A special focus will\nbe put in strengthening SEP-CNPS capacity to manage the Social Registry and to coordinate its roll out with partners.\n\n85. This sub-component will also support some basic analytical services to build the evidence base which the Technical\nCommittee and the SEP could use for policy and program design.\n\n\n**Sub-component 5.2: Monitoring, Evaluation and Capacity Building (US$5 million)**\n\n\n<mark>86.</mark> <mark>The project will support strong monitoring practices to ensure the scaling up of safety nets interventions under</mark>\n<mark>the Merankabandi project and the expansion of coverage and delivery of payments in accordance to the key project</mark>\n<mark>results indicators to achieve the intended outcomes.</mark> <mark>This subcomponent would support the development of social</mark>\n<mark>accountability and citizen engagement mechanisms with the support of local-level institutions and stakeholders. The</mark>\n<mark>project will introduce monitoring mechanisms to prevent errors, fraud, and corruption in delivery through monitoring</mark>\n<mark>and validation of payments. The project will provide support to process assessments and evaluations during</mark>\n<mark>implementation. IBM mechanisms will be put in place to assess satisfaction of beneficiaries on project performance and</mark>\n<mark>to collect real time data on the evolving vulnerability context.</mark>\n\n<mark>87.</mark> <mark>This subcomponent will support a wide range of capacity-building activities including a range of training modules</mark>\n<mark>on technical and operational aspects required for the implementation of the safety nets and", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000130:32:1:0", "start": 313, "end": 328, "surface": "Social Registry", "probe_tag": "confusion", "probe_score": 0.0835, "luna_label": 0}, {"key": "refugee_pads:000130:32:1:1", "start": 1579, "end": 1593, "surface": "real time data", "probe_tag": "drop", "probe_score": 0.0145, "luna_label": 0}]}, {"key": "aj-191", "text": " 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 of health facilities for enrollment registers, volume, quality of services, reporting and\npayments, and sample of beneficiary households for verification of enrollment, receipt of\nservices and satisfaction; and (iv) look at different levels of monitoring as undertaken by MoPH\nto ensure that payments are being verified adequately against claims and services are actually\nprovided. Evaluation report will be submitted once per year and will include recommendations\non how to further improve project implementation and prevent fraudulent behavior or abuse.\n**_Beneficiary Assessment._** A beneficiary assessment will be carried out to determine the impact\nof the project on the household service utilization, the cost of the services used, and the capacity\nof PHCCs to deliver services in effective and cost efficient manner. **_Evaluation by the Bank._**\nAs part of regular supervision, the Bank will conduct an evaluation at the mid-year to (i)\nundertake its own assessment of the project achievements and progress towards development\nobjective; (ii) review the results of both the independent evaluation and beneficiary assessment\nreports, and reconcile the results with its own assessment; and (iii) suggest any changes to be\nmade to the evaluation methods, scope and frequency as needed.\n\n51. **Steps for Evaluation:** In order to meet the assessment requirements, the following steps\nshould be taken to prepare for evaluation: (i) data collection and analysis process, formally\ninstitutionalized; (ii) baseline data established per targeted indicator; (iii) clarity regarding the\ndefinition and collection of each indicator; (iv) defining indicators must be a participatory effort\n\n\n55", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000020:54:1:0", "start": 1772, "end": 1785, "surface": "baseline data", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 0}]}, {"key": "aj-192", "text": " on sub-project choices –<br>Water, Education, Health, Infrastructure)<br> <br>|\n|12-48 months| <br>Implementation of all components’<br>activities<br> <br>Community consultation and<br>inclusion<br> <br>Capacity building of project staff<br> <br>Technical quality control<br> <br>Selection of investments according to<br>selection criteria<br> <br>Collection of M&E data<br> <br>Adherence to fiduciary procedures<br> <br>Policy dialogue with government<br> <br>Information sharing and coordination<br>with other donors/agencies<br>supporting NGOs<br> <br>|As above<br>|\n|48 – 60<br>months| <br>Finalization of component activities<br> <br>Collection of end-line M&E data<br> <br>Final technical audit|As above|\n\n\n71", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000071:81:1:0", "start": 369, "end": 377, "surface": "M&E data", "probe_tag": "drop", "probe_score": 0.0122, "luna_label": 0}]}, {"key": "aj-193", "text": "39. The NPTP Project Unit in the MOSA is responsible for the following: (i) managing the\nNPTP database in MOSA; (ii) receiving household applications; (iii) interfacing with applicants;\n(iv) entering data; (v) conducting household visits; (vi) checking for data errors; (vii)\ntransmitting data to the central database of the NPTP CMU; (viii) verifying claims from\nhospitals, schools, and primary healthcare centers (PHCs) and authorizing payments; (ix)\nmanaging the outreach campaign; (x) managing the e-card food voucher beneficiaries list,\ndelivery of the e-cards to beneficiaries, and follow up; and (xi) monitoring of the program\n(specifically inputs and outputs).\n\n40. The NPTP CMU in the PCM is responsible for the following: (i) managing the central\ndatabase; (ii) validating data and cross-checking with national databases; (iii) processing\nhousehold data and generating scores and ranks according to the PMT formula; (iv) maintaining\nthe PMT formula, and providing the list of beneficiaries (v) analyzing national data and reporting\nfindings to the Social Inter-Ministerial Committee (Social-IMC); (vi) monitoring of program\nresults including targeting performance; and (vii) auditing data processing.\n\n41. MOSA SDCs are responsible for: (i) receiving household applications and interface with\napplicant; (ii) data entry into program application; (iii) conducting household visits; (iv)\nchecking possible data errors in application forms against provided official documents; (v)\ntransmitting households’ application data to MOSA central unit; and (vi) handling appeals and\nclaims received by households.\n\n42. With respect to the implementation arrangements of the e-card food voucher, the\nfollowing arrangements have been agreed upon: (i) WFP will conduct training for NPTP field\nwork coordinators and social workers, including on", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000139:24:0:0", "start": 89, "end": 110, "surface": "NPTP database in MOSA", "probe_tag": "confusion", "probe_score": 0.2841, "luna_label": 0}, {"key": "refugee_pads:000139:24:0:1", "start": 849, "end": 863, "surface": "household data", "probe_tag": "drop", "probe_score": 0.0478, "luna_label": 1}, {"key": "refugee_pads:000139:24:0:2", "start": 1014, "end": 1027, "surface": "national data", "probe_tag": "confusion", "probe_score": 0.0572, "luna_label": 1}]}, {"key": "aj-194", "text": "PIT Project Implementation Team\nPO Production officers\nPOM Program Operations Manual\nPPDA Public Procurement and Disposal of Public Assets\nPPP Private-Public Partnership\nPPSD Project Procurement Strategy for Development\nPSC Project Steering Committee\nPSFU Private Sector Foundation Uganda\nPTC Project Technical Committee\nRHD Refugee-Hosting District\nSOPs Standard Operating Procedures\nSORT <mark>Systematic Operations Risk-rating Tool</mark>\nSTEP Systematic Tracking of Exchanges in Procurement\nUBOS Uganda Bureau of Statistics\nUGGDS Uganda Green Growth Development Strategy\nUIA Uganda Investment Authority\nUIRI Uganda Industrial Research Institute\nUNHCR United Nations High Commissioner for Refugees\nUNHS Uganda National Household Survey\nUEW Unsafe Environment for Women\nUWEP Uganda Women Entrepreneurship Program\nVSLAs Village Savings and Loans Associations\nWEE Women’s Economic Empowerment\nWHR Window for Host Communities and Refugees", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000088:3:0:0", "start": 706, "end": 738, "surface": "Uganda National Household Survey", "probe_tag": "drop", "probe_score": 0.0194, "luna_label": 0}]}, {"key": "aj-195", "text": " BAEC, PITE, Bureau of Curriculum and Extension Center, DoS, and BISE.\n41 It includes (a) allocating DDO code to the cluster head; (b) LECs preparing cluster plans and budgets; and (c) organizing\ntrainings of head teachers at the cluster head level on participatory planning, school‐based budgeting, cluster‐level\nprocurements, and conducting of summative and formative student assessments; and (d) EMIS cells gathering cluster data and\nsubmitting to the District Education Authority (DEA) and SED.\n\n\nPage 16 of 47", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000085:20:2:0", "start": 420, "end": 432, "surface": "cluster data", "probe_tag": "drop", "probe_score": 0.0288, "luna_label": 0}]}, {"key": "aj-196", "text": "|**Component 3: Road Safety**|\n|Development<br>and<br>operationalization of the Road<br>Accident Database Management<br>System<br>Training<br>and<br>awareness<br>campaigns in the Project area|• <br>Road<br>safety<br>awareness<br>campaigns in the Project area,<br>and road safety data collection<br>and management as part of<br>contingency planning including<br>accident data attributed to<br>climate<br>change<br>such<br>as<br>increased runoff and higher<br>temperatures which increase<br>the pavement deterioration<br>hence the ride quality of the<br>road<br>and<br>necessitating<br>frequent<br>maintenance<br>routines.<br>• <br>Use<br>the<br>Road<br>Accident<br>Database Management System<br>to inform decision making<br>towards targeted interventions<br>that make the road safer to<br>users and more resilient to<br>climate change||\n\n\n\nPage 78 of 80", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000146:82:1:0", "start": 267, "end": 283, "surface": "road safety data", "probe_tag": "drop", "probe_score": 0.0119, "luna_label": 0}, {"key": "sample:refugee_pads:000146:82:1:1", "start": 361, "end": 374, "surface": "accident data", "probe_tag": "drop", "probe_score": 0.0483, "luna_label": 0}]}, {"key": "aj-197", "text": "13\n\n\n**_Monitoring and Evaluation_**\n\n\nMonitoring will be done according to the development indicators given in the attachment to Annex 1.\nThe project will strengthen the capacity of CNOSEGE, and the Planning Unit of the Ministry so that\nmonitoring reports on the implementation of the reform can include key progress and impact\nindicators. Currently the Planning unit generates statistical data on all aspects of the education sector,\nhowever this can be further strengthened to monitor progress on key reform objectives such as access,\nequity and quality. In addition, during the donors round-table UNESCO offered support to develop an\nEducation Management Information System (EMIS). If this is not in place by the end of Phase I of the\nAPL, this would be a priority item for Phase II.\n\n\nEvaluation of the impact of the reforms will be done by CNOSEGE by recruiting experts in this field\n\nand an initial evaluation will be done at the end of Phase I. Particular areas of impact assessment will\nbe student performance and success in reaching out to disadvantaged groups. Normally, student\nperformance would be measured by overall test results but as the pool of students widens to include\nstudents from less advantaged socioeconomic groups, there will be a downward pressure on test\nscores. The Planning Unit of the Ministry will be strengthened to monitor progress in reaching out to\ndisadvantaged groups and test scores of students by socioeconomic background. Staff will carry out a\nrandom survey (5 to 10% sample) of students by socioeconomic background in 2001 to establish a\nbaseline. To keep the survey simple, the socioeconomic background questions will be limited to easily\nidentified categories such as day-laborers, civil servants, shopkeepers etc. The survey will be repeated\nin 2005 and 2110.\n\n\n**D.** PROJECT RATIONALE\n\n\n**1. Project alternatives considered and reasons for rejection**\n\nOriginally, the project was designed as a Sector Investment Loan, however, given the Government's\ncommitment to the education sector, and the", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000038:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "drop", "probe_score": 0.0402, "luna_label": 0}]}, {"key": "aj-198", "text": "جال التعليم والصحة ومن القسائم الغذائية\n\nلكترونية .. وسيُصار إلى جمع كافة البيانات التي تفصل بين الذكور واإلناث للتمكّن من رصد مشاركة النساء والفتياتاإل\n\nإلى ذلك، سترصد المؤشرات الوسيطة الوعي وفعالية البرنامج من حيث التوقيت بين تقديم الطلب واإلشعار باألهلية طيلة\n\n.فترة المشروع\n\n\nا م المعلومات اإلدارية المُمَكنَن الذي تمّ وضعه في إطار المرحلة األولى من البرنامج الوطني الستهداف44.  يشكّل نظ\n\nأسماء األسر التي قدّمت طلبًا فياألسر األكثر فقرًا العنصر األساسي في نظام الرصد والتقييم ويتضمّن وحدة تقوم بتسجيل\n\n)قاعدة بيانات البرنامج وتسجيل نتائج تقييم أهليتها (بما في ذلك النتيجة في البرنامج الوطني الستهداف األسر األكثر فقرًا\n\nالتي يقدّمونها. وستتواصل بلورة هذه الو", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000066:31:2:0", "start": 508, "end": 529, "surface": "قاعدة بيانات البرنامج", "probe_tag": "drop", "probe_score": 0.0215, "luna_label": 0}]}, {"key": "aj-199", "text": " implementation of project activities as per the implementation work plan\n\nin a timely and high quality manner;\n\n\nb) Monitoring the project performance with regard to achievement of project activities and\n\nif necessary, modify and/or redirect activities to maximize the potential impact of the\nproject;\n\n\nc) Establishing a basis for evaluating the project with regard to achievement of the overall\n\ndevelopment objective;\n\n\nd) Establishing working partnerships with PHCCs, Prime Minister’s (PM) office responsible\n\nfor the NPTP program, Hospitals (OPD) to gather and/or access the relevant data to\noptimize the M&E outputs for each component and sub-component under an integrated\nM&E plan;\n\n\ne) Organizing data collection from different stakeholders and facilitating verification,\n\nanalysis of the data/information received from various stakeholders;\n\n\nf) Liaising with implementing agencies and partners required for implementation of key\n\ntechnical instruments (i.e. facility surveys, beneficiary assessments etc.); and\n\n\n53", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000020:52:1:0", "start": 969, "end": 985, "surface": "facility surveys", "probe_tag": "drop", "probe_score": 0.0181, "luna_label": 0}, {"key": "refugee_pads:000020:52:1:1", "start": 987, "end": 1010, "surface": "beneficiary assessments", "probe_tag": "confusion", "probe_score": 0.1875, "luna_label": 0}]}, {"key": "aj-200", "text": "20to%20protection%20and%20a%20connection%20between)</u>\n<u>[and%20a%20connection%20between](https://www.refworld.org/cgi-bin/texis/vtx/rwmain?page=search&docid=5acb33ad4&skip=0&query=legal%20considerations%20regarding%20access%20to%20protection%20and%20a%20connection%20between)</u>\n**<u>71</u>** In 2018, as in previous years, the international protection rate for some nationalities varied significantly within the EU+ region. One\nexample of this based on Eurostat data for first instance asylum decisions is Afghan nationals for whom protection rates in the first three\nQuarters of 2018 amongst countries that had processed more than 100 applications varied from 86% in Italy and 75% in Greece to 11% in\nBulgaria and 14% in Denmark.\n**<u>72</u>** However, due to partial movement restrictions in Bosnia and Herzegovina and due to insufficient capacity of the national systems, some\n<u>people were not able to access the asylum procedure by formally lodging their claims.</u>\n**<u>73</u>** The fact that a refugee or an asylum-seeker has moved onward does not affect his or her right to treatment in conformity with\ninternational human rights law, or his or her potential need for international protection and associated rights under international refugee\n\n\n34 **DESPERATE JOURNEYS**", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001391:33:12:0", "start": 458, "end": 471, "surface": "Eurostat data", "probe_tag": "keep", "probe_score": 0.9592, "luna_label": 1}]}, {"key": "aj-201", "text": "\ndo not answer the landlords’ demands of steeper costs that cover\nservices they thought to have been included within the initial rent\nagreement. Such points that are left undecided (e.g. electricity\nor water supply bills, etc.) have been reported in FGDs as some\nof the main causes of conflicts between landlords and tenants.\nFor example, a woman from Tripoli reported in an FGD that the\nlandlord asked her to pay electricity bills without giving her the\nactual bill, and whenever she asked for it, he threatened to evict\nher.\n\n\n<mark>33</mark>\n\n\n\n<u>90%</u>\n\n\n\nSurveyed households\n\n\n\n\n\n\n\n\n\nWritten agreement Verbal agreement No agreement\n\n\n**Figure 30** Types of rental agreements of surveyed households\nSource: UN-Habitat and UNHCR household survey (July 2017)", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000666:31:2:1", "start": 713, "end": 750, "surface": "UN-Habitat and UNHCR household survey", "probe_tag": "keep", "probe_score": 0.9496, "luna_label": 1}]}, {"key": "aj-202", "text": "Chapter 4\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**Demographics of displacement**\n\n\nBased on the United States Customs and Border\nProtection arrivals figures, <sup>**87**</sup> the demographics of\nthose arriving continues to shift over time. Between\n2018 and 2020, the proportion of unaccompanied\nand separated children from El Salvador, Guatemala\nand Honduras arriving at the United States border\ndecreased slightly from 15 per cent to 11 per cent. In\n2018 and 2019, most arrivals were families. In 2020,\ndue to movement restrictions, there was a 75 per\ncent decrease in overall arrivals, notably families, with\nnearly three-quarters recorded as single adults.\n\n\nData recorded by the Mexican National Migration\nInstitute (MNMI) reflects a similar trend. Prior to 2018,\n\n\n\napproximately half of the children from El Salvador,\nGuatemala and Honduras were unaccompanied,\ncompared to 32 per cent in 2018 and 25 per cent\nin 2019. These figures indicate that people fleeing\nthese three countries were increasingly travelling\nas families. In 2020, the overall number of children\nfrom these three countries recorded in the MNMI\ndata decreased by nearly 80 per cent, primarily\ndue to movement restrictions enforced to contain\nCOVID-19, and the proportion of unaccompanied\nchildren increased to 44 per cent. The overall number\nof asylum claims in Mexico also surged from 3,400 in\n2015 to 70,400 in 2019 before dropping 41 per cent\nto 41,200 in 2020.\n\n\n\n**87** See <u>[https://www.cbp.gov/newsroom/stats/southwest-land-border-encounters#](https://www.cbp.gov/newsroom/stats/southwest-land-border-encounters)</u>\n\n\nUNHCR > **GLOBAL TRENDS 2020** 33", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000757:32:0:0", "start": 85, "end": 145, "surface": "United States Customs and Border\nProtection arrivals figures", "probe_tag": "keep", "probe_score": 0.9574, "luna_label": 1}, {"key": "reliefweb:000757:32:0:1", "start": 1104, "end": 1113, "surface": "MNMI\ndata", "probe_tag": "keep", "probe_score": 0.9692, "luna_label": 1}]}, {"key": "aj-203", "text": "**WESTERN BALKANS** REGULAR ECONOMIC REPORT NO.18\n\n\nMontenegro must now deal with the deepest\nrecession in decades…\n\n\n\n...mainly because the COVID-crisis has halted\ntourism.\n\n\n\nReal GDP growth, percent International tourists overnight stays, in thousands\n10 1,000\n\n\n900\n\n\n\n5\n\n\n0\n\n\n-5\n\n\n-10\n\n\n-15\n\n\n\n800\n\n\n700\n\n\n600\n\n\n500\n\n\n400\n\n\n300\n\n\n200\n\n\n100\n\n\n0\n\n\n\nJ <u>2015–19 average</u> J <u>2020</u>\n\n\n_Source:_ MONSTAT data, World Bank staff calculations. _Source:_ MONSTAT data, World Bank staff calculations.\nEmployment fell in all sectors... ...and inflation plunged into negative territory.\n\n\n\nAdministrative data, thousands, Jan 2015–Jul 2020 Administrative data, Jan 2014–Jul/Aug 2020\n220 55 12\n\n\n\n600\n\n\n500\n\n\n400\n\n\n300\n\n\n200\n\n\n100\n\n\n0\n\n\n\n55\n\n\n\n12\n\n\n10\n\n\n8\n\n\n6\n\n\n4\n\n\n2\n\n\n0\n\n\n-2\n\n\n-4\n\n\n\n200\n\n\n190\n\n\n180\n\n\n170\n\n\n160\n\n\n\n50\n\n\n45\n\n\n40\n\n\n35\n\n\n30\n\n\n25\n\n\n20\n\n\n\n\n- Employment - Employment_tc - PPI, percent - CPI, percent - Real net wage (EUR 2015), rhs\n\n<u>▬</u> <u>Unemployment, rhs</u> <u>▬</u> <u>Unemployment_tc, rhs</u>\n\n\n_Source:_ MONSTAT data, tc = trend cycle. _Source:_ MONSTAT data, World Bank staff calculations. Last obs. (CPI and\nPPI - Aug 2020, Real net wage - Jul 2020)\nBank lending is still solid… ...but the fiscal position has deteriorated.\n\n\n\nOutstanding loans, Jan 2012–Jul 2020, in millions 2015–20, percent of GDP\n3.5 60\n\n\n\n0\n\n\n-2\n\n\n-4\n\n\n-6\n\n\n-8\n\n\n-10\n\n\n-12\n\n\n-14\n\n\n\n3.0\n\n\n2.5\n\n\n2.0\n\n\n1.5\n\n\n1.0\n\n\n0.5\n\n\n0\n\n\n\n50\n\n\n40\n\n\n30\n\n\n20\n\n\n10\n\n\n0\n\n\n\nJ Private", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000814:74:0:0", "start": 403, "end": 415, "surface": "MONSTAT data", "probe_tag": "keep", "probe_score": 0.9424, "luna_label": 1}]}, {"key": "aj-204", "text": " and who face protection risks\nsimilar to those of IDPs but who, for practical or other reasons, could not be\nreported as such.\n\n**7** IDPs protected/assisted by UNHCR who have returned to their place of origin\nduring 2016.\n\n**8** Refers to persons who are not considered as nationals by any State under\nthe operation of its law. This category refers to persons who fall under the\nagency’s statelessness mandate because they are stateless according to this\ninternational definition, but data from some countries may also include persons\nwith undetermined nationality. See Annex Table 7 at http://www.unhcr.org/\nstatistics/16-WRD-table-7.xls for detailed notes.\n\n**9** Refers to individuals who do not necessarily fall directly into any of the other\ngroups but to whom UNHCR may extend its protection and/or assistance\nservices. These activities might be based on humanitarian or other special\ngrounds.\n\n**10** The statelessness figure refers to a census from 2011 and has been adjusted\nto reflect the number of persons with undetermined nationality who had their\nnationality confirmed in 2011-2016.\n\n**11** According to the Government of Algeria, there are an estimated 165,000\nSahrawi refugees in the Tindouf camps.\n\n**12** All figures relate to the end of 2015.\n\n**13** Australia’s figures for asylum-seekers are based on the number of applications\nlodged for protection visas.\n\n**14** The refugee population includes 243,000 persons originating from Myanmar\nin a refugee-like situation. The Government of Bangladesh estimates the\npopulation to be between 300,000 and 500,000.\n\n**15** The 300,000 Vietnamese refugees are well integrated and in practice receive\nprotection from the Government of China.\n\n**16** The statelessness figure is based on a Government estimate of individuals", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000712:64:1:1", "start": 947, "end": 963, "surface": "census from 2011", "probe_tag": "keep", "probe_score": 0.9132, "luna_label": 1}]}, {"key": "aj-205", "text": "-**\n**patriated. In recent years, UNHCR**\n**and States have worked to increase**\n**the use of resettlement as a strategic**\n**durable solution–serving to resolve**\n**some protracted refugees situations,**\n**to create protection space, and to**\n\n\n**15** The need for durable solutions is not limited to\nrefugees: IDPs and stateless persons also require lasting\nresolution to their legal and physical protection needs.\nHowever, due to the lack of reliable and comprehensive\ndata on solutions for other groups, the analysis in this\nsection is confined to durable solutions for refugees.\n\n\n**16** Based on consolidated reports from countries of\nasylum (departure) and origin (return).\n\n\n\n\n\n**16** UNHCR Global Trends 2011 **UNHCR Global Trends 2011** **17**", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "sample:reliefweb:001077:8:2:0", "start": 693, "end": 717, "surface": "UNHCR Global Trends 2011", "probe_tag": "keep", "probe_score": 0.9991, "luna_label": 1}, {"key": "sample:reliefweb:001077:8:2:1", "start": 720, "end": 744, "surface": "UNHCR Global Trends 2011", "probe_tag": "keep", "probe_score": 0.9965, "luna_label": 1}]}, {"key": "aj-206", "text": "using the Government of Jordan’s Household Income and Expenditure Survey (HIES) 2017/18.\n\n\nThe sampling frame for the refugee sample was\ndrawn from the ProGres registration database\nadministered by UNHCR <sup>1</sup> . This sample is stratified by rural/urban location and camp/non-camp\nlocation in four groups: Amman, other governorates-urban, other governorates-rural, camps. An\nex-post weight adjustment was also applied to the\nrefugee population to better reflect this population’s demographics using the ProGres database.\n\n\nThe Socio-Economic Situation of Refugees in\nJordan is a quarterly mobile phone panel survey conducted in 2022 with the main purpose to\nmonitor changes in vulnerability levels among\nrefugees over time. The questionnaire covered\ntopics about refugees’ households’ economic situation, food security, shelter, water, sanitation,\nand hygiene (WASH), and health. The survey\nwas centered on collecting information repeatedly from the same households (panel data) and it\nwas completed in four Rounds (Q1, Q2, Q3 and\nQ4 2022). <sup>2</sup> For Round 1 and Round 2, the survey covered Syrian and non-Syrian households\nresiding outside of camps across all 12 governorates in Jordan, <sup>3</sup> while in Round 3 and 4, the\n\n\n1. ProGres (Profile Global Registration System) is an\nregistration and case management tool developed by\nUNHCR which provides a common source of information\nabout individuals and it is used by different work units to\nfacilitate protection of persons of concern to the organization. ProGres is the main repository in UNHCR for storing\nindividuals’ data.\n2. In each round, the data were collected over the phone\nthe last two weeks of the last month of each quarter. The\nonly exception was the data collection for Q4 2022, which it\ntook place from 5/", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001535:8:0:4", "start": 973, "end": 983, "surface": "panel data", "probe_tag": "confusion", "probe_score": 0.4694, "luna_label": 0}]}, {"key": "aj-207", "text": "**West African migrants rescued by the IOM in northern Niger:** according to IOM, seventy-four West African\nmigrants were rescued on 4 October in northern Niger, in the middle of the desert bordering Algeria.\n\n\n**Crisis simulation in the Tillabéri region:** On the week of 17 October, more than 500 members from communities,\nlocal authorities, civil society and security forces participated in IOM’s fourth crisis simulation exercise in Tillabéri, Niger. The\naim of this simulation was to test local and regional authorities’ ability to respond to a large migration movement into Niger,\nprecipitated by a crisis at the border.\n\n\n**State of emergency in Mali extended:** on 24 October the state of emergency was extended in Mali for another\nsix months due to continued insecurity in the north of the country.\n\n\nMonthly Trends Analysis West Africa 3", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000266:2:0:0", "start": 810, "end": 845, "surface": "Monthly Trends Analysis West Africa", "probe_tag": "confusion", "probe_score": 0.5638, "luna_label": 0}]}, {"key": "aj-208", "text": "UNHCR, UNICEF and IOM October 2017\n\n\n**Endnotes:**\n\n1. Data on arrivals is partial due to the large scale of irregular movements\nand reflects only sea arrivals for Greece and Italy. Data for Spain\ninclude both sea and land arrivals.\n\n2. Separated children are children separated from both parents, or from\n\ntheir previous legal or customary primary caregiver, but not necessarily\nfrom other relatives. These may, therefore, include children\naccompanied by other adult family members. Unaccompanied children\nare children who have been separated from both parents and other\nrelatives and are not being cared for by an adult who, by law or custom,\nis responsible for doing so. (IASC)\n\n3. Arrival figures for Greece are collected in the framework of UNHCR\n\nborder activities and are provided by Hellenic Coastguard and\nHellenic Police.\n\n4. During the same period of time, a total of 2,557 referrals were made\n\nto the Greek National Centre for Social Solidarity (EKKA) based on\nchildren identified on islands and mainland Greece, including near the\nland border with Turkey.\n\n5. During the same period of time, 599 children applied for asylum in\n\n\n##### Limitation of available data on Children and UASC:\n\nThere is no comprehensive data on arrivals (both adults\nand children) in Europe, especially by land and air, as such\nmovements are largely irregular and involve smuggling\nnetworks, which are difficult to track. If collected, data is rarely\ndisaggregated by nationalities, risk category, gender or age.\n\n\nReliable data on the number of UASC either arriving to, or\ncurrently residing in, different European countries is often\nunavailable. The number of asylum applications filed by\nUASC is used to provide an indication of trends but does not\nnecessarily provide an accurate picture of the caseload due\nto backlogs in national asylum systems, onward irregular\nmovements or not applying for asylum at all. In addition, due\nto different definitions and national procedures and practices,\ncollecting accurate data on separated children specifically is", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000118:6:0:1", "start": 182, "end": 196, "surface": "Data for Spain", "probe_tag": "confusion", "probe_score": 0.4296, "luna_label": 1}, {"key": "reliefweb:000118:6:0:2", "start": 685, "end": 711, "surface": "Arrival figures for Greece", "probe_tag": "confusion", "probe_score": 0.8609, "luna_label": 0}]}, {"key": "aj-209", "text": " and the Great Lakes (Breakdown by country of asylum)</u>\n_(Statistics as at January 2002, including refugees not assisted by UNHCR)_\n\nZambia 218,540\nDRC 186,975\nNamibia 30,599\nRoC 15,300\nSouth Africa 7,207*\n\n_*includes asylum seekers_\n\nBotswana 898\nZimbabwe 226\nSwaziland 140\nMozambique 56\nMalawi 1\n\n\n5", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000498:4:1:0", "start": 60, "end": 89, "surface": "Statistics as at January 2002", "probe_tag": "confusion", "probe_score": 0.3006, "luna_label": 0}]}, {"key": "aj-210", "text": " _Adolescent Pregnancy. Fact sheet N°364_, 2012.\n18 WHO, _Preventing early pregnancy and poor reproductive outcomes among adolescents in developing countries:_\n_what the evidence says_ . WHO/FWC/MCA/12.0 (Geneva: 2011).\n<u>[http://whqlibdoc.who.int/hq/2012/WHO_FWC_MCA_12_02.pdf.](http://whqlibdoc.who.int/hq/2012/WHO_FWC_MCA_12_02.pdf)</u>\n19 Monica Akinyi Magadi, et al. “A comparative analysis of the use of maternal health services between teenagers\nand older mothers in sub-Saharan Africa: Evidence from Demographic and Health Surveys (DHS),” _Social Science &_\n_Medicine_ 64 (2006): 1311-1325.\n20 UNFPA, _State of the World Population 2012,_ 2012.\n21 UNFPA, _Marrying too Young, End Child Marriage_ (New York. UNFPA, 2012).\n22 UNFPA, _State of the World Population 2012,_ 2012: p. 12.\n23 McQuetion, Silverman and Glassman, 2012, as cited in UNFPA, _State of the World Population 2012,_ 2012.\n24 WHO, _Programming for adolescent health and development: Report of a WHO/UNFPA/UNICEF study group_ (New\nYork. WHO, 1999).\n25 IAWG on RH in Crises, “Chapter 4: Adolescent Reproductive Health,” _IAFM_, 2010.\n26 Julia Matthews and Sheri Ritsema, “Addressing the reproductive health needs of conflict-affected young people,”\n_Forced Migration Review_ 19, 2004.\n27 Save the Children and UNFPA, _Adolescent Sexual and Reproductive Health Toolkit in Humanitarian", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000501:39:2:0", "start": 509, "end": 539, "surface": "Demographic and Health Surveys", "probe_tag": "confusion", "probe_score": 0.7242, "luna_label": 1}]}, {"key": "aj-211", "text": "Chapter 5\n\n\n**Objective 4: Support conditions in**\n**countries of origin for return in safety and**\n**dignity**\n\nIndicator 4.1.1: Volume of ODA for the benefit of refugee\nreturnees in the ODA recipient country of origin\n\n\nIndicator 4.1.2: Number of donors providing ODA for\nthe benefit of refugee returnees in the ODA recipient\ncountry of origin\n\n\nData Source: OECD’s Development Finance for Refugee\nSituations in Low- and Middle-Income Countries, Years\n2020-21\n\n\nData Coverage: The data covers development\nfinancing to OECD’s low- and middle-income countries\nas provided by 63 survey respondents, including 48\nbilateral donors (32 DAC donors and 11 non-DAC donors)\nand five multilateral development banks. It also covered\nthe use of core funding by 11 United Nations agency\npartners and four other international organizations.\n\n\nIndicator 4.2.1: Number of refugees returning to their\ncountry of origin\n\n\nIndicator 4.2.2: Proportion of returnees with legally\nrecognized documentation and credentials\n\n\nData Source: UNHCR’s Administrative Records, return\nmonitoring and household surveys\n\n\nData coverage: Data on returnees are available for\n30 countries. Despite the progress made on indicator\n4.2.2 vis-à-vis data collection in some countries, data\n\nare not easily and fully available, highlighting a gap in\nmeasuring access to documentation and registration of\ncivil events for the displaced and host communities as\ndata on registration of civil events, including birth and\ndeath registration, remain generally incomplete and\nirregularly updated.\n\n\n74 GLOBAL COMPACT ON REFUGEES INDICATOR REPORT", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000435:73:0:1", "start": 1047, "end": 1086, "surface": "return\nmonitoring and household surveys", "probe_tag": "confusion", "probe_score": 0.571, "luna_label": 1}, {"key": "reliefweb:000435:73:0:2", "start": 1104, "end": 1121, "surface": "Data on returnees", "probe_tag": "confusion", "probe_score": 0.3563, "luna_label": 0}]}, {"key": "aj-212", "text": "|Col1|Nombre del proceso/Subproceso: Prevención y Protección|Código: PP-P01-F10|\n|---|---|---|\n||**ALERTA TEMPRANA **|Versión: 01|\n||**ALERTA TEMPRANA **|Vigente desde:<br>07/09/2018|\n\n\n\ndel proceso de producción, distribución y comercialización de drogas ilegales buscan la\ndistribución de ingresos por medio del tráfico de estupefacientes. Estos grupos hoy se trenzan\nen una disputa territorial por el monopolio de la comercialización de sustancias alucinógenas a\ntravés de la violencia ejercida contra el grupo adversario, dinámica que impacta en el conjunto\nde la población asentada en los sectores donde ejercen presencia.\n\n**<mark>2.1 E</mark>** **scenario de riesgo y situación actual.**\n\n\ny rurales.\n\nEl informe elaborado por el Sistema Integrado de Monitoreo de Cultivos Ilícitos (Simci), de la\nOficina de las Naciones Unidas contra la Droga y el Delito, denominado Monitoreo de territorios\nafectados por cultivos ilícitos 2020, evidencia el aumento año tras año de la cantidad de cocaína\n\n\n14", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000061:13:0:0", "start": 875, "end": 936, "surface": "Monitoreo de territorios\nafectados por cultivos ilícitos 2020", "probe_tag": "confusion", "probe_score": 0.8697, "luna_label": 1}]}, {"key": "aj-213", "text": "ósito de que puedan acceder a todos los servicios que\nbrinda actualmente ONPAR, y así obtener una adecuada coordinación, planificación y ejecución de\nlas decisiones que tome la Comisión Nacional para la Protección de Refugiados, que es la entidad\nque en nuestro país decide acerca del otorgamiento del estatuto de refugiado.\n\n\nDesde el mes de julio del año 2014 cuando asumí la dirección de la Oficina Nacional para la\natención de Refugiados (ONPAR), nos planteamos un plan de trabajo con metas a corto, mediano\ny largo plazo, algunas de las cuales ya hemos cumplido y se han traducido en cambios sustanciales\nen el funcionamiento de ONPAR, y en el mejoramiento del servicio que prestamos. Entre ellos\npodemos mencionar el fortalecimiento del procedimiento para la determinación del estatuto de\nrefugiado, implementando de manera inmediata las recomendaciones, proyecto que llevamos a\ncabo en conjunto con ACNUR, un mejor control en las entradas de las solicitudes, estadísticas,\nseguimientos, digitalización, entre otros, implementando la Base de Datos donada, la incorporación\nde investigaciones de contexto en el análisis de las solicitudes y el mejoramiento en la estructura y\nanálisis de los informes presentados a la Comisión, capacitaciones para los colaboradores con el\napoyo de organizaciones de la sociedad civil y de ACNUR, la incorporación de los niños, niñas y\nadolescentes al proceso de determinación del estatuto de refugiados con la asistencia", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000280:4:3:0", "start": 1040, "end": 1060, "surface": "Base de Datos donada", "probe_tag": "confusion", "probe_score": 0.623, "luna_label": 0}]}, {"key": "aj-214", "text": "A Knowledge, Attitude, Practice and Behaviour (KAPB) survey in June 2004\nconducted by the United Nations Children Fund showed that 67% of men and 57% of\nwomen had heard about HIV and that 80% of men and 71% of women knew about\nAIDS. There are, however, misconceptions about the ways HIV is transmitted. <sup>63</sup>\nStigmatization and discrimination against people living with HIV/AIDS is also a\nproblem; health workers are likely to discharge them from hospitals, and women are\nmore likely to be divorced and separated from their children. Social exclusion,\nvictimization and blame are also burdens to be shouldered by HIV/AIDS infected\npeople. <sup>61</sup> There are no HIV seroprevalence data for the IDP population.\n\n<u>HIV/AIDS and IDP Situation:</u>\nThe NSP 2005-06 mentions IDPs but does not state specific activities for them. <sup>64</sup> The\nWorld Bank’s MAP has no HIV proposal approved for Somalia. The GFATM round\n4 approved proposal with an HIV component for 2005-06 both mentions IDPs and has\nHIV activities for them. <sup>65</sup> No data exist on the HIV prevalence among IDPs. In a\nqualitative HIV survey among IDPs in Somalia in 2004, 73% stated that HIV was not\na problem and none claimed to have ever seen anyone with AIDS. <sup>61</sup> In the same\nstudy, no HIV interventions were reported in the IDP camps surveyed; however, civil\nsociety representatives in the towns of Bossaso and Hargeisa reported HIV awareness\nraising campaigns. <sup>61</sup>\n\n**7.** **<u>Sudan:</u>**\n\n<u>IDP Situation:</u>\nAbout 6", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001301:29:0:0", "start": 2, "end": 45, "surface": "Knowledge, Attitude, Practice and Behaviour", "probe_tag": "keep", "probe_score": 0.9465, "luna_label": 0}, {"key": "reliefweb:001301:29:0:2", "start": 1103, "end": 1147, "surface": "qualitative HIV survey among IDPs in Somalia", "probe_tag": "confusion", "probe_score": 0.8949, "luna_label": 1}]}, {"key": "aj-215", "text": ". In 2014, 35,900 Somalis were recognized as refugees, mainly in Kenya (11,500), Ethiopia\n(6,300), and Yemen (17,600).\n\nIn the Americas, the refugee population decreased by around 5 per cent, to 769,000. This\ndrop was mainly the result of a revision from\n\n\n**(10)** This figure includes 257,100 Colombians in Ecuador, the Bolivarian Republic\n\nof Venezuela, Costa Rica, and Panama considered to be in a refugee-like\nsituation.\n\n**(11)** Some 13,300 Congolese arriving in Uganda were granted refugee status\n\non a prima facie basis, while 13,700 sought asylum on an individual basis.\nThose arriving in Burundi and Kenya went through individual refugee status\ndetermination.\n\n\n\n<mark>TABLE 1</mark> **Refugee populations by UNHCR regions \u0003** | 2014\n\n\n\n\n\n\n\n|UNHCR regions<br>- Central Africa and Great Lakes<br>- East and Horn of Africa<br>- Southern Africa<br>- West Africa|Start-2014|Col3|Col4|End-2014|Col6|Col7|Change (total)|Col9|\n|---|---|---|---|---|---|---|---|---|\n|UNHCR regions<br>- Central Africa and Great Lakes<br>- East and Horn of Africa<br>- Southern Africa<br>- West Africa|Refugees<br> 508,600<br> 2,003,400<br> 134,500<br> 242,300|People in<br>refugee-like<br>situations<br> 7,400<br> 35,500<br> -<br> -|Total<br", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000809:9:2:0", "start": 697, "end": 733, "surface": "Refugee populations by UNHCR regions", "probe_tag": "confusion", "probe_score": 0.6085, "luna_label": 0}]}, {"key": "aj-216", "text": ">“In the case of non-Libyan children, who do not have legal guardianship, they are temporarily</mark>\n<mark>accommodated until communication takes place and they are transferred to their respective embassies.”</mark>\n\n\n<mark>This, however, only applies to residential institutions, meaning that care centres where children can</mark>\n<mark>actually benefit from social services are opened to migrant and refugee children and families, but this</mark>\n<mark>assessment did not further explore this topic.</mark> <sup>75</sup>\n\n\n<mark>For abandoned children and children born out of wedlock, the registration process to residential homes</mark>\n<mark>consists of the following steps, according to KIs:</mark>\n\n\n75 UNICEF and Coram’s current mapping of social service workforce delivering child protection services further investigate social\ninstitutions’ functioning in Libya.\n\n\n\n38", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001686:38:2:0", "start": 739, "end": 774, "surface": "mapping of social service workforce", "probe_tag": "confusion", "probe_score": 0.5143, "luna_label": 1}]}, {"key": "aj-217", "text": "|Asylum Levels and Trends in Industrialized Countries, 2008|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|**Table 12. Origin of asylum applicants in the European Union (26) by quarter, 2007**<br>Covering 26 European Union 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 12. Origin of asylum applicants in the European Union (26) by quarter, 2007**<br>Covering 26 European Union 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-", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000427:24:0:0", "start": 331, "end": 343, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.5891, "luna_label": 0}, {"key": "reliefweb:000427:24:0:1", "start": 331, "end": 343, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.544, "luna_label": 0}]}, {"key": "aj-218", "text": " application publicly displayed and potentially scrutinised by a Court. Alternatively, such a\ncouple may not know that the requirement to apply for permission exists. If a Buddhist woman\nand non-Buddhist man live together without marrying, or marry without registering the marriage first (both of which would be in breach of the 2015 Myanmar Buddhist Women’s Special\nMarriage Law), <sup>130</sup> NGO representatives expressed their concern that authorities will refuse to\nupdate the household registration list to demonstrate that they are in fact living together.\n\n\nSimilarly, NGO representatives reported that if women are in a polygamous marriage and living\nwith their husband and his other wives (which would be in breach of the 2015 Practicing of\nMonogamy Law), then authorities may also refuse to update their household registration list.\n\n\nAlternatively, NGO representatives discussed that women in either or both of the situations\nabove may simply avoid authorities altogether and not apply to update their household\nregistration list (or register births or apply for and update citizenship documentation) if they\nunderstand they are living in breach of the 2015 Myanmar Buddhist Women’s Special\nMarriage Law and/or 2015 Practicing of Monogamy Law.\n\n\nIn all of these situations, without an accurate household registration list it may be difficult for\nwomen to obtain or replace their own citizenship documentation at the required ages of 18,\n30 and 45, since a household registration list can be required for this process. This may also\nimpact children’s ability to obtain citizenship documentation because this process usually\nrelies on proving the citizenship of the child’s ancestors, as well as the provision of a household\nregistration list.\n\n\n\n<mark>22</mark>", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000092:27:1:1", "start": 484, "end": 511, "surface": "household registration list", "probe_tag": "confusion", "probe_score": 0.7139, "luna_label": 0}]}, {"key": "aj-219", "text": "**Jordan Home Visits Report 2014 - Living in the shadows**\n\n\n## **5**\n\n**5.1**\n\n\n\nRefugees with both an MOI card issued in the governorate of residence and proof of UNHCR registration were entitled to access public health and education services free of charge at the time of data\ncollection, although the Government policy on access to health services has since changed. As of\nDecember 2013, UNHCR registration and MOI service cards became valid for a period of 12 months. As\nMOI service cards only entitle refugees to access services in the governorate in which they are issued,\nthose who move between governorates must re-register with UNHCR and the Ministry of Interior in\nthe new governorate of residence in order to access public services.\n\n\n**Health**\n\n\n**Utilization of public health services has increased, but lack of documentation continues to be a barrier to access**\n**for some refugees.**\n\n\nAt the time of data collection, free primary, secondary and some tertiary health care at Ministry of Health\nfacilities was available to Syrian refugees with an MOI card and proof of UNHCR registration. Those refugees who lacked the required documentation could access health services at UNHCR partner clinics,\nwhich were free of charge and provide primary and some secondary care.\n\n\n\n**Access to services**\n\n\n###### _“A generous Jordanian doctor helps us get the medication_ _my wife needs. Without him we wouldn’t be able to afford it..._\n\n\n###### _My greatest concern and the thing I think about the most is_\n\n\n###### _the education of my children.”_\n\n\n\n\n- Mohammad", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "sample:reliefweb:001174:61:0:0", "start": 2, "end": 32, "surface": "Jordan Home Visits Report 2014", "probe_tag": "confusion", "probe_score": 0.7836, "luna_label": 1}]}, {"key": "aj-220", "text": "##### Annex 7: Uganda\n\nIn Uganda, the High-Frequency Phone Surveys for Refugees (URHFPS) tracks the\nsocioeconomic impacts of COVID-19 on refugees in the country. The survey is a product of a\ncollaboration between the World Bank (WB), the United Nations High Commissioner for\nRefugees, and the Uganda Bureau of Statistics (UBOS). Results from the URHFPS are\ncompared to host households using the national COVID-19 High-Frequency Phone Survey\n(HFPS). Survey respondents were randomly selected from UNHCR’s Profile Global\nRegistration System (ProGres) in addition to the refugee household survey conducted by the\nWorld Bank and the UBOS in 2018.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Col1|Round 1<br>[Displaced, National]|Round 2<br>[Displaced]|Round 3<br>[Displaced, National]|\n|---|---|---|---|\n|Date|22 October 2020 – 25<br>November 2020|5 - 24 December 2020|8 February 2021 -<br>14 March 2021<br>|\n|Sample|Displaced: 2,010<br>Host: 2,136<br>|Displaced: 1,852<br>|Displaced: 1,985<br>Host: 2,122<br>|\n|Representative<br>Population|The displaced sample is representative at seven strata constructed as a<br>combination of country of origin and region:<br>   Kampala-Somalia<br>   Kampala-other (Burundi, DRC, South Sudan)<br>   South West-Burundi(SW-Burundi)<br>   South West-DRC(SW-DRC)<br>   South West-South Sudan(SW-South Sudan)<br>   South West-Som", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000604:54:0:0", "start": 38, "end": 79, "surface": "High-Frequency Phone Surveys for Refugees", "probe_tag": "keep", "probe_score": 0.9261, "luna_label": 1}, {"key": "reliefweb:000604:54:0:1", "start": 346, "end": 352, "surface": "URHFPS", "probe_tag": "confusion", "probe_score": 0.4339, "luna_label": 1}]}, {"key": "aj-221", "text": "## **LIMITATIONS**\n\nWidespread insecurity severely impacted data collection and\ninterrupted humanitarian activities in the field. Protection\ncomponents related to human rights and protection monitoring were\nparticularly impacted throughout Q3, with disruptions and capacity\ngaps further worsening in the second half of August and into\nSeptember, following the Taliban's takeover.\n\n\n_Protection Monitoring in Trinikot City, Urozgan Province (©UNHCR)_\n\nLimitations and considerations regarding Q3 data include:\n\n\n - Many partners including those involved with protection\nmonitoring, opted to diversify or change **the modalities of data**\n**collection** as the context changed. In remote locations instead\nof in-person interviews, monitoring activities took place by\nphone. Community-based protection monitoring was impacted\nby the restrictions to work placed on female staff and remote\nwork arrangements did not enable sufficient space and privacy\nto guarantee full protection of female beneficiaries.\n\n\n\n\n- **Serious concerns exist about data protection**, especially on\nGBV. Recording and storage of GBV data was put on hold until\nits collection could be done in a safe manner in accordance\nwith GBViE Minimum Standards for data protection.\n\n- Direct **data on GBV is limited** by the risks of asking and\nreporting on questions that may lead to disclosures that can be\nretraumatizing and be especially harmful in areas where there\nis no response service. As such, there is limited data on the\nperpetrators of threats against women and girls.\n\n- The **lack of availability of cash and the closure of banks**\nimpacted protection monitoring activities, which are staffintensive, and challenged the collection of data at provincial\nlevels.\n\n- The range of security challenges and operational constraints\nincluding **movement restrictions** imposed by the Taliban\nadministration impeded the capacity of protection partners to\ncollect high quality data and to deliver humanitarian\nassistance.\n\n- UNAMA experienced increasing difficulty in maintaining its\n**verification standards for civilian casualties**, due", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001240:2:0:1", "start": 1101, "end": 1109, "surface": "GBV data", "probe_tag": "confusion", "probe_score": 0.3471, "luna_label": 0}]}, {"key": "aj-222", "text": "**<u>Forcibly displaced and returned persons in southern Africa, Data as of 30 November 2023*</u>** - \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNotes: The figures are subject to change; *IDPs by disaster are not included; **'Other' in the location refers to any known location other than camp or settlement sites, covering both urban and rural areas; the numbers in Nzakara, Wenze and Sidi in DRC are as of 31 May 2023; ***self-settled refers to\nthe individuals without available information such as their names and locations, and their locations are categorised to be 'unknown'; those by location in Congo, Democratic Republic of the Congo and Zimbabwe could be different from the numbers operation report due to inconsistency in proGres.", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001463:7:0:0", "start": 5, "end": 63, "surface": "Forcibly displaced and returned persons in southern Africa", "probe_tag": "confusion", "probe_score": 0.4096, "luna_label": 0}]}, {"key": "aj-223", "text": "### **Key Findings**\n\nWherever feasible, the key findings have been framed\nusing the sectoral and functional distinctions commonly\nused in the MENA humanitarian and development sector\nresponses.\n\n### **Population Data and** **Information Management**\n\n\n**UNCRPD Article 31 – Statistics and data collection**\n1. States Parties undertake to collect appropriate\ninformation, including statistical and research data,\nto enable them to formulate and implement policies\nto give effect to the present Convention.\n2. The information collected in accordance with this\narticle shall be disaggregated.\n\n### **Data and Trends**\n\n\nDue to the broad definition, the number of individuals\nwho meet the definition of “persons with disabilities” is\nlarge. A 2011 joint report <sup>42</sup> by the WHO and the World\nBank estimates that there are one billion people, i.e. one\nout of every seven people, with a disability across the\nglobe. Most of this group has non-visible impairments,\nthat is, the person’s disability is not necessarily obvious\nto other people. The World Report on Disability\nestimates that 15 per cent of the world’s population has\na moderate or severe disability and that this proportion\nis likely to increase to 18-20 per cent in conflict-affected\npopulations. Conversely, the inclusion of disability in\ncollecting data in emergency and humanitarian contexts\nis very scarce, and publications <sup>43</sup> have referred to the\n‘invisibility’ of persons with disabilities in crisis contexts.\n\n\nUNHCR uses a statistical online population database\n(proGres) to register persons of concern and uses codes <sup>44</sup>\nto collect and flag specific needs and circumstances in\nbiodata. Some of the codes which are relevant to this\nsurvey include those classified as Disability (DS) and as\nSerious Medical (SM). UNHCR is currently in the process\nof revising all special", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "sample:reliefweb:001114:14:0:0", "start": 382, "end": 411, "surface": "statistical and research data", "probe_tag": "confusion", "probe_score": 0.2914, "luna_label": 0}, {"key": "sample:reliefweb:001114:14:0:1", "start": 1508, "end": 1546, "surface": "statistical online population database", "probe_tag": "confusion", "probe_score": 0.7903, "luna_label": 0}, {"key": "sample:reliefweb:001114:14:0:2", "start": 1548, "end": 1555, "surface": "proGres", "probe_tag": "confusion", "probe_score": 0.6488, "luna_label": 0}]}, {"key": "aj-224", "text": "br>51<br>950<br>2,910<br>32<br>2,416<br>-<br>37<br>45<br>1,107<br>4,596<br>502<br>101<br>7,468<br>-<br>847<br>6,012<br>1<br>4,052<br>367<br>16,760<br>23,981<br>9<br>509<br>40<br>475<br>40<br>519<br>745<br>1,509<br>3<br>121<br>1,559<br>23<br>152<br>1,354<br>164<br>1,953<br>1,505<br>-<br>120<br>1<br>18<br>2,829<br>121<br>2<br>397<br>-|\n\n\n\n**42** UNHCR Global Trends 2011 UNHCR Global Trends 2011 **43**", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001077:21:10:1", "start": 346, "end": 370, "surface": "UNHCR Global Trends 2011", "probe_tag": "drop", "probe_score": 0.0327, "luna_label": 0}]}, {"key": "aj-225", "text": "DTM Mali – Décembre 2020\n\n### **PDIS RETOURNÉES**\n\n<u>Tableau III : Nombre Cumulé de PDIs retournées par région de juillet 2013 au 30 avril 2021</u>\n\n|Région|Ménage|Femme|Homme|Individus|\n|---|---|---|---|---|\n|Kayes|41 <br>|125 <br>|106 <br>|231 <br>|\n|Mopti|9457 <br>|25284 <br>|20627 <br>|45 911 <br>|\n|Segou|1136 <br>|3734 <br>|3213 <br>|6 947 <br>|\n|Gao|32 286 <br>|86 457 <br>|69 965 <br>|156 422 <br>|\n|Menaka|17 240 <br>|38 897 <br>|32 026 <br>|70 923 <br>|\n|Kidal|547 <br>|1201 <br>|983 <br>|2 184 <br>|\n|Tombouctou|67 904|167 661|137 956|305 617|\n|**TOTAL**|**128 611 **|**323 359 **|**264 876 **|**588 235 **|\n\n\n\nD’après les évaluations menées en avril 2021, le nombre de PDIs retournées est estimé à **588 235** individus.\nLe nombre de retournés au Mali est passé de 582 079 en décembre 2020 à 588 235 individus en avril\n2021,", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001352:15:0:0", "start": 0, "end": 8, "surface": "DTM Mali", "probe_tag": "drop", "probe_score": 0.0319, "luna_label": 1}]}, {"key": "aj-226", "text": "_Figure [9]: Non-food related coping strategies_\n\n\n<u>PRS</u>\n\n\nDuring an UNRWA assessment in October 2013, participants in 16FGDS indicated the following coping\nmechanisms:\n\n\n  - Reducing essential non-food items such as electricity, water, and transportation, acquiring drinking\nwater from the public tap instead of buying water bottles, and sharing accommodation with other\nfamilies. This had forcibly led many families to live in collective shelters with separators between one\nfamily and another. As a result, this had created lack of privacy as several PRS individuals reported.\n\n  - Sleeping on mattresses instead of beds and borrowing money from other PRS families were also\nnoted.\n\n\n_Debt_\n\n\n  - Oxfam19 found that Syrian refugee household debt was USD 454. This positively correlated with length\nof residency. In Tripoli the average debt was over USD 815 (average residency in Lebanon over 15\nmonths) while in Beirut this was USD 153 per household.\n\n  - The VASyR found that 75% of households had debts and 70% reported borrowing money or receiving\ncredit during the three months before the survey was conducted. The average amount of debt was USD\n600, but half of the interviewed households owed USD 200 or less. Loans were mainly provided by\nfriends or relatives to buy food (81%), pay the rent (52%) or cover health expenses (25%). (Figure 10).\nHouseholds registered longer ago were significantly more likely to have higher amounts of debts.\n\n\n19Oxfam _2014) Winterization 2013-14 Baseline Report. February\n\n23", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "sample:reliefweb:000617:22:0:0", "start": 124, "end": 130, "surface": "16FGDS", "probe_tag": "confusion", "probe_score": 0.8028, "luna_label": 1}, {"key": "sample:reliefweb:000617:22:0:1", "start": 968, "end": 973, "surface": "VASyR", "probe_tag": "drop", "probe_score": 0.0001, "luna_label": 1}]}, {"key": "aj-227", "text": "**Time: 15:00**\n\n# **Next Steps**\n\n\n- **Summary write up of workshop discussions**\n\n- **Preliminary output shared with ISCG to support RRP planning**\n\n- **Follow up agreements to improve or consolidate data sets**\n\n- **Bilateral meetings on specific identified issues**\n\n- **Work together, share information and jointly find solutions** ☺\n\n\n**Winterization Workshop, October 2022**", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001548:50:0:0", "start": 202, "end": 211, "surface": "data sets", "probe_tag": "drop", "probe_score": 0.0271, "luna_label": 0}]}, {"key": "aj-228", "text": "ANNEX > GENERAL EVALUATION\n\n\n**As usual, attendance was strong at**\n**the start (over 90% of the respondents**\n**attended sessions on the first day)**\n**and slowly decreased throughout**\n**the three days, making of the last day**\n**the least attended one. This could**\n**be explained by a very packed**\n**programme which creates some**\n**fatigue at the end of the week.**\n\n\n_Number of respondents – 116_\n_(online survey)_\n\n\n**A large majority of respondents (73%)**\n**indicated that they attended 5 sessions**\n**or more.**\n\n\n_Number of respondents – 99_\n_(paper-based survey)_\n\n\n**79% of respondents are planning to**\n**attend the Consultations next year,**\n**11% are not planning to, 8% do not**\n**know yet.**\n\n\n_Number of respondents – 99_\n_(paper-based survey)_\n\n\n2018 Report > UNHCR Annual Consultations with NGOs **35**", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001325:34:0:2", "start": 556, "end": 574, "surface": "paper-based survey", "probe_tag": "drop", "probe_score": 0.0283, "luna_label": 1}]}, {"key": "aj-229", "text": " students that participated in the study was small (approximately 1%), limiting the statistical\nanalysis and the possibility of disaggregating results by this stratum.\n\n\ny **ERCE 2019:** The same questions about country of birth were included in the family and student\n\nsurveys. Asking students about their country of birth and that of their parents improved the availability\nof information and allowed disaggregating the test results by first- and second-generation migrants.\nStatistically significant differences were found in favour of students born in the country where they took\nthe test for all the grades and subjects assessed (Treviño et al., 2015).\n\n\ny **ERCE 2025** : LLECE is currently coordinating with the participating countries to develop technical guidelines\n\nand instruments to ensure the inclusion of children on the move in selected ERCE participating countries.\nIn particular, the assessment design considers oversampling in schools with a high proportion of immigrant\nstudents to allow comparison based on that stratum. Additionally, the background questionnaires will\ninclude new modules, one of them explicitly focused on human mobility. This module will collect information\nthat will allow access to more comprehensive data on the transnational mobility of the student, bullying and\ndiscrimination associated with student mobility, classroom diversity, and other associated factors that could\nbe related to the learning outcomes of displaced students. The dimensions to be included in this new\nmodule will be confirmed after the pilot studies that will take place in all participating countries between\nAugust 2023 and June 2024.\n\n\nThe adjustments taken by LLECE to promote data inclusion of children on the move is a good practice that\nreflects a comprehensive technical effort to respond to the regional need to better understand the learning\noutcomes of children on the move, mainly displaced Venezuelans. Further, it highlights that international\nlearning assessments hold the potential and technical capacity to promote refugee data inclusion, and to fill\nthe global gap in information about refugees’ learning outcomes.\n\n\n**46**", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "sample:reliefweb:000382:45:1:0", "start": 250, "end": 277, "surface": "family and student\n\nsurveys", "probe_tag": "drop", "probe_score": 0.0378, "luna_label": 1}, {"key": "sample:reliefweb:000382:45:1:1", "start": 2049, "end": 2061, "surface": "refugee data", "probe_tag": "confusion", "probe_score": 0.106, "luna_label": 0}]}, {"key": "aj-230", "text": "### **Better use of disaggregated data**\n\nIn order to understand and respond appropriately to\npeople’s vulnerabilities, needs, capacities and ensure\naccess to life-saving services, humanitarian agencies\nneed to collect information based on sex and age. <sup>15</sup>\nWithout this data, they are unable to effectively\nunderstand and respond to the priorities of older men\nand women. However, the humanitarian system still\ndoes not age-disaggregate its data collection and\nanalysis across all stages of emergency response.\n\n\n**Case study: Myanmar**\n\nOn 2 May 2008, Myanmar was struck by Cyclone\nNargis. High winds, heavy rainfall and tidal surges\nkilled nearly 85,000 people, with roughly 54,000 people\nleft missing and a further 20,000 injured. The cyclone\naffected 2.4 million people – just under one third of\nthe estimated 7.35 million people living in the affected\ntownships. Of these, approximately 200,000 were\n55 years or older at the time of the disaster. <sup>16</sup>\n\nAs part of multi-agency and sector monitoring, from\nSeptember 2008 to August 2009 the Tripartite core\ngroup involving the Association of Southeast Asian\nNations (ASEAN), the United Nations (UN) and the\ngovernment of Myanmar carried out three reviews\nof sector responses to generate data to inform targeted\nassistance, determine future assessments and\naccelerate appropriate response and recovery\nactivities. <sup>17</sup>\n\nWithin the protection element of the review, the\nHelpAge ageing expert seconded to the Global\nProtection Cluster (see page 1) observed gaps in\ninformation gathering about older people. Working with\nprotection agencies, the expert helped to revise the\nmonitoring questions used in the review. This resulted\nin a more holistic analysis and inclusion of information\non older men and women. The new format included\nstandardising the definition of an older person as\nsomeone aged 60+, and disaggregating protection", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001104:5:0:0", "start": 20, "end": 38, "surface": "disaggregated data", "probe_tag": "drop", "probe_score": 0.0243, "luna_label": 0}]}, {"key": "aj-231", "text": " repatriation, resettlement to a third country, local integration, naturalization\nand return to place of origin prior to displacement (for IDPs). However, a growing number of displaced populations\nhave limited opportunities for a durable solution. The COVID-19 pandemic has only exacerbated these challenges.\nThe partial or full closure of borders, along with more general restrictions on movement aimed at limiting the\npandemic’s spread, has dramatically impacted opportunities for displaced people to return to their home\ncountries or resettle to other countries.\n\n\n**Voluntary Repatriation (of Refugees)**\n\n\nFigure 11 **|** **Voluntary Repatriation Trends**\n\nThe region has experienced a constant decline in\nthe number of voluntary refugee repatriations since\n2016 with a sharp drop from 2016 to 2017 and gradual\nreductions with the lowest number in 2020 due\nmainly to travel restrictions as a result of the Covid19\npandemic with only 2,500 returns in the region.\nAfghanistan accounts for at least 90 per cent of all\nreturns in the region, with the largest refugee\nreturnee figures in the region over the last 5 years.\nIOM <sup>10</sup> report some 800,000 undocumented Afghans\nreturn from Pakistan in 2020.\n\n\n**8** \u0007The data for some countries may include a significant number of repeat claims, i.e. the applicant has submitted at least one previous application in\nthe same or another country.\n**9** \u0007The estimated percentage change in first instance asylum applications in 2020 does not include Government-registered refugee populations in\nIran, China, India and Nepal, as detailed information on this caseload is not available.\n**10** afghanistan-return_of_undocumented_afghans_situation_report_20-31_december_2020.pdf (reliefweb.int)\n\n\n14 Asia & the Pacific Regional Population Trends Analysis: Forced Displacement 2020", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "sample:reliefweb:000881:13:1:0", "start": 1224, "end": 1247, "surface": "data for some countries", "probe_tag": "drop", "probe_score": 0.0004, "luna_label": 1}]}, {"key": "aj-232", "text": "**Types of program Implementation, monitoring and evaluation data**\nDifferent types of data help humanitarian actors understand the situation of people living with\ndisabilities and the progress of the response.\n\n\n1. Data at the **individual level** that identifies disability, needs, and barriers individuals may face as well\nas the capacities they may have. Individual data allows the population to be differentiated, providing\nan insight into size of the population of persons with disabilities that allows meaningful planning\ntargets to be set and evaluations to occur. Individual data can be obtained in two ways:\n\n\n  - Data that **extrapolates for the whole population** such as a national census or large-scale sample\nsurvey helps determine prevalence, and is useful to shape programmatic interventions. This type\nof individual data is better gathered in advance of the crisis, and where it exists this type of data\nprovides an excellent baseline against which to assess the response during an evaluation.\n\n\n  - **Administrative processes** where data from individuals is collected during the course of a\nhumanitarian response can also be used effectively to understand how people with disabilities\nare being reached. Data such as collected by UNHCR when a refugee is registered that is entered\ninto the “ProGres” database can be used by the humanitarian community to understand the\nprevalence of persons with disabilities. Administrative data of this type has limitations if it was\nimproperly captured, if individuals were “unregistered”, or their disabilities were “unidentified”.\n\n\n2. **Service level data** on the availability of inclusive services (or barriers to be addressed) does not track\nindividuals, but the proportion of services, facilities or activities in terms of accessibility to persons\nwith disabilities. Data of this type can be used for program planning, setting targets, measuring\nprogress and evaluations. During a humanitarian action, this kind of data may be easier to obtain.\nThis kind of data looks at the proportion of WASH facilities are accessible, for example", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "sample:reliefweb:001044:14:0:0", "start": 686, "end": 701, "surface": "national census", "probe_tag": "confusion", "probe_score": 0.1314, "luna_label": 1}, {"key": "sample:reliefweb:001044:14:0:1", "start": 705, "end": 730, "surface": "large-scale sample\nsurvey", "probe_tag": "drop", "probe_score": 0.0326, "luna_label": 0}, {"key": "sample:reliefweb:001044:14:0:2", "start": 1311, "end": 1318, "surface": "ProGres", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1}, {"key": "sample:reliefweb:001044:14:0:3", "start": 1430, "end": 1449, "surface": "Administrative data", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1}, {"key": "sample:reliefweb:001044:14:0:4", "start": 1596, "end": 1614, "surface": "Service level data", "probe_tag": "confusion", "probe_score": 0.2674, "luna_label": 0}]}, {"key": "aj-233", "text": ", the MSNA team have found the following response gaps:\n\n\n  - Approximately two-thirds of the 225 vulnerable cazas reportedly do not have WASH programmes. The\nnumber of vulnerable cazas continues to increase as more Syrian refugees come into the country.\n\n  - Hygiene programmes.\n\n  - Water and sanitation infrastructure at the community and municipal levels\n\n  - Longer-term infrastructure projects\n\n\nThe participants of the MSNA SWG workshop identified the following response gaps:\n\n\n  - Rapid response – the time it takes between the start and completion of an intervention\n\n  - Preparedness for an outbreak\n\n  - The strategy – partners do not have one focus geography, but are spread across the area\n\n  - Coordination of an exit strategy (with development projects)\n\n  - Coverage of all areas and all services\n\n  - Collective shelters\n\n  - Extra support for the municipalities\n\n  - Sewage and wastewater treatment\n\n\n**1.5 Future Developments with Possible Impacts on the Sector**\n\n\nBased on the data available, MSNA team have found the following future developments may have an impact\non the sector:\n\n\n  - With dwindling economic resources, access to municipal water is decreasing and alternatives such as\nwater trucking are being used, creating potential over-pumping of wells, which could lead to well\n\n6", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "sample:reliefweb:000826:5:1:0", "start": 999, "end": 1013, "surface": "data available", "probe_tag": "drop", "probe_score": 0.0001, "luna_label": 1}]}, {"key": "aj-234", "text": ". car(s)<br>7. other<br>8. prefer not to answer<br>9. Don’t own||\n|Housing, Land and Property||**K3**|K3_1 Do you wish to participate in Focus Group<br>Discussions related to the study in about two<br>months?|1. Yes<br>2. No||\n|Housing, Land and Property||**K3**|K3_2 Phone number?|||\n|End||**L1**|L Register GPS coordinates|||\n\n\n80", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000440:79:4:0", "start": 309, "end": 324, "surface": "GPS coordinates", "probe_tag": "drop", "probe_score": 0.0269, "luna_label": 0}]}, {"key": "aj-235", "text": " se definen\nen función de su lugar de nacimiento.\nLa muestra no ponderada incluyó\na 3,018 ecuatorianos y 1,261\nvenezolanos.\n\n\nPerú\n\n\n**El análisis de venezolanos en Perú**\n**se hizo con base en la ENPOVE**,\nrealizada por el INEI y financiada por\nel Banco Mundial.\n\n\nLa encuesta se realizó en dos rondas.\nLa primera se ejecutó en noviembrediciembre del 2018. La segunda se\nrealizó en febrero-marzo del 2022,\ncon el apoyo del Centro Conjunto de\nDatos (JDC por sus siglas en inglés)\nde ACNUR y el Banco Mundial.\nAmbas rondas fueron conducidas\nde manera presencial. Aportan\ninformación detallada sobre las\ncaracterísticas de la vivienda, y de\nlos miembros del hogar, como por\nejemplo, estatus migratorio, salud,\neducación, empleo, discriminación,\ngénero y victimización. El presente\ninforme utiliza información de la\nronda 2022, que cuenta con una\nmuestra de 7,751 individuos.\n\n\n**La** **información** **sobre** **las**\n**comunidades de acogida se obtuvo**\n**de la ENAHO** . Implementada por\nel INEI, la ENAHO permite obtener\n\n\n\nVenezolanos en Chile, Colombia, Ecuador y Perú\n\n       - una oportunidad para el desarrollo\n\n\ninformación detallada sobre las\ncaracterísticas de la vivienda y\nlos miembros del hogar. Se utiliza\npara monitorear la evolución de los\nindicadores de pobreza, bienestar\ny condiciones", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "sample:reliefweb:001322:55:2:0", "start": 197, "end": 203, "surface": "ENPOVE", "probe_tag": "drop", "probe_score": 0.0024, "luna_label": 1}, {"key": "sample:reliefweb:001322:55:2:1", "start": 961, "end": 966, "surface": "ENAHO", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1}]}, {"key": "aj-236", "text": " First, the\nlikely size of the MoLSA budgets will determine how many new households can enter the program.\nSecond, the degree of confidence of a successful referral is taken into account. Referring more people will\nincrease the absolute number of households being determined eligible after MoLSA assessment but will\nalso increase the absolute number of households being referred and not being determined eligible,\npotentially resulting in resentment and mismanaged expectations. Thus, thresholds were set for differing\ndegrees of confidence that a given pseudo-PMT score would result in eligibility when the full MoLSA PMT\nis applied. To simplify the exposition, we present the results for targeting poorest 18 percent of the\npopulation under low (50 percent), medium (70 percent), and high (90 percent) referral confidence. The\nresults for other targeting thresholds – 25 percent, 30 percent, and 35 percent – are presented in the\nAnnex.\n\n\n4.1 Likely Eligibility of Humanitarian Beneficiaries under MoLSA Targeting\n\nInitial aggregate results highlight the potential for both significant referral numbers and a sequenced\nstrategy. Table 7 presents a first scenario with a smaller MoLSA budget for varying level of referral\nconfidence. For a MoLSA program targeting the poorest 18 percent of the population and requiring a 90\npercent confidence in the referral based on the pseudo-PMT, 24.3 percent of the existing humanitarian\ndatabase would be eligible for referral. For the same level of targeting, 44.2 and 54.1 percent of the\n\n\n13 At the time of the analysis, the new MCNA models were yet to be implemented for the humanitarian assistance\neligibility. Besides it being limited to the Norther governorates, the beneficiary database was entirely based on the\n2016 VM targeting methodology. Hence, the analysis in this section focuses on the 2016 VM pseudo-PMT model\nfor the North only. Similar analytical exercise using the database resulting from employing the SEVAT targeting\ncriteria and for other regions using other CWG partner’s database remains part of the next steps.", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000883:15:1:1", "start": 1714, "end": 1734, "surface": "beneficiary database", "probe_tag": "confusion", "probe_score": 0.5295, "luna_label": 1}]}, {"key": "aj-237", "text": "**2.** **Methodology** <sup>**5**</sup> [^5: This section is based on the Manual for the ADePT Simulation module which can be consulted for further details.\n6A similar approach has been applied recently to update poverty and distributional studies on a number of developing countries,\nincluding Costa Rica, Panama, Moldova, Tajikistan and Mexico.]\n\nA number of methodologies can be employed to simulate an income or consumption distribution and\nassociated poverty measures when household survey data are missing. However, not all of them are adequate\nin addressing the kind of questions posed above. In this paper we propose a micro-simulation model focused\non multiple transmission channels which seem appropriate for the Serbian context, namely (i) labor markets\nas a key pathway for the deterioration of the economic situation in Serbia over the past years; as well as (ii)\nnon-labor income components, mainly international remittances; and (iii) price changes.\n\n\nThe micro-simulation approach superimposes macroeconomic projections on behavioral models built on last\navailable household survey (HBS 2010). The model is loosely based on previous approaches to microsimulation described in Bourguignon, Bussolo and Pereira da Silva (2008) and Ferreira, et al. (2008) – with an\nimportant simplification of omitting the computable general equilibrium (CGE) component, which is difficult\nto employ in most developing countries. Instead the approach described here links the behavioral model to\nsectoral and aggregate macroeconomic data for Serbia in 2011, and extrapolates the microeconomic snapshot\nof a future scenario from this projection. <sup>6</sup>\n\n\nUsing actual macroeconomic data for the period 2010-2011, we are able to predict income distributions at the\nindividual and household level. The poverty and distributional assessment can be performed by comparing\nthe simulated scenario with 2010 data. The model focuses on labor markets and (international) remittances as\ntransmission mechanisms and allows for changes to labor income- modeled as employment changes, earning\nchanges or a combination of both- and changes to", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:006003:4:0:0", "start": 478, "end": 499, "surface": "household survey data", "probe_tag": "confusion", "probe_score": 0.7945, "luna_label": 1}]}, {"key": "aj-238", "text": "terms of the volume and variability of capital ‡ows, so that cross-sectional heteroskedasticity\n\nin particular is a concern. Below we use the HAC covariance estimator of Andrews and\nMonahan (1992) to estimate - <sup>^</sup> from the residuals of a …rst-round estimation.\n\n\nThe above discussion assumes that the numbers of global and group factors are known a\n\npriori, which in practice is rarely the case. Following Choi and Jeong (2017), we determine\n\nthe appropriate number of factors using the ICp2, BIC; and HQ criteria, as adapted to the\nmulti-level setting by Choi et al (2017). <sup>8</sup>\n\n#### 3 Data\n\n\nTo study the global and group patterns of capital ‡ows, we assemble a large cross-country\n\ndataset drawing from the International Monetary Fund’s Balance of Payments Statistics\n\n(BoP). After dropping countries with incomplete data and very small economies, we end up\n\nwith a balanced panel of 85 countries covering the years 1979-2015, with a total of 3,145\nobservations. <sup>9</sup> We classify the countries into three groups: advanced (19 countries), emerging\n\n(28) and developing (38). The list of countries and their grouping are given in Table A1 in\n\nthe appendix.\n\n\nFollowing Broner et al (2013), we construct two measures of capital ‡ows from the BoP\n\ndata:\n\n\ni. Capital in‡ows by foreign agents (CIF): the sum of direct investment in the report\ning economy, portfolio investment liabilities, and other investment liabilities.\n\n\nii. Capital out‡ows by domestic agents (COD): the sum of direct investment abroad,\n\nportfolio investment assets, other investment assets, and international reserve assets.\n\n\nThese measures of ‡ows relate to the", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:007302:10:0:2", "start": 1269, "end": 1278, "surface": "BoP\n\ndata", "probe_tag": "confusion", "probe_score": 0.7763, "luna_label": 1}]}, {"key": "aj-239", "text": "u>to 07</u> <u>SOE</u> <u>FIE</u> <u>LP</u> <u>SME</u>\n\n**<u>Gross Exports</u>** **<u>11191</u>** **<u>9.72</u>** <u>1052</u> <u>6340</u> <u>1164</u> <u>2635</u>\n\n\n\n**<u>2010</u>** <u>Total</u>\n\n\n\nchange relative\n\n\n\nVA Contribution\n\n(%)\n\n\n\nSOE 16.27 14.77 50.32 12.53 16.41 11.60\n(34.44, 15.89)\nFIE 19.71 9.17 8.09 28.95 9.82 6.46\n(19.77, 9.18)\nLP 7.46 5.81 5.54 5.18 27.78 4.73\n(21.29, 6.49)\nSME 30.44 5.56 18.14 20.83 23.56 61.53\n(34.59, 26.93)\n<u>Abroad</u> <u>26.12</u> <u>-18.07</u> <u>17.90</u> <u>32.50</u> <u>22.42</u> <u>15.69</u>\n\n\n\nNote: Estimation based 2007 and 2010 IO Table. Numbers in brackets are direct and indirect VA export share, respectively,\n\n\n\n39", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:006064:40:1:0", "start": 580, "end": 588, "surface": "IO Table", "probe_tag": "confusion", "probe_score": 0.2232, "luna_label": 1}]}]