queue_annotate_aj.json

[{"key": "aj-000", "text": " of US$1,257 and among the ten\nlowest worldwide. Ethiopia is ranked 173 out of 187 countries in the Human Development Index\n(HDI) of the United Nations Development Program (UNDP). However, high economic growth\nhas helped reduce poverty, in both urban and rural areas. Since 2005, 2.5 million people have\nbeen lifted out of poverty, and the share of the population below the poverty line has fallen from\n38.7 percent in 2004/05 to 29.6 percent in 2010/11 (using a poverty line of US$0.6/day).\nHowever, because of high population growth the absolute number of poor (about 25 million) has\nremained unchanged over the past fifteen years. Ethiopia is among the countries that have made\nthe fastest progress on the Millennium Development Goals (MDGs) and HDI ranking over the\npast decade. It is on track to achieve the MDGs related to gender parity in education, child\nmortality, HIV/AIDS, and malaria. Good progress has been achieved in universal primary\neducation, although the MDG target may not be met. The reduction of maternal mortality\nremains a key challenge.\n\n5. **GoE is currently implementing its ambitious Growth and Transformation Plan**\n(GTP; 2010/11-2014/15), which sets a long-term goal of becoming a middle-income country by\n2023, with growth rates of at least 11.2 percent per annum during the plan period. To achieve the\nGTP goals and objectives, GoE has followed a “developmental state” model with a strong role\nfor the government in many aspects of the economy. It has prioritized key sectors such as\nindustry and agriculture, as drivers of sustained economic growth and job creation. The GTP also\nreaffirms GoE’s commitment to human development. Development partners have programs that\nare broadly aligned with GTP priorities.", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:018659:1:1:0", "start": 100, "end": 123, "surface": "Human Development Index", "probe_tag": "keep", "probe_score": 0.9557, "luna_label": 1, "luna_reason": "HDI ranking provides a concrete attributed development finding."}]}, {"key": "aj-001", "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:014581:19:1:0", "start": 272, "end": 283, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9509, "luna_label": 1, "luna_reason": "Existing survey data underlie reported enrollment differences by expenditure quintile."}]}, {"key": "aj-002", "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:010872:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Household survey data supports enrollment-gap findings and indicates education-level separation limits."}, {"key": "fcv_pads_east_africa:010872:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1, "luna_reason": "Existing household survey data supports a concrete finding about girls’ school withdrawal."}]}, {"key": "aj-003", "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:015679:19:1:0", "start": 272, "end": 283, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9509, "luna_label": 1, "luna_reason": "Existing survey data supports reported enrollment inequality findings."}]}, {"key": "aj-004", "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:009490:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Survey data supports reported enrollment-rate differences across expenditure quintiles."}]}, {"key": "aj-005", "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:019477:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1, "luna_reason": "Survey data directly supports reported enrollment-rate disparities."}, {"key": "fcv_pads_east_africa:019477:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Survey data underpin enrollment comparisons and show secondary education was not separately distinguished."}]}, {"key": "aj-006", "text": ".22|0.55|\n|IDA-53120|Effective|USD|10.00|10.00|0.00|0.00|10.25|\n|TF-97272|Effective|USD|46.28|46.28|0.00|36.79|9.49|\n\n\n**<u>Disbursement Graph</u>**\n\n\n**<u>Key Decisions Regarding Implementation</u>**\n(a) Planned numbers of households to be enrolled in the next FYs and geographic targeting informed by poverty and vulnerability data will be included in the NSNP Expansion Plan.\nThis Expansion Plan will be approved by Cabinet Secretary (MLSSS).\n(b) The MLSSS will engage the National Treasury to identify means for ensuring that allocation of government funds to cash transfer programs is timely, regular and predictable.\n(c) The MLSSS will develop a tracking system to monitor performance with regards to timeliness of payments and define a strategy to address constraints to predictable delivery of\nbeneficiary payments.\n\n\n**<u>Restructuring History</u>**\n\n\nPage 6 of 7", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:019408:5:1:0", "start": 303, "end": 333, "surface": "poverty and vulnerability data", "probe_tag": "confusion", "probe_score": 0.6259, "luna_label": 1, "luna_reason": "Existing data informs geographic targeting in the expansion plan."}]}, {"key": "aj-007", "text": "**The World Bank**\nNational Safety Net Program for Results (P131305)\n\n\n**ANNEX 1. RESULTS FRAMEWORK, DISBURSEMENT-LINKED INDICATORS, AND PROGRAM ACTION PLAN**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Indicator Name|Unit of Measure|Baseline|Original Target|Formally Revised<br>Target|Actual Achieved at<br>Completion|\n|---|---|---|---|---|---|\n|Net change in beneficiary<br>household monthly per adult<br>equivalent consumption<br>expenditure <br>|Number|0.00|150.00||66.00|\n|Net change in beneficiary<br>household monthly per adult<br>equivalent consumption<br>expenditure <br>||26-Jun-2013|31-Dec-2020||31-Dec-2020|\n|<br>**Comments (achievements against targets):** <br>This indicator was to be measured by the CT-OVC Impact Evaluation and the HSNP Impact Evaluation. Unfortunately, the results from the CT-OVC Impact<br>Evaluation show that due to a number of reasons (the quality of the consumption data collected for the end line with food consumption likely to be<br>significantly underreported as well as the high attrition rate), it is difficult to detect the true impact of the CT-OVC program on the household food<br>consumption. <br>According to the HSNP Impact Evaluation, the Propensity-score matching (PSM) results confirm an impact on monthly food expenditure, showing that<br>households that have received a regular HSNP payment experience an increase in monthly per adult equivalent food expenditure of around Ksh66.<br>However, the spill-over", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:016570:41:0:0", "start": 867, "end": 883, "surface": "consumption data", "probe_tag": "confusion", "probe_score": 0.6698, "luna_label": 1, "luna_reason": "End-line consumption data were analyzed to assess program impact despite underreporting concerns."}]}, {"key": "aj-008", "text": " identified); and most importantly\nthese need to be audited by inspectors or and external auditors as part of the normal annual\naudit.\n\n  - **_Cutoff and Action on Balances-_** It is important to identify balances these are (a) regional balances\nappearing in MoF records at the start of the conflict (Nov 3, 2020, and balances per regional\nrecords at the start of the conflict, which should obviously be reconciled; and (b) balance at the\npoint of set up of the current regional interim administration (March 23, 2023); and (c) identify\ntransactions that happened in between these two dates. All these are to be audited by external\nauditors. The reports should be sent to us along with the damage assessment report by\nSeptember 30, 2023. As noted above, transactions that happened after the setup of the interim\nadministration need to be recorded in the accounting records (either in the new or old accounts)", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:001376:9:2:0", "start": 259, "end": 270, "surface": "MoF records", "probe_tag": "confusion", "probe_score": 0.0683, "luna_label": 0, "luna_reason": "Financial records used for balance reconciliation and audit bookkeeping."}, {"key": "fcv_pads_east_africa:001376:9:2:1", "start": 331, "end": 347, "surface": "regional\nrecords", "probe_tag": "confusion", "probe_score": 0.2411, "luna_label": 0, "luna_reason": "Regional accounting records are used for balance reconciliation and audit bookkeeping."}]}, {"key": "aj-009", "text": "completed sub-projects local authorities provide\nconform to ministry standards, resources and staff to operate\ndesigns and norms. and maintain facilities (e.g.\nprovision of teachers and\n\ntextbooks in the case of\nprimary schools);\n\n\nl(b) Targeted communities are lb. 1 At <sup>least 90%</sup> <sup>of</sup> - Beneficiary/ impact - Sub-projects reflect\nempowered to carry out projects are assessed as assessments and other beneficiary needs and\npriority investments. successful by communities evaluation reports improved access to social and\n(achieve rmnimum expected economic services;\noutputs and\noutcomes/imnpacts).\n\n                                     - Supervision missions                                     - PPA methodology is\nlb.2 All supported                  - Beneficiary Assessments internalized by NaCSA and\ncommunities have conducted                - NSAP quarterly progress partners;\nparticipatory needs reports\nassessments and project                 - Participatory M&E results\nidentification using PPA\napproach.                 - Continuous social                 - Capacity building efforts\nI assessment process provided and/or coordinated\nlb.3 100% of communities                  - Participatory M&E results by NaCSA are appropriate and\nhave project management effective;\nstructures in place and trained\ncommunity members.\n\n**2.** Pilot and Special 2a.1 100 km. of feeder roads - Supervision <sup>missions;</sup> - NaCSA's commitment to\n\n**Programs in** Newly rehabilitated. - NSAP quarterly reports; pilot both programs remains\n**Accessible** **Areas** - Annual technical audits; strong\n2a.2 800,000 person days of                  - NaCSA M&E data\n**2(a)** **Rural Public Works** temporary employment\n**Program:** created.\n\nInfrastructure constructed 2a.3 250,000 \"woman days\" of\nand/or upgraded using labor temporary employment\nintensive techniques. created.\n\n\n2a.4 At mid-term review the\ncost per day", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:012956:30:0:0", "start": 1658, "end": 1672, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "NaCSA monitoring data verifies the temporary-employment output."}]}, {"key": "aj-010", "text": "**The World Bank** Implementation Status & Results Report\nCompetitiveness and Enterprise Development Project (CEDP) (P130471)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**Data on Financial Performance**\n\n\n**Disbursements (by loan)**\n\n\nProject Loan/Credit/TF Status Currency Original Revised Cancelled Disbursed Undisbursed % Disbursed\n\n\nP130471 IDA-52690 Closed USD 100.00 100.00 0.00 92.64 0.00 100%\n\n\nP130471 IDA-65380 Effective USD 99.80 99.80 0.00 58.12 38.69 60%\n\n\n**Key Dates (by loan)**\n\n\nProject Loan/Credit/TF Status Approval Date Signing Date Effectiveness Date Orig. Closing Date Rev. Closing Date\n\n\nP130471 IDA-52690 Closed 09-May-2013 31-Jan-2014 06-Jun-2014 31-Mar-2019 31-May-2022\n\n\nP130471 IDA-65380 Effective 02-Mar-2020 09-Nov-2020 06-Apr-2021 31-May-2022 30-May-2024\n\n\n**Cumulative Disbursements**\n\n\n1/23/2024 Page 13 of 14", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:000267:12:0:0", "start": 143, "end": 172, "surface": "Data on Financial Performance", "probe_tag": "confusion", "probe_score": 0.0988, "luna_label": 0, "luna_reason": "Standalone table heading for project financial performance data."}]}, {"key": "aj-011", "text": "The World Bank\n\n|Col1|Col2|Col3|Comments|Source: Routine CBN data,<br>FMOH<br>(revised indicator definition,<br>baseline value and data<br>source)|Source: Routine CBN data,<br>FMOH|Routine CBN data, FMOH|\n|---|---|---|---|---|---|---|\n|Number and percentage of children 6-59<br>months receiving a dose of Vitamin A every 6<br>months||Number<br>Sub Type<br> Breakdown|Value|10200000.00|10700000.00|11300000.00|\n|Number and percentage of children 6-59<br>months receiving a dose of Vitamin A every 6<br>months||Number<br>Sub Type<br> Breakdown|Date|29-Apr-2008|08-May-2013|07-Jan-2014|\n|Number and percentage of children 6-59<br>months receiving a dose of Vitamin A every 6<br>months||Number<br>Sub Type<br> Breakdown|Comments|UNICEF EOS 2008 data<br>(revised indicator definition<br>and baseline value)|Source: FMOH 2010-11 data|Source: Routine CHD/EOS<br>reports, FMOH|\n|Number and percentage of children 6-59<br>months receiving a dose of Vitamin A every 6<br>months|||||||\n\n\n\n**<u><mark>Intermediate Results Indicators</mark></u>**\n\n\n\n\n\n\n\nReport No: ISR10608\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Indicator Name|Core|Unit of Measure|Col4|Baseline|Current|End Target|\n|---|---|", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:013876:2:0:1", "start": 49, "end": 65, "surface": "Routine CBN data", "probe_tag": "confusion", "probe_score": 0.5116, "luna_label": 1, "luna_reason": "Source-line data cited for indicator values presented in the table."}, {"key": "fcv_pads_east_africa:013876:2:0:3", "start": 810, "end": 827, "surface": "FMOH 2010-11 data", "probe_tag": "confusion", "probe_score": 0.8119, "luna_label": 1, "luna_reason": "Table source line identifies FMOH data underlying the indicator."}]}, {"key": "aj-012", "text": "Annex 6\nPage 5 of 6\n\n\n\n**Table B: Thresholds for Procurement Methods and Prior Review 1**\n\n\n\n**Expenditure Category** **Contract Value** **Contracts Subject to**\n**Threshold** **Procurement** **Prior Review**\n(US$ thousands) Method (US$ millions)\n1. **Works** - US$50,000 NCB All ICB if any.\n< US$50,000 Simplified NCB NCB contracts above US$150,000\nFirst 5 contracts regardless of\nvalue; first 3 contracts for each\n\n\n\nyear starting January 1.\n**2. Goods** - US$100,000 ICB All ICB.\n< US$100,000 NCB NCB contracts above US$70,000\n< US$50,000 IS or NS where there are at First 5 contracts regardless of\n\n\n\nleast 3 capable national value; first 3 contracts for each\nsuppliers. year starting January 1.\n<US$10,000 DC\n**3. Services** - US$50,000 QCBS Contracts above US$200,000\n(Firms) would be advertised in the UNDB.\n\n              - US$25,000 Contracts for firms above\n(Indiv.) CQ US$50,000, and for individuals\nSS above US$25,000.\n< US$25,000 First 5 contracts regardless of\n=< US$15,000 value; first 3 contracts for each\nyear starting January 1.\nAll TORs\n_____________________________________________________________IAll Sole Source\nThresholds generally differ by country and project.", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:012936:47:0:0", "start": 809, "end": 813, "surface": "UNDB", "probe_tag": "confusion", "probe_score": 0.223, "luna_label": 0, "luna_reason": "Span appears within a procurement thresholds table artifact."}]}, {"key": "aj-013", "text": " not only because there are uncertainties associated with the sector but also to ensure that\nrelevant skills are developed with strong linkages with the private sector. The consultations will\nprovide a basis for the skills assessment that will follow; (2) Skills Assessment for Oil and Gas\nSector: Based on the findings of the consultations, a firm will conduct an in-depth analysis of the\nskills gaps in the extractives sector. The consultants will make use of the National Skills\nAssessment Report and the latest Manpower Survey conducted by the Ministry of Labour to\ninform the depth of the skills assessment; and (3) National Skills Development\nStrategy/Framework for O&G sector: In light of the gaps identified, the Government will\ndevelop a Skills Development strategy/Framework, which will provide guidance on the types of\nmodels that can be used to address the skills gaps (this particular activity (C2.3) in the amount of\nUS$4.5 million is expected to be funded by DFATD); and (4) Operational Support: The Bank\nteam will explore options to support piloting of different models of skills development including\nPPPs **.**\n\n\n41. **Activity C2.1. Stimulate Local Skills Development** . The identification and design\nphase will include (i) Demand-Side Analysis – assess the skills required in the Kenyan oil and\ngas sector, in cooperation with NOCK and the private sector, taking into account scenarios for\nthe growth of the petroleum sector; (ii) Skills Gap Analysis – Assessment of manpower /\nworkforce requirements with IOCs / operators and EPCs to identify workforce numbers of\nemployees per categories (skilled, semi-skilled, and unskilled); (iii) Educational Gap Analysis –\nConduct of an analysis of educational institutions in Kenya and a baseline diagnostic assessment\n(including analysis of strengths and weaknesses) of current educational capabilities to meet\nrequirements of local and national companies; (iv) Development of an Educational\nDevelopment Program for the academic, technical", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:010440:49:1:1", "start": 515, "end": 530, "surface": "Manpower Survey", "probe_tag": "confusion", "probe_score": 0.8819, "luna_label": 1, "luna_reason": "Existing survey findings inform the planned skills assessment."}]}, {"key": "aj-014", "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:013218:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": 0.1197, "luna_label": 1, "luna_reason": "Named administrative data source listed for program performance verification."}, {"key": "fcv_pads_east_africa:013218:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Administrative data is listed without a concrete finding or demonstrated use."}]}, {"key": "aj-015", "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:015084:40:0:0", "start": 1148, "end": 1181, "surface": "Random surveys of school children", "probe_tag": "confusion", "probe_score": 0.3714, "luna_label": 0, "luna_reason": "Future surveys will be conducted to assess enrollment progress."}]}, {"key": "aj-016", "text": " amount\nof waste/animal manure fed into the biogas system.\n\n  - The proper soil application (not resulting in methane emissions) of the final\nsludge verified on a sampling basis.\n\n  - The baseline manure handling practice at a sample of all biodigester users. These\nexact data will substitute MCFj and MS%Blj estimated in the respective SCCCPA-DDs.\n\nWhere AMS-III.R applies, operational data that installed systems are still in use will be\ncollected. This data will be collected through a biogas data logger which checks if the gas\nis flowing in a set time interval, or through a survey to check that the systems are\noperational, conducted via sampling. Average annual hours of operation will be\ndetermined through asking users how many days a year they use the biodigesters, and for\nhow many hours per day, on average, they cook using biogas. Through a Manure\nManagement Survey, part of the Monitoring Survey, the CME will also ask users to\nestimate their annual average animal population, as well as to estimate the quantity of\nmanure generated on the farm and the quantity fed into the system. Users will also be\nasked to detail the methods used to apply the final sludge to soil.\n\n\n30", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:019344:31:1:0", "start": 375, "end": 391, "surface": "operational data", "probe_tag": "confusion", "probe_score": 0.8323, "luna_label": 0, "luna_reason": "Operational data will be collected through loggers or surveys."}]}, {"key": "aj-017", "text": " is based on data from 2017, which was the last time<br>MoES had a comprehensive Education Management Information System (EMIS). Enrollments in<br>target areas have increased in the interim for reasons outside the influence of the project. The<br>new, recently developed EMIS is being populated with values to be available by the end of the<br>year.|Current values are based on a survey conducted by the PCU in August/September 2023. Values<br>exceed the end targets because the RF is based on data from 2017, which was the last time<br>MoES had a comprehensive Education Management Information System (EMIS). Enrollments in<br>target areas have increased in the interim for reasons outside the influence of the project. The<br>new, recently developed EMIS is being populated with values to be available by the end of the<br>year.|Current values are based on a survey conducted by the PCU in August/September 2023. Values<br>exceed the end targets because the RF is based on data from 2017, which was the last time<br>MoES had a comprehensive Education Management Information System (EMIS). Enrollments in<br>target areas have increased in the interim for reasons outside the influence of the project. The<br>new, recently developed EMIS is being populated with values to be available by the end of the<br>year.|Current values are based on a survey conducted by the PCU in August/September 2023. Values<br>exceed the end targets because the RF is based on data from 2017, which was the last time<br>MoES had a comprehensive Education Management Information System (EMIS). Enrollments in<br>target areas have increased in the interim for reasons outside the influence of the project. The<br>new,", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:004008:3:3:0", "start": 13, "end": 27, "surface": "data from 2017", "probe_tag": "confusion", "probe_score": 0.6811, "luna_label": 0, "luna_reason": "Bare date-only data qualifier cannot inherit the adjacent EMIS source."}, {"key": "fcv_pads_east_africa:004008:3:3:1", "start": 380, "end": 407, "surface": "survey conducted by the PCU", "probe_tag": "confusion", "probe_score": 0.894, "luna_label": 0, "luna_reason": "Survey was conducted by the project unit to generate current values."}]}, {"key": "aj-018", "text": " 2009, which authorizes banks to\nappoint agents (usually shopkeepers) to provide limited banking services such as taking deposits and managing withdrawals.\n18 Smartcards are electronic cards used to access a bank account or a cash transfer that contains biometric information unique to\nthe client. In other words, the PCK uses one-factor authentication and Equity bank uses two-factor authentication. Annex 4\npresents a more detailed analysis of these issues.\n\n\n20", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:020604:27:2:0", "start": 254, "end": 275, "surface": "biometric information", "probe_tag": "confusion", "probe_score": 0.1777, "luna_label": 0, "luna_reason": "Merely describes smartcard authentication; no data use or source resource is shown."}]}, {"key": "aj-019", "text": "Resettlement Policy Framework                                                  Page **30** of **73**\n\n\n**Conclusion**\n\nAs the analysis shown in Table 2 indicated, there is no substantial mismatch between WB’s\npolicy on resettlement and that of GOE’s laws on the same issue. Land is publicly held in\nEthiopia; hence all landholders have only use rights. Compensation of land is, therefore,\nsubject to standard plots regardless of the size of previous holding. The Federal Government’s\nlaws are replicated by regional governments with minor changes; hence there is consistency\ncountry-wide. In relation to the ESTDP the stringent rules (WB or GOE) will be applied to\nsafeguard the livelihood of resettlers; i.e. in case of conflict or discrepancies, for purposes of\nproject, WB standards and policies will apply and overrule National ones\n#### **6.0 Categories of assets, method of valuation and delivery** **of entitlements**\n\n##### **Category of assets for compensation**\n\n\nThe categories of assets of persons who would be displaced as a result of the tourism\ndevelopment projects include the following.\n\n\n   - Buildings for various uses\n\n\n   - Fence\n\n\n   - Septic tank\n\n\n   - Water tank\n\n\n   - Farm land\n\n\n   - Grazing land\n\n\n   - Fruit bearing trees and plants having economic value (perennial plants)\n\n\nTown administrations use directive given by regional Bureau of Works and Urban\nDevelopment to determine list of components and unit rates for compensation. The unit rate\nindex is updated periodically (quarterly in the case of ANRS) and all towns in the region use\nthe index uniformly. The compensation for farm land and fruit-bearing trees is regulated by\nthe directive from the regional Bureau of Agriculture and Rural Development.\n\n\nIn resettlement projects it is not enough to limit the compensation to immovable property\nalone; displaced persons loose means of livelihoods too. Therefore, the category of assets to\nbe considered for compensation should include the following components:\n\n\n   - Income generating activities performed", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:015996:29:0:1", "start": 1532, "end": 1536, "surface": "ANRS", "probe_tag": "confusion", "probe_score": 0.8144, "luna_label": 0, "luna_reason": "A regional acronym, not a named data resource or source."}]}, {"key": "aj-020", "text": "business<br>owner|Land for land replacement or compensation in cash according to PAP’s choice. Land for land<br>replacement will be provided in terms of a new parcel of land of equivalent size and market potential<br>with a secured tenure status at an available location which is acceptable to the PAP.<br> <br>Transfer of the land to the PAP shall be free of taxes, registration & other costs.<br> <br>Relocation assistance (costs of shifting + allowance)<br> <br>Opportunity cost compensation equivalent to 2 months net income based on tax records for previous<br>year (or tax records from comparable business, or estimates).|\n|Displacement:<br>Premise used for business severely<br>affected, remaining area<br>insufficient for continued use|Business owner is lease<br>holder|Opportunity cost compensation equivalent to 2 months net income based on tax records for previous<br>year (or tax records from comparable business, or estimates), or the relocation allowance, whichever is<br>higher.<br> <br>Relocation assistance (costs of shifting)<br> <br>Assistance in rental/lease of alternative land/property (for a maximum of 6 months) to re-establish the<br>business|\n|BUILDINGS & STRUCTURES|BUILDINGS & STRUCTURES|BUILDINGS & STRUCTURES|\n|No displacement:<br>Structure partially affected but the<br>remaining structure remains viable<br>for continued use|Owner<br>|Cash compensation for affected building and other fixed assets<br> <br>Cash assistance to cover costs of restoration of the remaining structure|\n|No displacement:<br>Structure partially affected but the<br>remaining structure remains viable<br>for continued use|Rental/lease", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:009352:62:1:0", "start": 538, "end": 549, "surface": "tax records", "probe_tag": "confusion", "probe_score": 0.3687, "luna_label": 1, "luna_reason": "Existing tax records provide income figures for compensation calculation."}, {"key": "fcv_pads_east_africa:009352:62:1:1", "start": 575, "end": 611, "surface": "tax records from comparable business", "probe_tag": "confusion", "probe_score": 0.1073, "luna_label": 1, "luna_reason": "Existing comparable-business tax records support calculating opportunity-cost compensation."}]}, {"key": "aj-021", "text": "6\n\n\nThere is strong support in the Government for increasing resources for education, and the Government\nmade a commitment to increase education's share of budget from 16% in 2001-02 to 25% in 2009-10.\n\n\nOne of the reasons for choosing an APL with a ten-year perspective is that the education budget\nshortages will continue to be a constraint in the next few years. Over this period, Government\n\nexpenditures in non-priority areas will be brought under control and Government expenditures on\neducation can be expected to increase significantly. Despite the manageability in the long-run, the\nshort-run prospects on the budget are more challenging and donors will need to finance some recurrent\ncosts. The proposed APL will be implemented in three phases with distinct triggers (see Section B. 4).\nAs a result, a 10-year projection of enrollments and education costs has been developed (which is the\noverall framework for the APL), and a detailed five year plan and project proposals have been\nprepared (which is the framework for the first phase of the APL).\n\n\n3. Sector issues **to be addressed by the project and strategic choices**\n\nThe project will directly address all the issues below except for higher education.\n\n\n_Issues/Sector Problems_ _Government strategy and project proposal_\n\n**School Places**\n\nThe immediate problem in Djibouti City and The Government's strategy includes a combination\nsurrounding suburbs and other towns is the lack of of building more schools and continuing with the\nschool places due to the strong demand for schooling. double-shifting policy. The project will finance new\nclassrooms, sanitation services, and school furniture.\n\n**Equity, Gender, Disparities**\n\nChildren from poorer families, rural children, and The Government will construct schools in underespecially girls do not always attend school. The served areas, particularly in poorer parts of Djiboutirecent Household Expenditure Survey states that Ville where almost 70% of the population lives.\nmajor reasons for the", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:013669:9:0:0", "start": 1906, "end": 1934, "surface": "Household Expenditure Survey", "probe_tag": "confusion", "probe_score": 0.8798, "luna_label": 1, "luna_reason": "Existing household survey supports a concrete population-distribution finding."}]}, {"key": "aj-022", "text": " trunk water\nand sewerage networks, including in low income settlements; and the 2011 IDA-financed Kenya Informal Settlements\nImprovement Project (KISIP – P113542), which financed improved living conditions in informal settlements including\nthrough enhancing security of tenure and improving basic infrastructure. The project also supported the\nimplementation of NCWSC’s social connection policy, which was supported by the Water and Sanitation Program\nfinanced “Innovation in Scaling-up Access to WSS for the Urban Poor” (P132105) project.\n\n\n**Project Development Objectives (PDOs)**\n\n3. At approval the development objective of the project was to increase access to sanitation and water services in\nselected low-income communities in the Target Areas of the Recipient’s territory <sup>3</sup> .\n\n\n4. The rationale for the project was to test the application of output-based aid (OBA) to incentivize homeowners\nto invest in networked sewer connections, which traditionally lack financial sustainability. The Theory of Change (Figure\n1) also aimed to provide more affordable sewerage (and associated sanitation hardware) and water supply connections\nto low income households by blending subsides with commercial loan finance. This helped consumers to spread the cost\nof connection charges by spreading the household capital contribution over time. The project also enabled NCWSC\nenhance its long-term financial sustainability by supporting access to private finance.\n\n\n5. NCWSC accessed a commercial loan from the market on its balance sheet to pre-finance the cost of the\ninfrastructure and will recover the cost through a combination of the output subsidies paid by the project and a monthly\nsurcharge to consumers for capital cost recovery up to five years. The loan, together with the cost reduction from the\n\n\n1 Kenya Water Services Regulatory Board (WASREB)\n2 World Bank Data (Improved Sanitation Facilities), World Bank, Washington, DC, http://data.worldbank .org/indicator/SH.STA.ACS", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:014487:7:1:0", "start": 1866, "end": 1881, "surface": "World Bank Data", "probe_tag": "confusion", "probe_score": 0.8604, "luna_label": 1, "luna_reason": "Source-line citation identifies World Bank data for the sanitation indicator."}]}, {"key": "aj-023", "text": "**The World Bank**\nAdditional Financing Ethiopia Electrification Program (P178895)\n\n\naffordability constraints have not kept them from making new connections to date, they anticipate\naffordability may present a constraint in the medium-to-long term. Namely, as the grid reaches more\nremote areas, the cost of new connections is expected to rise (with lower population density in\nunelectrified areas) <sup>8</sup> [^8: EEU sets the customer connection fee based on an estimate which an EEU technician conducts on-site.] . Simultaneously, disposable income of potential customers in these areas will likely\ndecrease as the grid reaches increasingly remote areas. These two factors combined are expected to\npresent an affordability barrier and this requires further investigation to identify the capacity that\npotential customers will have to afford connections, as well as to identify ways to mitigate the potential\nimpact through potential cross-subsidization of the connection fee. Given that recent and reliable data on\naffordability of connection costs does not exist, both EEU and the World Bank have recognized the need\nto conduct a connection cost affordability study to be conducted in the first year of the proposed\noperation.\n\n24. **Despite the lack of utility data, a 2017 study on affordability** <sup>**9**</sup> [^9: Willingness to Pay Completion Report; USAID Ethiopia/ Beyond-the-grid (August 2017).] **can provide some approximate**\n**insight.** This survey of 150 unelectrified towns across 4 regions studied the willingness to pay (WTP) for\nelectricity services by households. The results of the survey indicate wide variability in WTP within towns\nas well as across town. Within towns, the figure below shows a steep decline in WTP after ~15 percent,\nfollowed by gradual decline thereafter. Similarly, the survey also found variability across towns. The study\ncites that a 30 percent benchmark is typically used in tariff setting for initial market penetration. At this\n30 percent mark, WTP varied across the towns from US$ 1", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:006025:11:0:0", "start": 1013, "end": 1054, "surface": "data on\naffordability of connection costs", "probe_tag": "confusion", "probe_score": 0.6204, "luna_label": 0, "luna_reason": "States data does not exist and motivates a future affordability study."}, {"key": "fcv_pads_east_africa:006025:11:0:1", "start": 1261, "end": 1273, "surface": "utility data", "probe_tag": "confusion", "probe_score": 0.7635, "luna_label": 1, "luna_reason": "Absence of utility data motivates use of an existing affordability study as substitute evidence."}]}, {"key": "aj-024", "text": "/sup> <sup>project</sup>\nvehicles procured, financial management <sup>measures</sup> <sup>in place</sup> <sup>and Single</sup> <sup>Fiduciary Management Units</sup> <sup>**(SFMU)**</sup>\n\nformed in phase 1 Counties.\n\n\n\nDigital Public Works (Pilot) **-** <sup>Digital</sup> <sup>Public Works (DPW) which</sup> <sup>is under</sup> <sup>piloting</sup> <sup>phase</sup> <sup>in Nairobi</sup> <sup>county</sup>\nsettlements of Embaksi KCC, <sup>Kahawa</sup> <sup>Soweto</sup> <sup>and</sup> <sup>Embakasi</sup> <sup>Village</sup> <sup>intends</sup> <sup>to:</sup> <sup>Create</sup> <sup>high-quality</sup> <sup>urban</sup>\n\ngeographic data that <sup>will</sup> <sup>**be**</sup> <sup>used</sup> <sup>to</sup> <sup>inform</sup> <sup>future</sup> <sup>urban</sup> <sup>planning</sup> <sup", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:007160:11:3:0", "start": 618, "end": 633, "surface": "geographic data", "probe_tag": "drop", "probe_score": 0.0369, "luna_label": 0, "luna_reason": "Future planned use of geographic data, not evidence of existing data analysis."}]}, {"key": "aj-025", "text": " strategy in<br>draft form|Policy ratified by Cabinet|Framework fully developed<br>(through consultative process)<br>and established|\n|Social Protection strategy and policy<br>framework established||Text|Date|31-Mar-2009|31-May-2012|31-Dec-2013|\n|Social Protection strategy and policy<br>framework established||Text|Comments||||\n\n\n\n**<u>Data on Financial Performance (as of 01-May-2012)</u>**\n\n\n**<u>Financial Agreement(s) Key Dates</u>**\n\n|Project|Ln/Cr/Tf|Status|Approval Date|Signing Date|Effectiveness Date|Original Closing Date|Revised Closing Date|\n|---|---|---|---|---|---|---|---|\n|P111545|IDA-45530|Effective|31-Mar-2009|08-May-2009|03-Jul-2009|31-Dec-2013|31-Dec-2013|\n|P111545|TF-97272|Effective|08-Jul-2010|20-Jul-2010|20-Jul-2010|31-Dec-2013|31-Dec-2013|\n\n\n\n**<u>Disbursements (in Millions)</u>**\n\n|Project|Ln/Cr/Tf|Status|Currency|Original|Revised|Cancelled|Disbursed|Undisbursed|% Disbursed|\n|---|---|---|---|---|---|---|---|---|---|\n|P111545|IDA-", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:009643:5:1:0", "start": 337, "end": 366, "surface": "Data on Financial Performance", "probe_tag": "drop", "probe_score": 0.0309, "luna_label": 0, "luna_reason": "Standalone financial-performance heading introducing project tables."}]}, {"key": "aj-026", "text": " capita -O 2 -0 5 Lower-middle-income _group_\nESports of goods and servic\"s\n\n\n**STRUCTURE ofthe ECONOMY**\n\n**1979** **1989** **1998** **1999** **Growth rates of output and Investment ()**\n_{%I ol GOP)_\nAgriculture 3.4 _.._ **_2._**\nIndustry 21.0 O.Manufacturing 5.6 **_-2_** **_94_** _as_ _se_ _s_\nServices **75.6** 6\n\nPrivate consumption **-r.**\nGeneral government consumplion **G**\nImports of goods and services\n\n\n\n**1979-89** **1989-99** **1998** **1999**\n_(average annual orowth)_\nAgriculture\n\n\n\nIndustry\n\n\n\nManufacturing\nServices\n\n\n\nPrivate consumption\nGenerai government consumption\nGross domestic investment\nImports of goods and services\nGross national product 1 7 **1.4**\n\n\nNote. 1999 data are preliminary estimates.\nThis table was produced from the Development Economics central database.\n\nThe diamonds show four kev midicators in the country (in bold) compared with its income-group average. 11 data are missing, Ihe diamond will", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:014952:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "drop", "probe_score": 0.0227, "luna_label": 0, "luna_reason": "Generic data note gives preliminary status, not an attributed finding or analysis."}, {"key": "fcv_pads_east_africa:014952:62:2:1", "start": 758, "end": 796, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.9824, "luna_label": 1, "luna_reason": "Named database cited as the source for the table's economic data."}]}, {"key": "aj-027", "text": " involve the use of manual labour using simple construction equipment/tools. The project will put in\nplace mitigation measures proportionate to the potential occupational health and safety risks. This will include\nidentification of potential hazards to the workers, provision of appropriate and adequate personal protective\nequipment, induction and training of workers on OHS related issues and maintaining site incident registers and other\nOHS records.\n\n\nThe labour-management procedures also specify the way in which community workers will raise grievances in\nrelation to the project. The risks associated with labour influx are not anticipated for this project as labour will be\ncommunity workers. A Labour Management Procedure has been prepared and disclosed at project appraisal.\n\n\n**ESS3 Resource Efficiency and Pollution Prevention and Management**\n\nThis standard is not relevant. The proposed project will use the 'Do Nou' technology that employs the use of local\nmaterials in repairing and maintaining roads. The repair and maintenance will explore measures to mitigate soil\nerosion activities, such as grassing and planting of trees along the selected roads. The Do Nou technology will not\nmake use of significant resources such as energy and water during implementation or generate any pollution\nmaterials.\n\n\n**ESS4 Community Health and Safety**\n\n\nMay 07, 2021 Page 9 of 12", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:019255:8:1:0", "start": 407, "end": 430, "surface": "site incident registers", "probe_tag": "drop", "probe_score": 0.029, "luna_label": 0, "luna_reason": "Routine project OHS compliance records, not substantive data reuse."}]}, {"key": "aj-028", "text": ".5<br>3.5<br>15.8|Insurance Premium<br>2.0<br>2.0<br>2.0<br>2.8<br>3.5<br>3.5<br>15.8|Insurance Premium<br>2.0<br>2.0<br>2.0<br>2.8<br>3.5<br>3.5<br>15.8|\n|Total Cost|8.2|8.2|8.2|10.8|13.8|13.8|63.0|\n\n\n\n42. **Supporting institutional and capacity strengthening of NDMA.** In addition, the proposed project\nwill invest in strengthening the NDMA’s capacity to store and analyze disaster data and support the\ndecision-making process to trigger a shock response with clear accountability. Resources would be\nprovided for strengthening the NDMA’s institutional capacity for overall project implementation, M&E,\nand fiduciary strengthening. The current Project Implementation and Learning Unit (PILU), which is fully\nfinanced and contracted by DFID, will be in place until the end of March 2019. This arrangement will\nhowever change under proposed KSEIP where NDMA will set up an HSNP unit and hire six key staff using\nthe proposed project resources in the areas of: FM, MIS, operations, communication, M&E and payments.\nThe position of the HSNP Coordinator will be performed by NDMA personnel at the level of\nManager/Deputy Director. The NDMA staff would be assigned to each of the specific functions to enable\ngradual transfer of skills and knowledge to NDMA officers for full takeover of HSNP implementation in the\nfuture.\n\n\n**Table 7 – IDA and DFID support to Component 3 (in million)**\n\n\n\n\n\n\n\n\n\n|DLIs IDA DFID<br>(", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:013099:25:4:0", "start": 376, "end": 389, "surface": "disaster data", "probe_tag": "drop", "probe_score": 0.0282, "luna_label": 0, "luna_reason": "Names data infrastructure capacity without showing analysis or use of existing data."}]}, {"key": "aj-029", "text": "**Objective (3): Establish effective models of disaster management in pastoral areas** **(Rating: Modest).**\n\n\nDisaster preparedness and contingency plans and emergency -warning systems were prepared for 23 woredas; 447\ndisaster-preparedness subprojects were financed . These interventions occurred late in the project and had less\nimpact than expected.\n\n\nDisruptions caused by unexpected institutional reorganization and the reassignment of responsibilities for disaster\nmanagement impeded the achievement of this objective . Drought emergency response was uneven, resource use\nwas often not efficient and results were often not sustainable . In the absence of a coherent, overarching institutional\nframework, the various regions adopted different procedures, some more successfully than others . The ICR notes\nthat the project \"did not establish an effective model of disaster management in pastoral areas due to the\nrestructuring and subsequent lack of uptake by the institution that was to carry out the component . The activities\nwere therefore badly sequenced with the planning mechanisms being completed only shortly before project closing \"\n(p. 17).\n\n\n**5. Efficiency (not applicable to DPLs):**\n\nThe project could have sponsored an ex -post estimate of a random stratified sample of subprojects . But this did not\nhappen. \"The project did not collect any comprehensive quantitative input /output/revenue data that would enable the\nICR to carry out the conventional analyses \" (p. 17). The ICR notes that construction costs of health posts and\nschools compared favorably with those of similar NGO -led initiatives; but no quantitative data are provided to support\nthis statement--and, anyway, health and education subprojects accounted for only 33 percent of all completed\nsubprojects (p. 28).\n\n\nActual project costs were as envisaged at appraisal but there was some shortfall in outputs : 25 percent of the\nsubprojects initiated were not completed (ICR, p.36); and 20 percent of the completed projects are not operational\n(ICR, p.", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:008072:3:0:0", "start": 1378, "end": 1417, "surface": "quantitative input /output/revenue data", "probe_tag": "drop", "probe_score": 0.0476, "luna_label": 0, "luna_reason": "Project explicitly did not collect these data."}, {"key": "fcv_pads_east_africa:008072:3:0:1", "start": 1630, "end": 1647, "surface": "quantitative data", "probe_tag": "confusion", "probe_score": 0.3913, "luna_label": 0, "luna_reason": "States data are absent, without substitute estimates or analysis."}]}, {"key": "aj-030", "text": " SDIP and SDMSME\nwere established in recent years, and there is a need to make more procurement documents available to sufficiently\nassess the procurement processes and contract management to identify risks to propose relative mitigation measures.\nHowever, the assessment recognized that the IAs’ procurement staff have experience in Government procurement\nprocedures.\n\n\n**62.** **The assessment of the Implementing Agencies (IAs) is highlighted in the procurement planning, procurement**\n**process and contract management, and procurement filing and records management.** The key risks identified, gaps to\nbe filled, and required mitigation measures are: (a) need for procurement manual with step-by-step on procurement\nprocesses based on the provision of the Public Procurement and Asset Disposal Act, 2015 (PPADA); (b) need for on-time\napproval of the PPs to prevent delays in the initiation of Procurement processes, which lead to poor budget absorption;\n(c) need to ensure compliance with the requirements of mandatory Reporting based on Executive Order No. 2 and Public\nProcurement Regulatory Authority (PPRA) circulars; (d) need for preparation of monthly contract status reports; (e) need\nfor market survey reports; (f) need for evaluation reports and professional opinion to be based on the Templates provided\n\n\nNov 16, 2023 Page 31 of 60", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:005133:35:2:0", "start": 1201, "end": 1222, "surface": "market survey reports", "probe_tag": "drop", "probe_score": 0.023, "luna_label": 0, "luna_reason": "Reports are identified as a procurement requirement, not existing data used."}]}, {"key": "aj-031", "text": "**INTEGRATED SAFEGUARDS DATASHEET**\n\n**APPRAISAL STAGE**\n\n**I. Basic Information**\nDate prepared/updated: 03/27/2012 Report No.: AC6622\n\n|1. Basic Project Data|Col2|\n|---|---|\n|Country: Kenya|Project ID: P107314|\n|Project Name: Nairobi Metropolitan Services Improvement Project|Project Name: Nairobi Metropolitan Services Improvement Project|\n|Task Team Leader: Andreas Rohde|Task Team Leader: Andreas Rohde|\n|Estimated Appraisal Date: February 9,<br>2012|Estimated Board Date: May 3, 2012|\n|Managing Unit: AFTUW|Lending Instrument: Specific Investment<br>Loan|\n|Sector: Solid waste management (25%);Sanitation (25%);Sub-national government<br>administration (25%);General transportation sector (25%)|Sector: Solid waste management (25%);Sanitation (25%);Sub-national government<br>administration (25%);General transportation sector (25%)|\n|Theme: Other urban development (67%);Pollution management and environmental<br>health (33%)|Theme: Other urban development (67%);Pollution management and environmental<br>health (33%)|\n|IBRD Amount (US$m.):<br>0 <br>IDA Amount (US$m.):<br>300<br>GEF Amount (US$m.):<br>0 <br>PCF Amount (US$m.):<br>0 <br>Other financing amounts by source:<br> <br>BORROWER/RECIPIENT<br>30.00<br> <br>", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:021140:0:0:0", "start": 2, "end": 33, "surface": "INTEGRATED SAFEGUARDS DATASHEET", "probe_tag": "drop", "probe_score": 0.0258, "luna_label": 0, "luna_reason": "Document title, not a cited or analyzed data resource."}]}, {"key": "aj-032", "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:019064:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 1, "luna_reason": "NaCSA monitoring data serves as verification evidence for beneficiary indicators."}, {"key": "fcv_pads_east_africa:019064:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "NaCSA administrative data is declared as a source for program performance information."}]}, {"key": "aj-033", "text": "br>Based Selection|Open - Internationa<br>l||1,800,000.00|0.00|Canceled|2021-06-11||2021-07-02||2021-08-15||||2021-09-12||2021-10-12||2021-11-16||2021-12-21||2023-12-11||\n|KE-MOTI-238027-CS-QCBS /<br>CONSULTANCY SERVICES FO<br>R MAPPING AND DEVELOPME<br>NT OF A NATIONAL GEO DAT<br>ABASE OF SLUMS AND INFOR<br>MAL SETTLEMENTS IN KENYA|IDA / 67590|Institutional capacity develo<br>pment for slum upgrading|Prior|Quality And Cost-<br>Based Selection|Open - Internationa<br>l||1,600,000.00|0.00|Under Review|2021-06-11|2022-07-29|2021-07-02||2021-08-15||||2021-09-12||2021-10-12||2021-11-16||2021-12-21||2022-04-20||\n|KE-MOTI-245281-CS-QCBS /<br>Consultancy Services for Dev<br>elopment of Settlement Lev<br>el Community Development<br>Plans, Sub-Action Plans on So<br>cio-Economic Inclusion, Inves<br>tment Selection, Crime & Viol<br>ence Prevention, Disaster Ma<br>nagement & Solid Waste Man<br>agement in Kilifi County|IDA / 67590|Socio-economic inclusion pla<br>nning", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:006853:31:8:0", "start": 262, "end": 278, "surface": "NATIONAL GEO DAT", "probe_tag": "drop", "probe_score": 0.0489, "luna_label": 0, "luna_reason": "Fragment of a procurement table entry, not a data-use mention."}]}, {"key": "aj-034", "text": "br>Change in % of farmers (M/F) expressing satisfaction with the<br>technologies introduced.<br>Type<br>Unit of Measure<br> Custom Indicator<br> Text|Value<br>76.5 %<br>Date<br>01-Aug-2008<br>Comment<br>baseline collected through<br>research system|Value<br>M=63 %;<br>F = 61 %<br>Date<br>01-Apr-2010<br>Comment<br>little success in terms of the<br>change farmers' level of<br>satisfaction with the quality of<br>technologies introduced to them,<br>suggesting that  the capacity<br>building efforts of the project have<br>not yet yielded results on the<br>ground|Value<br>90%<br>Date<br>31-Oct-2011<br>Comment|\n\n\n\nPage 6 of 9", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:008618:5:1:0", "start": 233, "end": 248, "surface": "research system", "probe_tag": "drop", "probe_score": 0.0186, "luna_label": 0, "luna_reason": "Names a research system without showing its data informing analysis or a decision."}]}, {"key": "aj-035", "text": "9-month<br>report||\n||COMMENTS||||||\n|2|**BENEFICIARIES OF SOCIAL SAFETY NET PROGRAMS (Number,**<br>**Corporate)**|**BENEFICIARIES OF SOCIAL SAFETY NET PROGRAMS (Number,**<br>**Corporate)**|||||\n||RAW DATA<br>SOURCES:||||||\n||COMMENTS||||||\n|3|**BENEFICIARIES OF SAFETY NET PROGRAMS (Other cash transfer**<br>**programs - number, breakdown) **|**BENEFICIARIES OF SAFETY NET PROGRAMS (Other cash transfer**<br>**programs - number, breakdown) **||||7,997,218|\n||RAW DATA<br>SOURCES:||||||\n||COMMENTS||||||\n|4|**BENEFICIARIES OF SAFETY NET PROGRAMS - FEMALE (Number)**|**BENEFICIARIES OF SAFETY NET PROGRAMS - FEMALE (Number)**||||4,113,060|\n||RAW DATA<br>SOURCES:|||||EFY 2010 AWP|\n||COMMENTS||||||", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:012431:49:1:1", "start": 197, "end": 205, "surface": "RAW DATA", "probe_tag": "drop", "probe_score": 0.0318, "luna_label": 0, "luna_reason": "Standalone table label, not an independently used data resource."}]}, {"key": "aj-036", "text": "Mexico (Santos-Burgoa, et al., 1998, 52 households), and Gambia (Campbell, 1997, 12\n\n\nhouseholds. Recently, a larger sample of houses has been studied in rural India\n\n\n(Balakrishnan, et al., 2002; Parikh, et al., 2001). In Section 9, we will compare the India\n\n\nresults to those obtained by this study.\n\n\nBecause monitoring studies are costly, IAP exposure analyses frequently use\n\n\nbiofuel consumption data to proxy the degree of exposure to fine particulates, and\n\n\nextrapolate to estimates of ARI prevalence and mortality (Smith, 2000). Although fuel\n\nuse data are widely available, this approach implicitly assumes a constant relationship\n\n\nbetween fuel combustion and indoor air pollution across households. However, the\n\n\npreviously-mentioned studies indicate that IAP levels in households with identical fuel\n\n\nuse are affected by factors such as the location of cooking (inside/outside), ventilation\n\n\nthrough windows and doors, and air flow through building materials. Additional\n\n\ninformation could have a large social payoff in this context, since simple alterations in\n\n\nstructures, ventilation practices, building materials and cooking locations may be much\n\n\nless costly than switching to cleaner fuels or investing in clean stoves.\n\n\nThis paper provides evidence on PM10 and PM2.5 concentrations in poor\n\n\nhouseholds, using new air monitoring data from Bangladesh. Recent technical advances\n\n\nhave significantly increased the power, portability and durability of equipment for\n\n\nmonitoring particulate pollution. Our study has used two types of equipment: air\n\n\nsamplers that measure 24-hour average PM10 concentrations, and real-time monitors that\n\n\nrecord PM10 and PM2.5 at 2-minute intervals for 24 hours. Each device has advantages\n\n\nthat we will describe in the paper. Together, their readings provide a detailed record of\n\n\n4", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002660:4:0:2", "start": 1343, "end": 1378, "surface": "air monitoring data from Bangladesh", "probe_tag": "keep", "probe_score": 0.9363, "luna_label": 1, "luna_reason": "Air monitoring data are used to provide evidence on particulate concentrations."}]}, {"key": "aj-037", "text": "**Abstract**\n\n\nStandards and technical regulations are an increasingly prominent part of\nthe international trade policy debate. In particular, there has been\nconsiderable discussion of whether standards and regulations affect trade\ncosts and export prospects for developing countries. In this paper, we\nexamine how meeting foreign standards affects firms’ export performance,\nreflected in export propensity and market diversification. The analysis\ndraws on the World Bank Technical Barriers to Trade Survey database of\n619 firms in 17 developing countries. Our results indicate that standards\nand technical regulations in developed countries do affect firms' propensity\nto export in developing countries. In particular, testing procedures and\nlengthy inspection procedures by importers reduce exports by 9% and 3%,\nrespectively. Furthermore, in our model, the difference in standards across\nforeign countries causes diseconomy of scale for firms and affects decisions\nabout whether to enter export markets. The empirical analysis presented\nhere implies that standards impede exporters' market entry, reducing the\nlikelihood of exporting to more than three markets by 7%. In addition, we\nfind that firms that outsource components are more challenged by\ncompliance with multiple standards than those that do not outsource..\n\n\n2", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:003023:1:0:0", "start": 461, "end": 515, "surface": "World Bank Technical Barriers to Trade Survey database", "probe_tag": "keep", "probe_score": 0.9485, "luna_label": 1, "luna_reason": "Database is used for analysis of firms’ export performance and standards effects."}]}, {"key": "aj-038", "text": "**III. An Empirical Specification of Tanzania’s Long-Run Equilibrium REER**\n\n\nThis section presents the results of an estimated model of Tanzania’s long-run\nequilibrium REER (EREER). It is the first step in a two-step procedure that aims to\ndistinguish between movements in the REER that are consistent with persistent\nmovements in economic variables that affect the REER and temporary movements in the\nREER that may be explained by short-lived events like monetary policy. This approach\nwill allow the estimation of the long-run equilibrium exchange rate, the degree of\nmisalignment of the actual REER relative to the estimated equilibrium value, and the\nestimated speed of the actual REER’s adjustment to equilibrium. An error-correction\nmodel is employed to achieve these results. We use a modified version of Engle and\nGranger’s (1987) two-stage error-correction procedure to estimate the long-run\nrelationship between the EREER and its fundamentals and the short-term dynamics\naffecting the REER.\n\n\nThe first stage of Engle and Granger (1987) involves estimating a cointegrating\nrelationship by OLS. We modify this stage by estimating the cointegrating relationship\nusing Phillips and Hansen’s (1990) Fully Modified (FM) method. Although the OLS\nestimators are consistent and highly efficient, they are biased in small samples making\ninference based on parameter estimates unreliable. The Phillips and Hansen (1990) FM\nestimator applies a semi-parametric modification to the standard OLS procedure that\ncorrects for autocorrelation of the OLS residuals as well as endogeneity of the regressors.\nThe result is parameter estimates that are asymptotically optimal, making inference more\nreliable. There are alternative approaches to achieving reliable parameters estimates, such\nas Johansen’s (1995) maximum likelihood estimator using a vector error-correction\nmechanism specification, but it requires more data observations than are available in the\npresent analysis to be effectively implemented. We present", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:003663:14:0:0", "start": 1909, "end": 1926, "surface": "data observations", "probe_tag": "confusion", "probe_score": 0.582, "luna_label": 0, "luna_reason": "States insufficient observations are available; no existing data analysis or substitute use."}]}, {"key": "aj-039", "text": " to revisions of the learning loss estimates and simulations:\n\n\n  - The pandemic has continued. In many countries, school closures have affected two or more\nschool years, and the surge of COVID-19 cases brought about by the Omicron variants indicate we\n[are not out of the woods yet (WHO 2021).](https://www.who.int/news/item/28-11-2021-update-on-omicron)\n\n  - There is actual data on school closures. At the time of the previous simulations, there was no\nglobal data on school closures, and so those simulations relied on uniform assumptions about school\nclosure lengths for all countries under different scenarios. We now have observed country-level data\non actual school closures for February 2020 – February 2022 from the <u>[UNESCO school closures](https://en.unesco.org/covid19/educationresponse#durationschoolclosures)</u>\n<u>tracker, which we use for to build the baseline scenario and the different simulation scenarios.</u> <sup>7</sup> [^7: Note that while the previous simulations used school closures as a percentage of one full school year, schools have been closed for\nalmost two full school years to date; we adjust expected learning gains as a product of annual learning gains and observed pandemic\nschool years.]\nTherefore, baseline school closure data now differ across countries and regions.\n\n  - More up-to-date learning assessment data are available. Internationally comparable data from\nPASEC 2019, TIMSS 2019, LLECE 2019, <sup>8</sup> [^8: Learning Poverty reports LLECE results on the SERCE scale.] and a new regional learning assessment program that\ntook place in East Asia, SEA-PLM 2019, have been released, as have the results from AMPL-b 2021\nassessment (in Zambia), <sup>9</sup> [^9: Note that the AMPL-b 2021 assessment was conducted during the pandemic.] and policy linking results of national assessment in Lesotho. This new data\nhas", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000989:5:1:3", "start": 1251, "end": 1270, "surface": "school closure data", "probe_tag": "confusion", "probe_score": 0.441, "luna_label": 1, "luna_reason": "Observed closure data inform baseline and simulation scenarios."}, {"key": "prwp:000989:5:1:6", "start": 1422, "end": 1432, "surface": "TIMSS 2019", "probe_tag": "confusion", "probe_score": 0.8973, "luna_label": 1, "luna_reason": "Named existing international learning assessment data are cited as newly available."}]}, {"key": "aj-040", "text": "Policy Research Working Paper\n10190\nThe Distributional Impact of Taxes  \nand Social Spending in Bhutan\nAn Application with Limited Income Data\nJuan Pablo Baquero\nJia Gao\nYeon Soo Kim \nPoverty and Equity Global Practice \nSeptember 2022 \nPublic Disclosure Authorized\nPublic Disclosure Authorized\nPublic Disclosure Authorized\nPublic Disclosure Authorized", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000977:0:0:0", "start": 131, "end": 142, "surface": "Income Data", "probe_tag": "confusion", "probe_score": 0.0682, "luna_label": 0, "luna_reason": "Title phrase names limited income data without showing an analyzed finding or actual use."}]}, {"key": "aj-041", "text": "/social-issues-migration-health/society-at-a-glance-2019_soc_glance-2019-en)</u>\n———. 2019b. _<u>Linking Indigenous Communities with Regional Development</u>_ . Paris: Organisation for\n\n\n\n[Economic Co-operation and Development. https://www.oecd-ilibrary.org/urban-rural-and-](https://www.oecd-ilibrary.org/urban-rural-and-regional-development/linking-indigenous-communities-with-regional-development_3203c082-en)\n<u>[regional-development/linking-indigenous-communities-with-regional-](https://www.oecd-ilibrary.org/urban-rural-and-regional-development/linking-indigenous-communities-with-regional-development_3203c082-en)</u>\n<u>[development_3203c082-en.](https://www.oecd-ilibrary.org/urban-rural-and-regional-development/linking-indigenous-communities-with-regional-development_3203c082-en)</u>\nOffice for National Statistics (ONS). 2012. Religion in England and Wales 2011.\n\n\n\nhttps://www.ons.gov.uk/peoplepopulationandcommunity/culturalidentity/religion/articles/reli\ngioninenglandandwales2011/2012-12-11, accessed July 13, 2021.\n———. 2013. 2011 Census: Key Statistics and Quick Statistics for local authorities in the United\n\n\n\nKingdom.\nhttps://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populatione\nstimates/datasets/2011censuskeystatisticsandquick", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000920:34:1:0", "start": 841, "end": 875, "surface": "Religion in England and Wales 2011", "probe_tag": "confusion", "probe_score": 0.3617, "luna_label": 0, "luna_reason": "Bibliography entry naming an ONS publication, without demonstrated data use."}]}, {"key": "aj-042", "text": "**<u>Table 1: Food items considered for simulating the potential impact of higher food prices on poverty</u>**\n<u>Country</u> <u>Household Survey</u> <u>Food Items Taken into account for simulations</u>\nBurkina Faso QUIBB, 2003 Rice, Bread, Vegetable oil and butter, Sugar, Milk\nDem. Rep. Congo 123 Survey, 2005 Rice, Cassava, Maize, Palm oil, Plantain, Wheat, Sugar, Milk\nGhana GLSS, 2005-06 Rice, Bread, Flour, Maize\nGabon CWIQ, 2005 Rice, Cassava, Maize, Wheat, Palm oil and groundnut oil\nGuinea EIBEP, 2002-03 Rice\nLiberia CWIQ, 2007 Rice (locally produced and imported)\nMali ELIM, 2006 Rice, Millet, Maize, Wheat\nNiger QUIBB, 2005 Rice (locally produced and imported), Millet, Sorghum\nNigeria NLSS, 2003-04 Rice, Corn, Maize, Wheat flour and bread, Cassava\nSenegal ESPS, 2006 Rice, Vegetable oil, Sugar, Bread, Milk\nSierra Leone SLLS, 2003 Rice\n<u>Togo</u> <u>QUIBB, 2006</u> <u>Rice, Vegetable oil, Sugar, Bread, Milk</u>\nSource: Authors’ estimation using respective household surveys.\n\n\n13", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:003949:14:0:1", "start": 279, "end": 305, "surface": "Dem. Rep. Congo 123 Survey", "probe_tag": "confusion", "probe_score": 0.8413, "luna_label": 0, "luna_reason": "Survey name appears as a standalone table cell, not an independently usable data mention."}]}, {"key": "aj-043", "text": "**Figure 6: Banks’ risks and sovereign stress in EMDEs**\nA. Sovereign EMBI spreads annual median 2017 (bps) vs. Probability of default of financial institutions (%)\n\n\n\n\n\n\n\nSources: University of Singapore – Credit Research Initiative; Bloomberg, Fitch Connect. Own calculations.\n\n\nB. Haircut in Government Securities to wipe out Common Equity Tier 1 capital (% holdings of Government\n\n\n\n\n\n\n\n\n\n\n\nData as of 2016, except for Georgia, Jordan, Eswatini (2015), and Cambodia, Kazakhstan, Kenya, Senegal, Tanzania (2014). Sample coverage:\n474 commercial banks in 33 EMDEs. Source: Fitch Connect. Own calculations.\n\n\n48", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002294:49:0:0", "start": 373, "end": 399, "surface": "Government\n\n\n\n\n\n\n\n\n\n\n\nData", "probe_tag": "confusion", "probe_score": 0.2067, "luna_label": 1, "luna_reason": "Government data underpin the calculated sovereign-stress haircut metric."}]}, {"key": "aj-044", "text": "empowerment through education and has been in place in the study area since 1995.\n\n\nThe program was rapidly scaled up between 2004 and 2008; data from the Indian\n\n\nCensus, District-level Household and Facility Survey, and National Family Health\n\n\nSurvey suggest that this scale-up was not systematically targeted at particular ar\n\neas. As a result, we only sampled treated villages that received the program in the\n\n\nexpansionary stage along with control villages that had not yet received the program.\n\n#### **2 Study Context**\n\n\nFollowing decades of local demand for a separate state, Uttarakhand was carved out\n\n\nof the state of Uttar Pradesh in November 2000. Small, scattered villages, comprising\n\n\nseveral clusters of houses isolated from others by the hilly terrain, pose challenges\n\n\nto the state’s development. Many lack access to basic infrastructure including roads\n\n\nand schools, severely limiting contact with others. Households generally engage in\n\n\nsubsistence-type agriculture, although the state also supplies migrant labor to Delhi\n\n\nand other urban centers. Caste hierarchy is strictly maintained in the villages, and\n\n\nmost interactions are limited to members of the same caste.\n\n\nAlcoholism and domestic violence are common problems in Uttarakhand. Al\n\nmost 40 percent of Uttarakhandi men consume alcohol, compared with the national\n\n\naverage of 32 percent, and 26 percent of all Uttarakhandi women have experienced\n\n\nphysical violence [IIPS and ORC Macro, 2007]. Only 18 percent of these women— 5\n\n\npercent of the overall population— have sought help to control or end the violence.\n\n\nUttarakhandi women tend to have few social interactions outside the immediate fam\n\nbelow), yielding a final sample of 404 women and 942 friends.\n\n\n7", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002291:7:0:1", "start": 172, "end": 216, "surface": "District-level Household and Facility Survey", "probe_tag": "confusion", "probe_score": 0.886, "luna_label": 1, "luna_reason": "Survey data support findings about program scale-up targeting."}]}, {"key": "aj-045", "text": " 2023). <sup>22</sup> There is also a large drop in air pollution in the regulatory data in 2020, which\n\n\nis not seen in 2023. The satellite estimates also show a drop in pollution in 2020, though they seem to\n\n\nshow higher values before March 14 in 2020 compared to 2023, and then pollution drops after March 14\n\n\nin 2020 but not in 2023.\n\n\nThere are two reasons that the impact may be more pronounced using the data from the Purple Air\n\n\nmonitors. First, for 2023, we are able to use data from 13 different monitors that had observations on at\n\n\nleast 80 of the 110 days in the analysis, which helps to smooth the otherwise high variation in pollution\n\n\nwhen measured from only a single monitor. Second, the Purple Air monitors only partially capture dust\n\n\npollution, which makes up a large portion of the pollution measured by the satellite and regulatory-grade\n\n\nmonitors. Because dust is driven by environmental factors rather than human-activities, the Purple Air\n\n\ndata may focus on the elements of PM2.5 that are likely to be influenced by mobility policies.\n\n\nNext, we formally analyze the impact of the policy on air pollution levels using the three data\n\n\n22Plots using daily aggregated data can be seen in Figure A4.\n\n\n23", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001445:24:1:0", "start": 73, "end": 88, "surface": "regulatory data", "probe_tag": "confusion", "probe_score": 0.8252, "luna_label": 1, "luna_reason": "Regulatory data are tied to the observed 2020 pollution drop."}]}, {"key": "aj-046", "text": "- First, four blocks were randomly selected from a total of 22 blocks in the district of\n\n\nJaunpur, namely, Maharajganj, Shahganj, Sikrara, and Ramnagar.\n\n\n  - Second, 280 villages were randomly selected (out of 313 villages in these four\n\n\nblocks).\n\n\nIn each of the 280 villages, 10 randomly selected households were surveyed about\n\n\nthe status of education services, perceptions of children’s learning achievement, and the\n\n\nrole for public action to improve outcomes, as were all government primary schools\n\n\nheadmasters, and all VEC members. Data on school resources and functioning were also\n\n\ncollected through direct observation of the interviewing teams. The final sample consists of\n\n\n2,800 household interviews, 316 school interviews and observations, and 1,029 VEC\n\nmember interviews from the 280 villages. <sup>5</sup> [^5: In obtaining averages for the survey as a whole, all responses and results are weighted by their relevant\npopulations.]\n\n\nData on actual learning achievement of children were collected through a testing\n\n\ntool developed by Pratham. All children between the ages of 7 and 14 were tested from 30\n\n\nrandomly selected households in each village (including the 10 households from which the\n\n\nother information mentioned above was collected). The final sample consists of 17,608\n\n\nchildren from these 280 villages.\n\n\n3. **Findings of the survey**\n\n\nThe survey has provided new data on actual outcomes of education service\n\n\ndelivery—the extent to which children are learning, in terms of the basic competencies of\n\n\nreading, writing, and simple arithmetic. In this section, we first describe what the basic\n\n\nlearning outcomes are, and then contrast this with the stated perceptions of learning by\n\n\nparents, teachers, and VEC members. Although there is no clear evidence of a knowledge\n\n\ngap about the state of actual learning in the village as a whole, illiterate children in large\n\n\npart are not identified by their parents as such. Furthermore, parents show through\n\n\nresponses to a range of questions that they have not paid much attention to the", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:003180:6:0:0", "start": 958, "end": 1005, "surface": "Data on actual learning achievement of children", "probe_tag": "drop", "probe_score": 0.0303, "luna_label": 0, "luna_reason": "The sentence states these learning data were collected through a testing tool."}]}, {"key": "aj-047", "text": "Iba˜nez, A. M., S. V. Rozo, and D. Bahar (2020). Empowering migrants: Impacts of a migrant’s\n\n\namnesty on crime reports. IZA Discussion Papers 13889, Institute of Labor Economics\n\n\n(IZA).\n\n\nKaushal, N. (2006). Amnesty programs and the labor market outcomes of undocumented\n\n\nworkers. _Journal of Human Resources 41_ (3), 631–647.\n\n\nKaushal, N., Y. Lu, N. Denier, J. S.-H. Wang, and S. J. Trejo (2016). Immigrant employment\n\n\nand earnings growth in canada and the usa: Evidence from longitudinal data. _Journal of_\n\n\n_Population Economics 29_ (4), 1249–1277.\n\n\nKling, J. R., J. B. Liebman, and L. F. Katz (2007). Experimental analysis of neighborhood\n\n\neffects. _Econometrica 75_, 83–119.\n\n\nLondo˜no-V´elez, J. and P. Querubin (2022). The impact of emergency cash assistance in a\n\n\npandemic: Experimental evidence from colombia. _Review of Economics and Statistics 104_ (1),\n\n\n157–165.\n\n\nMacPherson, C. and O. Sterck (2021). Empowering refugees through cash and agriculture:\n\n\nA regression discontinuity design. _Journal of Development Economics 149_, 102614.\n\n\nMastrobuoni, G. and P. Pinotti (2015). Legal status and the criminal activity of immigrants.\n\n\n_American Economic Journal:_ _Applied Economics 7_ (2), 175–206.\n\n\nMonras, J., J. V´azquez-Grenno, and F. Elias (2018). Understanding the Effects of Legalizing\n\n\nUndocumented Immigrants. CReAM Discussion Paper Series 1708, Centre for Research\n\n\nand Analysis of Migration (C", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001798:36:0:0", "start": 482, "end": 499, "surface": "longitudinal data", "probe_tag": "drop", "probe_score": 0.023, "luna_label": 0, "luna_reason": "Fragment from a bibliography entry, not an independently used data source."}]}, {"key": "aj-048", "text": " OECD for more than 40 years as the OECD group.\n\n\nThe rest of countries are classified by region and income. We use the average of GDP per capita (World Bank\n\n\n2017e) over 1985–2014 to break the sample into income quintiles. Table B.1 shows the country list by region and\n\n\nincome quintile groups, indicating their inclusion in the samples by type of analysis (descriptive and statistical),\n\n\ndata source (PWT, GTAP, and WDI), and other characteristics (oil rent and population).\n\n\n1 Heavy dependence is defined as reliance on oil production for more than 32 percent of GDP on average during 2006-15, which is 90th –\n100th percentile among 98 countries with positive oil rents (World Bank 2017j); Angola (45%), Congo, Rep. (46%), Equatorial Guinea\n(42%), Gabon (32%), Iraq (52%), Kuwait (47%), Libya (54%), Oman (37%), Saudi Arabia (44%), and South Sudan (45%).\n\n\n12", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001996:13:1:1", "start": 411, "end": 415, "surface": "GTAP", "probe_tag": "confusion", "probe_score": 0.1171, "luna_label": 1, "luna_reason": "Named dataset declared as a data source for sample analyses."}, {"key": "prwp:001996:13:1:2", "start": 421, "end": 424, "surface": "WDI", "probe_tag": "drop", "probe_score": 0.0201, "luna_label": 1, "luna_reason": "Named World Development Indicators source used for sample analyses."}]}, {"key": "aj-049", "text": "**I.6** **Quantitative results for Section 4.3.5**\n\n\n**Table I.6:** Counterfactual Experiment\n\nusing moments from simulated paths\n\n\nParameters Calibrated Values\n\nin simulated 1996\n\n\neconomy\n\n_𝐵_ ¯ 0.594\n_𝐷_ ¯ 1.080\n\n_𝜃_ 0.586\n\n_𝜖_ 0.014*\n\n\n1988–1996\n\n\n(1) (2) (3)\n\nMoments Steady state 1988 Simulated 1996 Counterfactual 1996\n\n\n(%)\n\n\nLocal debt 0.062 0.130 0.103 (59%)\n\nCentral debt 0.337 0.613 0.359 (8%)\n\nTotal government debt 0.399 0.743 0.462 ( **18%** )\n\n\nNote: The calibration finds the set of parameters ( _𝐵_ <sup>¯</sup>, _𝐷_ <sup>¯</sup>, _𝜃_, _𝜖_ ) such that the moments—total local government\n\ndebt, central government debt, central-local spending ratio, and central-to-local transfer—in the simulated\n\n1996 economy ( _off-steady state_ ) match those in the data for 1996. Total government debt (also known as “gen\neral government debt”) is the sum of the local and central government debt. Counterfactual experiments\n\nuse revenue ( _𝑒_ and _𝑓_ ) and spending responsibility share ( _𝜃_ ) values from 1996, and keep all other param\neters at the 1988 values. Numbers in **parentheses** show the percent of debt changes that is explained by\n\ndecentralization. All debt levels are normalized by national GDP. *We assume _𝜖_ for 1996 is the same as 1988.\n\n\n**Figure I.1:** Simulated path of total debt from 1998 steady state\n\n\nNote: The plot shows the path of total debt simulated from", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000012:88:0:0", "start": 770, "end": 783, "surface": "data for 1996", "probe_tag": "drop", "probe_score": 0.024, "luna_label": 0, "luna_reason": "Bare date-only data qualifier lacks an identified source or producer."}]}, {"key": "aj-050", "text": "Boyd, John H., Stanley L. Graham and R. Shawn Hewitt (1993), Bank Holding Company\n\n\nmergers with Nonbank Finanical Firms: Effects on the risk of failure, _Journal of Banking_\n\n\n_and Finance_ 17, 43-63.\n\n\nCaprio, Gerard, Daniela Klingebiel, Luc Laeven, and Guillermo Noguera (2005), Banking Crisis\n\n\nDatabase. In: Patrick Honohan and Luc Laeven (Eds.), _Systemic Financial Crises:_\n\n\n_Containment and Resolution_ . New York: Cambridge University Press, pp. 307-340.\n\n\nClaessens, Stijn and Leora Klapper (2005), Bankruptcy Around the World: Explanations of its\n\n\nRelative Use. _American Law and Economics Review_ 7, 253-283.\n\n\nClaessens, Stijn, Daniela Klingebiel and Luc Laeven (2003), Financial Restructuring in Banking\n\n\nand Corporate Sector Crises: What Policies to Pursue? In: Michael Dooley and Jeffrey\n\n\nFrankel (eds.), _Managing Currency Crises in Merging Markets_ . Chicago: University of\n\n\nChicago Press.\n\n\nClaessens, Stijn, Simeon Djankov, and Leora Klapper (2003), Resolution of Financial Distress:\n\n\nEvidence from East Asia’s Financial Crisis. _Journal of Empirical Finance_ 10(1-2): 199\n\n216.\n\n\nDe la Torre, Augusto (2000), Resolving Bank Failures in Argentina: Recent Developments and\n\n\nIssues, World Bank Policy Research Working Paper 2295.\n\n\nDemirguc-Kunt, Asli and Enrica Detragiache (2002), Does Deposit Insurance Increase Banking\n\n\nSystem Stability? An Empirical Investigation, _Journal of Monetary Economics_ 49, 1373\n\n1406.\n\n\nDemirguc-Kunt, Asli and Harry Huizinga (2004), Market Discipline and Deposit Insurance,\n\n\n_Journal of Monetary Economics_ 51, 375-399", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:003133:24:0:0", "start": 282, "end": 307, "surface": "Banking Crisis\n\n\nDatabase", "probe_tag": "drop", "probe_score": 0.0422, "luna_label": 0, "luna_reason": "Bibliographic database citation, not evidence of data being used."}]}, {"key": "aj-051", "text": "D Thesis - The University of North Carol|\n|**Data source**<br>YLS<br>NLSY<br>HILDA<br>ECLS-K<br>**Years**<br>2007 & 2009-2010<br>biannually from 1986-1998<br>2,007<br>fall 1998, spring 1999, fall 1999, spring<br>2000, spring 2002, spring 2004<br>**Sample size**<br>3,725<br>16,650 child-year observations from 6283<br>mothers; sample size for FE ranges from<br>4,159 for child FE to 7919 for sibling FE<br>907 (IV), 430 (mother FE)<br>18,990 person-years for 6,330 indivdiuals<br>**Location**<br>India (state of Andhra Pradesh)<br>US<br>Australia<br>US<br>**Sample restrictions/**<br>**Mother characteristics**<br>Sample restricted to households  in rural<br>areas in both periods<br>**Sample**|**Data source**<br>YLS<br>NLSY<br>HILDA<br>ECLS-K<br>**Years**<br>2007 & 2009-2010<br>biannually from 1986-1998<br>2,007<br>fall 1998, spring 1999, fall 1999, spring<br>2000, spring 2002, spring 2004<br>**Sample size**<br>3,725<br>16,650 child-year observations from 6283<br>mothers; sample size for FE ranges from<br>4,159 for", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001080:25:1:0", "start": 721, "end": 725, "surface": "NLSY", "probe_tag": "drop", "probe_score": 0.0219, "luna_label": 1, "luna_reason": "Named survey identified as the study’s data source."}, {"key": "prwp:001080:25:1:1", "start": 729, "end": 734, "surface": "HILDA", "probe_tag": "drop", "probe_score": 0.0219, "luna_label": 1, "luna_reason": "Named survey listed as the study's data source."}]}, {"key": "aj-052", "text": "SRTM Digital Elevation Data Version 4; 90m <sup>2</sup> resolution (Jarvis et al., 2008)\n\n\nBuilt-up Land Cover\n\nGHSL: Global Human Settlement Layers, Built-Up Grid 1975-1990-2000-2015 (P2016);\n\n38m <sup>2</sup> resolution (Pesaresi et al., 2015)\n\n\nNormalized Difference Vegetation Index/NDVI\n\nLandsat 8 Collection 1 Tier 1 32-Day NDVI Composite; 30m <sup>2</sup> resolution\n\n\nNormalized Difference Water Index/NDWI (U.S. Geological Survey)\n\nLandsat 8 Collection 1 Tier 1 Annual NDWI Composite; 30m <sup>2</sup> resolution (U.S. Geolog\nical Survey)\n\n\nLand Use\n\nCopernicus Global Land Cover Layers: CGLS-LC100 Collection 3; 100m <sup>2</sup> resolution\n\n(Buchhorn et al., 2020)\n\n#### Appendix B Additional Tests\n\n\nThis section provides additional computations to investigate possible effects of data\n\ncollection and of choice of different spatial units.\n\n\nB.1 Heterogeneity in Employment Data Collection\n\n\nThe employment data used throughout this work is collected either via travel surveys\n\nor via population and firm censuses <sup>16</sup> . This introduces noise to the data both with respect\n\nto heterogeneity of polygon sizes within which the data was originally collected but also in\n\nterms of what specific employment is measured. Travel surveys overall typically include any\n\ntype of employment, whereas firm censuses are generally limited to collecting information\n\non formal employment and do not capture employment within the informal sector. As the\n\ninformal sector represents a substantial part of jobs within developing countries (Bryan et al.,\n\n2020), omitting this might introduce a noticeable measurement issue within the training of the\n\nalgorithms. In order to test this, we split the", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002432:40:0:1", "start": 376, "end": 414, "surface": "Normalized Difference Water Index/NDWI", "probe_tag": "confusion", "probe_score": 0.6647, "luna_label": 1, "luna_reason": "Named NDWI dataset is identified as a geospatial input source."}, {"key": "prwp:002432:40:0:5", "start": 875, "end": 890, "surface": "Employment Data", "probe_tag": "drop", "probe_score": 0.0465, "luna_label": 1, "luna_reason": "Existing employment data are analyzed for collection heterogeneity and measurement effects."}]}, {"key": "aj-053", "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, "luna_reason": "Data are described as collected for a prospective representative-sample analysis."}, {"key": "prwp:000135:20:0:2", "start": 897, "end": 911, "surface": "narrative data", "probe_tag": "drop", "probe_score": 0.0381, "luna_label": 0, "luna_reason": "Generic data concept with no attributed finding or concrete existing dataset use."}]}, {"key": "aj-054", "text": " for\nsome cautious inference with 4.2 million\nPolish citizens in mid-2022 and 6.2 million\nin mid-2024. This has been combined with\naverage salaries in 128 occupational groups\nin GUS data for October 2022 (the most\nrecent data). Over the two-year period from\nQ2 2022 to Q2 2024, following the arrival\nof Ukrainian refugees, the occupational\ngroup distribution of Polish citizens shifted\n\n\n\n**Chart 30. Polish citizens occupational distribution change by salary bracket, Q2 2022 and Q2 2024**\nQ2 2022 distribution, Q2 2024 distribution, and percentage point changes between them\n\n\n-1.8 pp.\n\n\n\n\n\n\n\n\n\n<= 4,000 (4,000; 6,000] (6,000; 8,000] (8,000; 10,000] - 10,000\n\n\n\nQ2 2022 Q2 2024\n\n\n41\n\n\n\n30 Unfortunately, GUS data for these time periods and poviat level include the enterprise sector (firms that employ 10 or more persons) and public sector, which is\nmost of the labour market, but not the total economy.\n\n\n40\n\n\n\nSource: Deloitte own elaboration based on ZUS data and GUS data on salaries of 128 3-digit occupations in\nOctober 2022. Note that ZUS data is not comprehensive.", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "jad_paddy_docs:000001:20:4:0", "start": 956, "end": 964, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9232, "luna_label": 1, "luna_reason": "ZUS data underlies the charted occupational distribution analysis."}]}, {"key": "aj-055", "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, "luna_reason": "Survey data underlies Deloitte calculations and the reported refugee income finding."}]}, {"key": "aj-056", "text": "\nremain in the informal sector. The higher\nbound is the product of employment rates\nfrom surveys of refugees from Ukraine,\nand their working age population from\nthe active PESEL UKR database. By JulyAugust 2023 Ukrainian refugee households\nsupported themselves, with 80% of their\nincomes coming from work. <sup>49</sup>\n\n##### We find that refugees from Ukraine as workers, entrepreneurs, consumers, and taxpayers had a positive impact on economic output, which will increase in the long run.\n\n\n\n49  Deloitte calculations based on Multi-Sector Needs Assessment Poland 2023 survey data provided by UNHCR.\n50  E.g. vice-president of Polish Development Found Bartosz Marczuk estimated it at around 16 billion PLN, but this estimation also included spending of\nNGOs which was combined with spendings of local governments <u>[Polska pomoc dla Ukrainy 2022 - ile kosztowała? - Infor.pl.](https://www.infor.pl/prawo/nowosci-prawne/5635962,Polska-pomoc-dla-Ukrainy-2022-ile-kosztowala.html)</u>", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:20:4:0", "start": 89, "end": 121, "surface": "surveys of refugees from Ukraine", "probe_tag": "keep", "probe_score": 0.9909, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:20:4:1", "start": 172, "end": 190, "surface": "PESEL UKR database", "probe_tag": "keep", "probe_score": 0.9899, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:20:4:2", "start": 531, "end": 584, "surface": "Multi-Sector Needs Assessment Poland 2023 survey data", "probe_tag": "keep", "probe_score": 0.9901, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-057", "text": "\nbetween 2008 and 2017. He exploited the\nrandom assignment of refugees to French,\nGerman, and Italian cantons, as well as\ntheir prior language knowledge (e.g. French\nspeakers placed in French or German\nspeaking cantons). The results showed that\nlanguage proficiency more than doubled\nthe employment level in the first five years\nafter arrival.\n\n\n**The econometric model estimated**\n**in the SEIS UNHCR survey shows**\n**substantial wage gains from Polish**\n**language proficiency for Ukrainian**\n**refugees.** Deloitte has estimated an\neconometric model incorporating\nindividual income determinants of\nUkrainian refugees. Our study has\n\n\n\nto work, but only in a designated health\ncare center, and for no longer than five\nyears without the possibility of extension.\nIn February 2022, Ukrainian psychologists\nwere allowed to provide services to other\nUkrainian citizens, but only for 18 months.\nWhile such changes are likely to be\nextended, they do not motivate Ukrainian\nrefugees – unsure, if they will be allowed\nto continue practice in the long term – to\nacquire the necessary skills, for instance\nto improve language fluency or invest in\nprofessional courses. Instead, the refugees\nmay prefer to seek opportunities in other\ncountries or change their line of work to\none that will be more accessible, but less\nvaluable for the Polish economy.\n\n\nconfirmed the results found in international\nliterature presented above. The most\nimportant result is that Ukrainian refugees\nwho are fluent in Polish earn a net wage\npremium of about PLN 700 (+16% relative\nto refugee net median wage,\nPLN 1,000 gross wage) when compared to\nthose with beginner language skills. This\nresult is stable across different model\nspecifications. Note that such an earnings\ngain would bring the median net wage of\na Ukrainian refugee (estimated based on\nthe SEIS UNHCR survey in chapter 2) from\n80% to 98% of the median in the economy\nas a whole (or from 80% to 93% according\nto Ukrainian refugee’s median in", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "jad_paddy_docs:000001:15:2:0", "start": 391, "end": 408, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9715, "luna_label": 1, "luna_reason": "Survey data supports econometric estimates of refugee wage gains."}]}, {"key": "aj-058", "text": " not\nanticipate a significant negative impact\non wages due to increased competition.\nEnhancing language fluency is one way\nto address occupational downgrading. To\navoid double counting, we estimated that\naround PLN 1 billion has been already\nincluded in the previously estimated gains\nfrom reduced downgrading.\n\n\n\n**A further increase in labour**\n**participation of refugees would**\n**yield significant macroeconomic**\n**benefits.** Although the employment rate\nof Ukrainian refugees in Poland is already\nhigh when compared to other countries,\nthere is still room for improvement.\nIncreasing the employment of refugees by\n15 thousand people, which would close\nhalf of the gap between the employment\nrate of refugees and Poles, would yield at\nleast PLN 1 billion of value added in the\neconomy. This calculation is made under\nan assumption that those newly hired\nwould be paid minimum wage and should\nbe considered a lower-bound estimate, as\nit understates potential benefits. In part,\nthe higher productivity of workers might\nboost the profits for employers, further\nenhanced by increased investment.\n\n\n#### **4.4 Persons not previously in employment**\n\n\n\n**Employment rates among Ukrainian**\n**refugees are exceptionally high, with**\n**only a minority requiring targeted**\n**assistance to enter the labour market,**\n**in particular those who were not**\n**employed before their displacement.**\nIn the SEIS survey, employment rates\namong refugees aged 18-64 previously\nemployed or self-employed in Ukraine, are\n81% and 91% respectively. That is already\nvery high and any further increase would\nbe marginal. On the other hand, those who\nback in Ukraine managed the household\nhave an employment rate in Poland of\n38%. Even before becoming refugees, they\nwould have required support to enter the\nworkforce and now, in the host country,\nthey would be likely to benefit from such\nhelp even more. Some of them may be\ndiscouraged by not having been able", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:17:2:0", "start": 1400, "end": 1411, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.9295, "luna_label": 1, "luna_reason": "Named survey provides employment-rate findings for Ukrainian refugees."}]}, {"key": "aj-059", "text": ". <sup>11</sup> [^11: According to the social security data until 30th September 2023.]\nWhile public data does not distinguish\nbetween refugees entering these sectors\nand pre-2022 Ukrainian workers changing\njobs, it is largely consistent with the MSNA\nPoland 2023 survey, in which the most\nrefugees are employed in manufacturing\n(14%), accommodation and food service\n(12%), and trade and repair (6%).\n\n\nUkrainian refugee households in Poland\n\nto the MSNA Poland 2023 survey, 20% of\nUkrainian refugee households earn less\nthan 3 000 PLN, 41% earn between 3 000\nand 6 000 PLN, and 12% earn more than\n6 000 PLN, while 27% of respondents\npreferred not to answer. That said, the\nstandard of living of Ukrainian refugees\nmay be significantly lower than that of\nnative residents, even at similar incomes,\ndue to their lack of housing, which in\nPoland is usually occupant-owned.\n\n\n\n**Inflow of Ukrainian refugees into**\n**Poland**\nThe beginning of the full-scale war in\nUkraine in February 2022 resulted in large\nflows of refugees, reaching more than\n6 million globally. <sup>3</sup> [^3: As of December 2023, according to UNHCR, based on governmental sources <u>[Situation Ukraine Refugee Situation (unhcr.org)](https://data.unhcr.org/en/situations/ukraine)</u>] Much of this exodus\nhappened through the Polish border.\nAs of October 2023, almost 1 million\nUkrainian refugees were living in Poland <sup>4</sup> [^4: According to the active PESEL UKR database.]\n(Chapter 1). In the past decade, Poland\nexperienced large labour migration from\nUkraine. The number of workers with\nUkrainian citizenship that registered for\nsocial security (this data does not include\nthose working in the shadow economy\nor some minor cases that do not require\nregistration) grew from just 33 thousand interm", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:3:1:0", "start": 39, "end": 59, "surface": "social security data", "probe_tag": "keep", "probe_score": 0.9995, "luna_label": 1, "luna_reason": "Existing social security data are cited as the source for labor-market claims."}, {"key": "sample:jad_paddy_docs:000007:3:1:1", "start": 450, "end": 473, "surface": "MSNA Poland 2023 survey", "probe_tag": "keep", "probe_score": 0.9955, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:3:1:2", "start": 1432, "end": 1450, "surface": "PESEL UKR database", "probe_tag": "keep", "probe_score": 0.9866, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-060", "text": " it is\nprovided, allows over a third of low-income <sup>10</sup> [^10: Those that are below the poverty line]\nhouseholds to de facto live above the poverty line.\nIn Slovakia this effect is the most dramatic –\nsubsidized housing allows an additional 46% of the\nrefugee population to escape poverty.\n\n\n**<u>DECREASE IN THE REFUGEE POVERTY RATE AS A RESULT</u>**\n**OF ACCOMMODATION RENT SUPPORT, %** <sup>**1,2,3**</sup> [^2: Results for the Czech Republic not individually presented due to\nsampling limitations] [^3: Accommodation rent support has been calculated as the difference\nbetween the actual equivalized accommodation expense and the\nmedian equivalized market rent in the region]\n\n\n\nNot adoping\n\ncoping\nstrategies\n\n\n\nStress coping\n\nstrategies\n\n\n\nCrisis coping\n\nstrategies\n\n\n\nEmergency\n\ncoping\nstrategies\n\n\n\nWithout accommodation rent\nsupport\n\n\n\n1. The statistic on emergency strategies may have been affected by\nthe survey wording on illegal work\n\n\nThe Ukrainian refugee population also faces\nfinancial barriers to access critical services: one in\nten households have no health insurance and 22 %\nof those surveyed answered that they cannot afford\nfees at local health care clinics. Households that\ncontain a member with a disability are also more\nlikely to be below the poverty line. This group’s\npoverty rate stands at 59% versus 43% for\nhouseholds with no members with disabilities.\n\n\n**Support with accommodation expenses – an**\n**important vulnerability shield**\nOverall, almost half (48%) of refugee households in\nthe region report receiving accommodation or\nhousing assistance. Twenty percent are living in\n\n\n\n\n\n\n\n\n\n\n\n\n\nWith accommodation rent support\n\n\n\n\n\n\n\nBulgaria Hungary Poland Romania Slovakia Region\n\n\n1. Calculations for Moldova were not conducted, as this country is not\nincluded into the <u>[EU statistics on income and living conditions (SILC)](https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions)</u>\n<u>[survey,", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000000:4:1:0", "start": 1816, "end": 1861, "surface": "EU statistics on income and living conditions", "probe_tag": "keep", "probe_score": 0.9743, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-061", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nThe influx of refugees into Poland after the\nRussian invasion of Ukraine was large, with\nnearly 16 million border crossings <sup>18</sup> [^18: UNHCR data, <u>[https://data2.unhcr.org/en/situations/ukraine](https://data2.unhcr.org/en/situations/ukraine)</u>] from\nUkraine until the end of September 2023 and\ncumulatively 1.7 million PESEL registrations.\nIt must be noted that such a rapid population\nmovement of that scale was not seen in\nEurope since World War II. Not everyone\n\n\n\nstayed in Poland, however. A large number of\nthese refugees later returned to Ukraine or\nmoved to other European countries.\nBy October 2023, the remaining active PESEL\nUKR numbers stood at less than 1 million,\nwhile the border movement balance between\nPoland and Ukraine at 2.5 million.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 4.** Age and gender structure of refugees based with active PESEL numbers in\nOctober 2023\n\n\n|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|The s<br>imple<br>of for|udden<br>menta<br>eigner|drop in<br>tion of t<br>status a|registe<br>he 30-d<br>fter a r|red num<br>ay dead<br>egistere|bers is<br>line for<br>d depa|due to<br>revoca<br>rture fro|the<br>tion<br>m|Col18|\n|---|---|---|---|---|---|---|---|---|---|---|", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:7:0:0", "start": 220, "end": 230, "surface": "UNHCR data", "probe_tag": "keep", "probe_score": 0.9964, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-062", "text": "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n||||||||||the te|rritory|of Polan|d.||||||\n|||||||||||||||||||\n|||||||||||||||||||\n|||||||||||||||||||\n|Lau|nch of ass|igning|PESEL n|umber|s to the|refugee|s|||||||||||\n|||||||||||||||||||\n\n\n\n40%\n\n\n30%\n\n\n20%\n\n\n10%\n\n\n0%\n\n\n\nmen\n\n\n\nwomen\n\n\n\n**Chart 3.** Poland-Ukraine border movement balance and registered/active PESEL data\n\n\n\n3000\n\n\n2500\n\n\n2000\n\n\n1500\n\n\n1000\n\n\n500\n\n\n0\n\n\n\n\n\n65+\n\n\n55-64\n\n\n45-54\n\n\n35-44\n\n\n25-34\n\n\n18-24\n\n\n<18\n\n\n25% 20% 15% 10% 5% 0% 5% 10% 15% 20% 25%\n\n\n**Source:** Deloitte own elaboration based on the PESEL database as of October 2023\n\n\n**Chart 5.** Composition of refugee households in Poland\n\n\n60%\n\n\n50%\n\n\n\n\n\nTotal entries-exits of the Polish-Ukrainian border Pesel data\n\n\n**Source:** Deloitte own elaboration based on Polish Border Guard Headquarter and PESEL data.\n\n\n\n\n\n\n\nMost of the refugees from Ukraine\ncurrently living in Poland are women and\nchildren, though over half of the total\npopulation is of working age.\nThe best population data available is the\nactive PESEL", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:7:1:0", "start": 371, "end": 381, "surface": "PESEL data", "probe_tag": "keep", "probe_score": 0.9994, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:7:1:1", "start": 578, "end": 592, "surface": "PESEL database", "probe_tag": "keep", "probe_score": 0.9995, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:7:1:2", "start": 739, "end": 749, "surface": "Pesel data", "probe_tag": "keep", "probe_score": 0.9773, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-063", "text": " potential GDP should\nbe higher by around 0.9-1.35% due to\nrefugees contributions. <sup>44</sup> [^44: Note that long term refers to the time when the economy fully adjusts with no additional shocks. We do not model the current refugees from Ukraine\nchildren growing up and entering the labour market.]\n\n\nOur results are consistent with the\nprevious, similar studies. In estimating\nGDP impacts we take an approach that\nis most similar to the previous studies of\nthe pre-2022 Ukrainian migrants by NBP\neconomists (Gradzewicz, Jabłonowski,\nSasiela, and Żółkiewski, 2021; Strzelecki,\nGrowiec, and Wyszyński, 2022), but unlike\nthe previous Oxford Economics and ours\nimpact estimates of Ukrainian refugees\n(Urban, 2022; Deloitte, 2022) we do not\nallow for the possibility of a positive\nproductivity shock, because there is little\ndata to credibly estimate its size. Below,\nwe summarise impacts yielded by these\nstudies. As studies were done under\ndifferent assumptions on the number of\n\n\n\n\n\n\n\n\n\n41  Aggregate region in model consisting of Ukraine, Russia, Belarus, Moldova, Czechia, Slovakia, Hungary, Romania and Bulgaria.\n42  In other words it was assumed that money that would be spent e.g. through credit action for investments in Eastern Europe were spent for\nconsumption in Poland.\n\n\n34\n\n\n\n43  Data from forecast of Ministry of Finance from October 2023, <u>[Wytyczne dotyczące wskaźników makroekonomicznych - Ministerstwo Finansów - Portal](https://www.gov.pl/web/finanse/wytyczne-sytuacja-makroekonomiczna)</u>\n<u>[Gov.pl (www.gov.pl).](https://www.gov.pl/web/finanse/wytyczne-sytuacja-makroekonomiczna)</u>\n44  Note that long term refers to the time when the economy fully adjusts with no additional shocks", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:17:3:0", "start": 1295, "end": 1336, "surface": "Data from forecast of Ministry of Finance", "probe_tag": "keep", "probe_score": 0.973, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-064", "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, "luna_reason": null}]}, {"key": "aj-065", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n#### **2.2 Current occupational situation**\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nAdministrative ZUS data can be used as a\nproxy for both average and _gross_ earnings\npercentages. On June 30, 2024, average\nbases for social contributions of Ukrainian\nrefugees accounted for just 64% of those\nof Polish citizens. However, this is only\nrelative to Polish citizens not all workers in\nthe economy as a whole, and comes with\nother caveats of administrative instead\nof survey data sources – data given on a\nparticular day instead of period average,\nthe shadow economy unaccounted for,\ndifferent contributions based on the type\nof contract, no data for farmers who belong\nto a separate social insurance scheme.\nAlso, the ZUS data, that is available, is much\nmore limited that the SEIS survey data\nprimarily used in this report.\n\n\n\n**Ukrainian refugees in Poland have**\n**clearly improved their economic**\n**situation over the past year.** In\nthe 15-59/64 age group, employment\nrate of Polish citizens stood at 75% in\nQ2 2024 according to Eurostat, slightly\nmore than the 69% for Ukrainian refugees\nin the SEIS 2024 survey and 73% when\nadjusted for a different sex and age\nstructure. In the 15-64 age group, the\nemployment rate of Ukrainian male\nrefugees was 67% while that of Polish\ncitizens in Q2 2024 was 77%. For women,\nthe rates for Ukrainian refugees and Polish\ncitizens are closer in the 15-59 age group\n(female retirement age in Poland is 60) with\n70% for refugees and 72% for Poles. Visible\n\n\n\ndifferences are identified in 15-19 and\n20-24 age groups, with much higher\nemployment rates for Ukrainian refugees\ndue perhaps to the fact that the Ukrainian\nschool system ends at 17 while the Polish\none at 19. <sup>13", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:9:0:0", "start": 197, "end": 220, "surface": "Administrative ZUS data", "probe_tag": "keep", "probe_score": 0.96, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:9:0:1", "start": 828, "end": 836, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9235, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:9:0:2", "start": 887, "end": 903, "surface": "SEIS survey data", "probe_tag": "keep", "probe_score": 0.9811, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:9:0:3", "start": 1212, "end": 1228, "surface": "SEIS 2024 survey", "probe_tag": "keep", "probe_score": 0.9774, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-066", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n© UNHCR / Anna Liminowicz\n# **3.** Economic impact modelling\n\n\n\nSimple back-of-the-envelope calculations\ngive the intuition behind how additional\nworkers help to grow the economy.\nThe simplest estimate would be to assume\nthat an increase of employment by 1.4-2.2%\nwill grow the Gross Domestic Product by\nan equal percentage. In such a case, we\nwould need to assume that labour is the\nonly production factor, and thus all of GDP\ncan be equally divided between workers.\nHowever, this is not the case, as GDP is not\njust a function of labour, but also of capital\nthat workers have at their disposal – all the\nmachines, computer programs, offices, and\nthe like. As displacement is unexpected and\nrefugee status (both legal and intent to stay\nlong term) at first is uncertain, companies\ntake time to increase their stocks of capital\nto the new workers. A more elaborate\nestimate would account for the part of GDP\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\nthat is produced by labour alone. This can\nbe estimated by assuming that it is equal\nto the labour compensation share of GDP\n(GDP can be divided into compensation of\nlabour and capital), which in 2022 and 2023\nstood in Poland at 48% according to the\nEuropean Commission’s AMECO database.\nAccounting for that, gives a lower estimate\nof 0.7-1.0% GDP. Such calculations are\nvery abstract, and do not account for other\nphenomena developing simultaneously\nin the economy, like the various effects of\nthe war and energy shock. For this reason,\nwe turn next to formal general equilibrium\nmodelling, where equations of the model\nstate explicitly every assumption about\nthe workings of the economy and allow for\na credible estimation of counter-factual\nscenarios.\n\n\n\nRefugees’ impact on the economy manifests\nin a multi-layered fashion. An influx of\nrefugees means", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:15:0:0", "start": 1333, "end": 1347, "surface": "AMECO database", "probe_tag": "keep", "probe_score": 0.9688, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-067", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**<u>SHARE OF POPULATION PAYING FOR RENTED HOUSING: HOSTS VS REFUGEES</u>**\n\n\nShare of hosts living in rented housing (2023) Share of refugees fully paying for rent (2024) Share of refugees partially paying for rent (2024)\n\n\n100%\n\n\n80%\n\n\n60%\n\n\n40%\n\n\n20%\n\n\n0%\nBulgaria Czechia Estonia Hungary Latvia Lithuania Moldova Poland Romania Slovakia Region\n\n\nSource: Eurostat, survey data, SAG estimates\n\n\n**<u>HOUSING COST AS A SHARE OF HOUSEHOLD DISPOSABLE INCOME</u>**\n\n\nRefugees (2024) Hosts (2022)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Estonia Hungary Latvia Lithuania Moldova Poland Romania Slovakia Region\n\n\nSource: Eurostat, survey data, SAG estimates\n\n\n**<u>REFUGEE POVERTY RATES WITH AND WITHOUT CORRECTION FOR EXCESSIVE HOUSING COSTS</u>**\n\n\nRefugees (2024) Refugees with housing expense correction (2024) Hosts (2023)\n\n\n\n65%\n\n\n\n40%\n\n\n\n52%\n\n\n\n\n\n\n\n\n\n\n\n31%\n\n\n\n\n\n46%\n43%\n37%\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Hungary Moldova Poland Romania Slovakia Estonia Latvia Lithuania Region\n\n\nSource: Eurostat, survey data, SAG estimates\n\n\n**6**", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000010:5:0:0", "start": 476, "end": 487, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9968, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-068", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n\n**<u>ACCOMMODATION QUALITY BY POVERTY GROUP</u>**\n\n\nIncome above the poverty line Income below the poverty line\n\n\n\n**<u>LIVING CONDITIONS BY POVERTY GROUP</u>**\n\n\nIncome above the poverty line Income below the poverty line\n\n\n\nwalking in\nneighbourhood\n\n\n\nNot reporting\n\nfeeling safe\n\n\n\nUnable to\nstore or cook\n\nfood\n\n\n\nInsufficient\n\nprivacy\n\n\n\nLacking\nseparate\nshowers or\n\ntoilets\n\n\n\nleave\naccommodation\n\n\n\nFeeling under\n\n\n\npressure to\n\n\n\nLiving in\ncollective\n\nhousing\n\n\n\nafter dark\n\n\n\nSource: Survey data, SAG estimates\n\n\n**<u>POVERTY EFFECTS ON HEALTHCARE ACCESS</u>**\n\n\nIncome above the poverty line Income below the poverty line\n\n\n\nSource: Survey data, SAG estimates\n\n\n**<u>FOOD COPING STRATEGY OVER LAST 7 DAYS BY POVERTY</u>**\n**GROUP**\n\n\nIncome above the poverty line Income below the poverty line\n\n\n\n\n\n\n\nHad to reduce\n\nessential\n\nhealth\nexpenditures\nin last 30 days\n\n\n\nUnable to\nobtain needed\n\nhealthcare in\n\n\n\ndays\n\n\n\nCould not\nafford hospital\n\nor clinic fee in\n\nlast 30 days\n\n\n\nessential\n\n\n\nthe last 30\n\n\n\nHad to skip a\n\nmeal\n\n\n\nAdults had to\n\n\n\neat less to\nfeed small\n\n\n\nHad to borrow\n\n\n\nmoney for\n\n\n\n(including\n\n\n\nfood\n\n\n\ndrugs)\n\n\n\nchildren\n\n\n\nNote: Percentages of those that could not afford clinic fees are as\nshare of those not able to access healthcare in the last 30 days\n\n\nSource: Survey data, SAG estimates\n\n\n\nSource: Survey data, SAG estimates\n\n\n\n**Employment remains closely associated with significantly lower poverty rates, but size of**\n**employment income is key**\n\n\nJust like in the case of 2023 data, the current survey round suggests a strong link between employment and\npoverty. Whereas for individuals living in households with no one employed the poverty rate stands at a\nstaggering 62%, it drops to 10% for those with at least one person working. That", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000010:7:0:0", "start": 602, "end": 613, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9939, "luna_label": 1, "luna_reason": "Survey data are cited as the source for presented poverty-group figures."}, {"key": "sample:jad_paddy_docs:000010:7:0:1", "start": 1625, "end": 1634, "surface": "2023 data", "probe_tag": "keep", "probe_score": 0.9751, "luna_label": 0, "luna_reason": null}]}, {"key": "aj-069", "text": "\n**radically after February 24, 2022.**\nUp until 2021, Ukrainians in Poland were\nmostly men (close to two-thirds), who came\nfor work-related reasons, often leaving their\nfamilies back in Ukraine. The onset of the\nfull-scale conflict in Ukraine triggered the\narrival of individuals displaced by the war.\nThose were primarily women and children,\nwith men in Ukraine being mobilized for\nthe war effort. Social insurance data does\nnot reflect the full extent of the change,\nshowing only workers, without children and\nadults outside of employment.\n\n\n\n**Chart 3. Number of Ukrainians registered in Poland for social insurance by sex**\n\n\n2021 Q4 2022 Q4 2023 Q4 2024 Q2\n\n\n\nNumber of insured\nmen with Ukrainian\ncitizenship\n\n\n\nNumber of insured\nwomen with Ukrainian\ncitizenship\n\n\n\nNumber of insured\nwith Ukrainian\ncitizenship\n\n\n\nSource: Deloitte own elaboration based on ZUS data.\n\n\n3 Employed person is a person, who during the reference week worked for at least 1 hour for pay or profit, including contributing family workers; had a certain job\nattachment; or produced agricultural goods for sale or barter. A definition according to the Labour Force Survey: <u>https://ec.europa.eu/eurostat/statistics-explained/</u>\n<u>index.php?title=Glossary:Employed_person_-_LFS</u>\n\n\n08\n\n\n\nSource: Deloitte own elaboration based on the PESEL database as of September 2024.\n\n\n4 Available in the repository maintained by the government <u>https://dane.gov.pl/pl/dataset/2715</u> as well as UNHCR data portal <u>https://app.powerbi.com/</u>\n<u>view?r=eyJrIjoiODhkOGZiMzctZTliMi00NzA5LTgyM2QtZGZh", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:4:2:0", "start": 400, "end": 421, "surface": "Social insurance data", "probe_tag": "keep", "probe_score": 0.9986, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:4:2:1", "start": 862, "end": 870, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9734, "luna_label": 1, "luna_reason": "ZUS data underlies the chart on insured Ukrainians by sex."}, {"key": "sample:jad_paddy_docs:000001:4:2:2", "start": 1319, "end": 1333, "surface": "PESEL database", "probe_tag": "keep", "probe_score": 0.9802, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:4:2:3", "start": 1471, "end": 1488, "surface": "UNHCR data portal", "probe_tag": "keep", "probe_score": 0.9645, "luna_label": 0, "luna_reason": null}]}, {"key": "aj-070", "text": " in large metropolitan areas that offer\nthe most opportunities. Initially, in the first\nmonth after the outbreak of the full-scale\nwar in Ukraine, the number of renting\noffers in the OLX and Otodom portals\ndropped by approximately 60%, though it\nlater returned to previous levels.\n\n\n\n\n\nSince the beginning of the Russian invasion,\nthe number of Ukrainian workers covered by\nsocial security insurance in Poland increased\nby 126 thousand. The true increase\nof insured refugees however is larger.\nBy checking previous insurance status,\nZUS identified 228 thousand newly\nregistered insured persons active on 31st\nMarch 2023. <sup>31</sup> [^31: <u>[Cudzoziemcy w polskim systemie ubezpieczeń społecznych (zus.pl)](https://www.zus.pl/documents/10182/2322024/Cudzoziemcy+w+polskim+systemie+ubezpiecze%C5%84+spo%C5%82ecznych_2022.pdf/)</u> 32  According to NBP (2023), there were few respondents in this situation, and they most likely had secured other sources of income.] Considering the number\nof Ukrainians with active PESEL UKR at\nthe time, this would yield an employment\nrate of 43%. As this number does not take\ninto account jobs not covered by social\ninsurance and informal work, this can\nalign with the over 60% employment rate\nnoted in the surveys. This number does\nnot take into account over 500 thousand\nUkrainians that were paying social security\ncontributions before the beginning of the\nfull-scale war and remained in Poland after\nits outbreak.\n\n\nThe refugees present in Poland, while\nnot without difficulties, are largely able\nto provide for themselves and their\nfamilies. As reported in the Deloitte\n\n\n\nUkraine Refugee Pulse, which is based on\na survey carried out between October\nand December 2022, 40% of respondents\nhave enough income to meet basic needs\nor are able to support the same lifestyle\nthey had in Ukraine, while 60% say they do\nnot have enough income or have to rely on\nsavings and welfare. At the", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:14:1:0", "start": 1601, "end": 1634, "surface": "Deloitte\n\n\n\nUkraine Refugee Pulse", "probe_tag": "confusion", "probe_score": 0.3801, "luna_label": 1, "luna_reason": "Named survey report provides attributed income findings for Ukrainian refugees."}]}, {"key": "aj-071", "text": "**\n**policymakers.** According to a previous\nresearch, usually about a third of migrants\nholding a university degree work in jobs\nrequiring only high-school diplomas, versus\n10% of natives (Tani, 2020). Migrants may\nlack language fluency or country-specific\nknowledge (e.g. of the law or business\ncontacts) and find it difficult to prove the\nprofessional experience or educational\ncredentials gained in their home country.\nThey also face regulatory barriers to\nentering some professions, e.g., in the\npublic sector or regulated specialist\noccupations. Ensuring that migrants`\nand refugees` skills are fully utilized to\nthe benefit of both the individuals and\nhost-country economies is a central policy\nchallenge for the countries facing largescale immigration.\n\n\n**In Poland, non-EU27 citizens are twice**\n**as likely to be over-qualified (for their**\n**job) as Polish citizens.** In its Labour\nForce Survey (LFS), Eurostat defines overqualification rate as the share of persons\nwith tertiary education (bachelor’s degree\nand higher) who are employed in the ISCO\n(International Standard Classification of\nOccupations) 4-9 occupational groups <sup>25</sup> [^25: This is the same classification as quoted from GUS and ZUS in the previous chapters. Codes 4-9 refer to clerical support workers; service and sales workers; skilled\nagricultural, forestry and fishery workers; craft and related trades workers; plant and machine operators and assemblers; elementary occupations.] .\nWhile the LFS is unlikely to cover all\nUkrainian refugees and migrants from\nother nationalities in Poland, there was a\nvisible increase in over-qualification rates\nfor non-EU27 citizens between 2022 and\n2023 that is not observed for Polish\ncitizens.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 18. Over-qualification rates by citizenship**\n\n48%\n\n\nNon-EU27 Citizenship Polish Citizenship\n\n\n\nSource: Deloitte own elaboration based on Eurostat\n(Labour Force Survey) data.\n\n\n**Occupational downgrading is**\n**widespread among Ukrainian refugees**", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "jad_paddy_docs:000001:13:1:0", "start": 888, "end": 907, "surface": "Labour\nForce Survey", "probe_tag": "confusion", "probe_score": 0.8816, "luna_label": 1, "luna_reason": "Eurostat survey defines the rate and supplies chart data."}]}, {"key": "aj-072", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n## **1.** Ukrainian refugees in Poland\n\n##### The large influx of refugees since February 2022, added to a hefty and growing Ukrainian migrant population in Poland (1.1). Refugees have changed the demographics of the local Ukrainian residents, with most of them being women and children and many households led by women alone (1.2). In the past year, refugee household income sources have become more Poland-based (1.3).\n\n#### **1.1 Influx since February 2022**\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 1. Poland-Ukraine border movement balance and registered/active PESEL data**\n\n\n\n**Prior to the 2022 conflict in Ukraine,**\n**the population of Ukrainians in**\n**Poland was already significant and**\n**on the rise, but the exact number of**\n**migrants was challenging to quantify.**\nSince the onset of the armed conflict\nin eastern Ukraine in 2014, there was a\nconsistent influx of Ukrainians into Poland.\nMany of them sought work as Ukraine’s\neconomy declined and the currency\ndevalued. Most of the migrants came as\nguest workers, a status brought in by a\n2011 law enabling Ukrainians and five other\nnations to work in Poland for six months\n\n\n\nSource: Deloitte own elaboration based on Polish\nBorder Guard Headquarter and PESEL data.\n\n\n\n\n\nPESEL-UKR data Total entries-exits of the\nPolish-Ukrainian border\n\n\n\n\n\nduring a year without a work permit,\nbased on an employer’s declaration. This\nwas a circular migration, with Ukrainians\ncoming to Poland for half of the year,\nthen returning to Ukraine for another six\nmonths, and coming back to Poland. The\ndata on employers’ declarations do not\nreveal the actual number of Ukrainian\ncitizens who followed this system – a", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "jad_paddy_docs:000001:3:0:0", "start": 686, "end": 696, "surface": "PESEL data", "probe_tag": "confusion", "probe_score": 0.8762, "luna_label": 1, "luna_reason": "PESEL data underpin the chart analyzing Poland-Ukraine border movements."}, {"key": "jad_paddy_docs:000001:3:0:1", "start": 1360, "end": 1374, "surface": "PESEL-UKR data", "probe_tag": "keep", "probe_score": 0.9176, "luna_label": 1, "luna_reason": "Named PESEL-UKR data are presented in a chart of border entries and exits."}]}, {"key": "aj-073", "text": " the first stage, it\nadjusts for the non-random distribution\nof Ukrainian refugees across poviats,\nand in the second stage, it calculates\nthe models. These results can be\ninterpreted causally. The two previously\nmentioned instruments were used and\nyielded statistically significant results, as\nwell as passed statistical tests on their\nappropriateness for instrumenting the\nemployment share of Ukrainian refugees.\nOne instrument is the share of Ukrainian\nchildren in Polish schools, and the other is\nthe distribution of Ukrainian citizens across\nPoland in 2019, as recorded in declarations\n\n\n\nof intent to employ foreign workers. The\nresults are similar to the OLS estimation;\nhowever, when the second instrument is\nused without the first one, it gives a higher\nresult. Instrumental variables regressions\nshow that in 2023, a 1 percentage point\nhigher employment share of Ukrainian\nrefugees caused a PLN 70 higher wage\ngrowth (0.7 pp.) when instrumented by\nschool pupils' share, PLN 132 (1.4 pp.)\nwhen instrumented by 2019 Ukrainian\nworkers distribution, and PLN 75 (0.8 pp.)\nwhen both instruments were used. That\nsaid, the results should be treated with\ncaution, as the instruments used may not\nbe sufficiently exogenous to allow for fully\ncredible causal inference.\n\n\nThe early analysis by Gromadzki and\nLewandowski (2023), mentioned previously\nalso found a statistically significant effect\nof Ukrainian refugees on earnings. In their\nestimation, the share of the Ukrainian\nrefugee population had a small positive\nand statistically significant relationship with\nthe earnings of Polish women at a 0.1 level.\nTheir results for foreign women were\nalso positive, albeit smaller and lacking\nstatistical significance.\n\n\n\n2021, so the sample size is still growing.\nHowever, it is large enough to allow for\nsome cautious inference with 4.2 million\nPolish citizens in mid-2022 and 6.2 million\nin mid-2024. This has been combined with\naverage salaries in 128 occupational groups\nin GUS data for October 2022 (the most\nrecent data).", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:20:3:0", "start": 1974, "end": 1982, "surface": "GUS data", "probe_tag": "confusion", "probe_score": 0.6318, "luna_label": 1, "luna_reason": "GUS data are combined with salaries for analysis of occupational earnings."}]}, {"key": "aj-074", "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, "luna_reason": null}]}, {"key": "aj-075", "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, "luna_reason": "Existing survey data supports conclusions about refugee households."}, {"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, "luna_reason": null}]}, {"key": "aj-076", "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, "luna_reason": null}]}, {"key": "aj-077", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n## Executive summary\n\n\n##### Progress in market integration\n\n**Ukrainian refugees have been**\n**increasingly successful in terms of**\n**labour market integration.** The large\ninflux of refugees since February 2022,\nhas further increased and changed the\ndemographics of the already significant\nUkrainian migrant population in Poland.\nRefugees from Ukraine are primarily\nwomen and children, with over 67% of\nfemale-headed households. Poland was\nquick to open its labour market to refugees\nfrom Ukraine, who – despite difficulties\n\n- surprisingly promptly began their\neconomic integration and soon supported\nthemselves primarily from employment.\nIn the past year, refugees’ employment\nrate grew from 61% to 69%, with the\nmedian net wage rising from PLN 3,100 to\nPLN 4,000 and narrowing the gap to the\nmedian net wage in the entire economy.\n**As Ukrainian refugees entered the**\n**labour market, the economy adapted,**\n**resulting in more specialization and**\n**higher productivity.** In a simplistic\nsupply-demand framework, the influx\nof Ukrainian refugees would have had\na negative impact on Polish workers\nemployment or caused a decline in real\nwages. However, this has not occurred.\nFirst, Polish citizens employment rates\nhave grown, and unemployment rates\nhave fallen. Second, poviats in which the\nemployment share of Ukrainian refugees\nhas grown by 1 pp. saw 0.5 pp. higher\nemployment rates among Polish citizens,\nand 0.3 pp. lower unemployment rates.\nThird, there is no evidence of lowered\nwages, in fact the limited available data\nindicates that a higher share of Ukrainian\nrefugees in a poviat may have caused local\nwages to rise. Such findings are in line with\nacademic literature, which documents\na positive impact of migrants on native\nworkers. With foreigners entering the\n\n\n04\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n*", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:2:0:0", "start": 1588, "end": 1610, "surface": "limited available data", "probe_tag": "confusion", "probe_score": 0.8446, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-078", "text": " by\n1.4 pp. (PLN 6,000-8,000), 0.6 pp.\n(PLN 8,000-10,000), and 0.3 pp. (above\nPLN 10,000) (see Chart 30).\n\n\n\nThird, cross-section regression analysis\nshows that poviats with a higher number of\nUkrainian refugees saw a greater increase\nin wages, which was caused by larger\nshare of refugees in local employment.\nDue to limitations in the wage dataset,\nour calculations incorporated yearly data.\nCross-section models were estimated using\na data sample for all 380 poviats in 2023.\nThe share of Ukrainian refugees among\nall employed, temporarily employed, and\nself-employed persons insured at ZUS in a\ngiven poviat was averaged across quarters\nto construct a yearly variable. The wage\nvariable is the GUS data series on gross\nmonthly wages and salaries <sup>30</sup> [^30: Unfortunately, GUS data for these time periods and poviat level include the enterprise sector (firms that employ 10 or more persons) and public sector, which is\nmost of the labour market, but not the total economy.], and taken\nas nominal change in 2023 from 2022.\nThe ordinary least squares cross-section\nmodel shows that in 2023, a 1 percentage\npoint increase in the employment share of\nUkrainian refugees was associated with an\nincrease in wages of PLN 66. Given that in a\nmean poviat gross wage growth amounted\nto PLN 757 and the gross wage in the\nprevious year was PLN 5,803, wages have\ngrown by 13.1% in nominal terms (including\ninflation), of which 0.7 percentage points\nwere associated with Ukrainian refugees\n(considering the fact that the employment\nshare of the Ukrainian refugees in a mean\npoviat was 0.61%).\n\n\nInstrumental variables regressions were\nperformed with the Two Stage Least\nSquares technique. In the first stage, it\nadjusts for the non-random distribution\nof Ukrainian refugees across poviats,\nand in the second stage, it calculates\nthe models. These results can be\ninterpreted causally. The", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:20:2:0", "start": 337, "end": 349, "surface": "wage dataset", "probe_tag": "keep", "probe_score": 0.9753, "luna_label": 1, "luna_reason": "Existing wage data informed calculations using yearly observations."}, {"key": "sample:jad_paddy_docs:000001:20:2:1", "start": 381, "end": 392, "surface": "yearly data", "probe_tag": "keep", "probe_score": 0.9971, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:20:2:2", "start": 698, "end": 713, "surface": "GUS data series", "probe_tag": "confusion", "probe_score": 0.8794, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:20:2:3", "start": 785, "end": 793, "surface": "GUS data", "probe_tag": "keep", "probe_score": 0.9712, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-079", "text": " security administration, also known as Social Insurance Institution.\n\n\n44\n\n\n\nChart 1. Poland-Ukraine border movement balance and registered/active PESEL data\b 07\nChart 2. Ukrainians registered for social insurance\b 07\nChart 3. Number of Ukrainians registered in Poland for social insurance by sex\b 08\nChart 4. Age and gender structure of Ukrainian refugees\b 09\nChart 5. Ukrainian refugee households’ demographic composition\b 10\nChart 6. Local population shares of Ukrainian refugees\b 11\nChart 7. Income of Ukrainian refugee households by source\b 12\nChart 8. Ukrainian refugee household incomes from Poland and Ukraine in 2023 and 2024\b 12\nChart 9. Ukrainian refugee labour status 15\nChart 10. Ukrainian refugee median net wage 15\nChart 11. Main occupational groups of Ukrainian refugees, pre-war Ukrainians, other foreigners,\n\nand Polish citizens registered for social insurance, Q2 2022 and Q2 2024 (civilian, non-agricultural)\b 16\nChart 12. Ukrainian refugee wages relative to Polish citizens in the same employee-cells\b 17\nChart 13. Polish citizens and Ukrainian refugees’ employment rates by age group\b 18\nChart 14. Ukrainian refugee median net wage estimates in Q2 2024\b 19\nChart 15. Ukrainian refugees wages median net wage by age group\b 20\nChart 16. Median net wages of Ukrainian refugees median net wage by sector \b 21\nChart 17. Gross domestic product growth paths with and without Ukrainian refugees\b 23\nChart 18. Over-qualification rates by citizenship\b 27\nChart 19. Tertiary education and corresponding occupational groups shares\b 28\nChart 20. Ukrainian refugees median net wages by educational attainment\b 28\nChart 21. Share of regulated professions by citizenship and legal status, Q2 2024\b 29\nChart 22. Econometric model of determinants of Ukrainian refugees’ net wages\b 32\nChart 23. Average number of months since arrival of a Ukrainian refugee by Polish language fluency\b 33\nChart 24. What Ukrainian refugee groups have weakest Polish language fluency?\b 33\nChart 25. Ukrainian refugees’ employment rate in", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:22:1:0", "start": 148, "end": 158, "surface": "PESEL data", "probe_tag": "confusion", "probe_score": 0.6316, "luna_label": 0, "luna_reason": null}]}, {"key": "aj-080", "text": " This is particularly\nevident among women, who make up the\nmajority of refugees. The employment\nrate for women aged 20-64 has grown\nconsistently from 68.2% in Q2 2021 to\n70.2% in Q2 2022, 71.7% in Q2 2023, and\n72.2% in Q2 2024 (see Chart 26). The\nunemployment rate for women aged\n20-64 has consistently fallen, from 3.5% in\nQ2 2021 to 3.0% in Q2 2022, 2.6% in both\nQ2 2023 and Q2 2024.\n\n\n\nSecond, panel regression analysis shows\nthat a larger influx of Ukrainian refugees\nat the poviat level was accompanied by\na larger increase in employment rates\nfor Polish citizens along with a larger\ndecrease in registered unemployment\nrates. The data sample for all regressions\nencompassed quarterly data for all\n380 poviats from Q1 2022 to Q2 2024.\nThe Ukrainian refugee share variable was\nconstructed as the share of Ukrainian\nrefugees among all employed, temporarily\nemployed, and self-employed persons\ninsured with ZUS in a given poviat at the\nend of the quarter. The employment rate\nof Polish citizens was constructed as the\nshare of Polish citizens who are employed,\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\ntemporarily employed, and self-employed\nand who are insured with ZUS, divided by\nthe population from GUS in a given poviat\nin a given quarter (estimated based on halfyear population data). The unemployment\nrate is the registered unemployment\nrate series from GUS. Fixed effects panel\nregressions show that an increase in the\nemployment share of Ukrainian refugees\nby 1 percentage point correlates with an\nincrease in Polish citizens' employment\nrates by 0.5% and a decrease in the\nunemployment rate by 0.3%, in the\npreferred specification with dummy\nvariables for all quarters. All results are\nstatistically significant at a 0.01 level.\n\n\n\n**", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:19:1:0", "start": 680, "end": 714, "surface": "quarterly data for all\n380 poviats", "probe_tag": "confusion", "probe_score": 0.8312, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:19:1:1", "start": 1303, "end": 1327, "surface": "halfyear population data", "probe_tag": "confusion", "probe_score": 0.745, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-081", "text": "# Appendix. Model Calibration\n\n\n##### D.Climate is CGE 51 model developed by Deloitte Economic Institute based on GTAP model 52 . If source of data is not specified it means that shocks were calibrated to data from the model database.\n\nIt was assumed that impact of refugees on\nthe Polish economy was felt as combined\nfour different shocks: to population, labour\nsupply, average propensity to save, and\nproductivity, with shocks to population and\nlabour supply being balanced by equivalent\nshocks in Eastern Europe <sup>53</sup> [^53: Region combined from: Russia, Belarus, Ukraine, Moldova, Czechia, Slovakia, Hungary, Romania, and Bulgaria.] .\nMoreover, as part of assumed increase\nin spending by Ukrainians in Poland was\nfinanced by savings from Ukraine this was\nbalanced by equivalent negative shock on\ninvestment in Eastern Europe.\n\n\nShock to population was calibrated to\nmatch data for residents of Poland from\nStatistics Poland and number of refugees\nbased on PESEL UKR. Equivalent shock in\nEastern Europe was calculated using data\nfor population in this region from World\nPopulation Prospects UN.\n\n\n\nShock to labour supply was calibrated\nto match data of working Ukrainians\npresented in chapter 2. As we didn’t have\ndata of number of unemployed refugees,\nwe calibrated it that an increase in the\nnumber of workers due to an increase in\nlabour supply matched employment data\nin two variants: lower (around 225 thou.)\nand higher employment (around 350 thou.).\nTotal increase in labour supply in Poland\nwas balanced by equal decrease of labour\nsupply in Eastern Europe.\n\n\nShock to propensity to save was calculated\nin two steps. First and foremost, it was\nassumed that due to their precarious life\nsituation refugees won’t save any income.\nAs such shock was set to match shock for\nthe population <sup>54</sup> [^54: All shocks are percent deviations.] . Additionally, to account\nfor spending of savings from Ukraine, data\nfrom the National Bank of Ukraine on\ncash", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:21:0:0", "start": 883, "end": 911, "surface": "data for residents of Poland", "probe_tag": "keep", "probe_score": 0.9983, "luna_label": 1, "luna_reason": "Statistics Poland data calibrate the population shock for Poland."}, {"key": "sample:jad_paddy_docs:000007:21:0:1", "start": 967, "end": 976, "surface": "PESEL UKR", "probe_tag": "keep", "probe_score": 0.9527, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:21:0:2", "start": 1034, "end": 1068, "surface": "data\nfor population in this region", "probe_tag": "keep", "probe_score": 0.9942, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:21:0:3", "start": 1074, "end": 1103, "surface": "World\nPopulation Prospects UN", "probe_tag": "keep", "probe_score": 0.9994, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:21:0:4", "start": 1155, "end": 1181, "surface": "data of working Ukrainians", "probe_tag": "keep", "probe_score": 0.9773, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:21:0:5", "start": 1366, "end": 1381, "surface": "employment data", "probe_tag": "confusion", "probe_score": 0.8191, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-082", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**Refugee employment rates significantly improved in 2024, moving closer to national levels**\n\n\nUkrainian refugee employment rates <sup>14</sup> [^14: The employment rate is defined as the number of employed or self-employed individuals of working age (15-64) as a share of\nthe total number of people in this age group] have experienced a sizeable increase from 2023 in most countries,\nrising by 9 percentage points year-over-year at the regional level to 64%. This indicator now stands just 7\npercentage points below the equivalently weighted mean for the host population (71%).\n\n\nRefugee employment grew both owing to a decrease in unemployment, which declined to 9% from 13% last\nyear, as well as new individuals coming into the labor force <sup>15</sup> . In fact, the 2024 labor force participation rate\namounted to 70%, which stands almost in line with the equivalent host country indicator of 73%.\n\n\n**<u>EVOLUTION OF THE REFUGEE EMPLOYMENT RATE FROM 2023 TO 2024</u>**\n\n\n70%\n\n\n60%\n\n\n50%\n\n\n40%\n\n\n30%\n\n\n20%\n\n\n10%\n\n\n\n0%\n\n\n\nEmployment\n\nrate 2023\n\n\n\nEmployment\n\nrate 2024\n\n\n\nUnclear\nlabor force\n\nstatus\n\n\n\nDecrease in\nunemployment\n\n\n\nDecrease in\n\npotential\nlabor force\n\n\n\nExpansion of\n\n\n\nforce\n\n\n\nthe labor\n\n\n\nNote: Only includes data from the 7 countries surveyed in both rounds (Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and\nSlovakia)\n\n\nSource: Survey data, SAG estimates\n\n\n**<u>REFUGEE VS HOST EMPLOYMENT RATES BY COUNTRY</u>**\n\n\nRefugee (2023) Refugee (2024) Host (2023)\n\n\n\n76% 76%\n\n\n\n76%\n\n\n\n\n\n\n\n62%\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Estonia Hungary Latvia Lithuania Moldova Poland Romania Slovakia Region\n\n\nNote: For comparability, employment rates for host countries have been recalculated assuming a similar gender distribution to that of refugees\n\n\nSource: ILO, survey data\n\n\n14. The employment rate is defined", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000010:9:0:0", "start": 1489, "end": 1500, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9928, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:9:0:1", "start": 1502, "end": 1515, "surface": "SAG estimates", "probe_tag": "keep", "probe_score": 0.9921, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:9:0:2", "start": 1916, "end": 1927, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.8192, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-083", "text": " is more uncertain, resulting in the\n\n\n\nneed to finance their consumption using\ncapital generated outside the country e.g.\nsavings from their home countries.\n\n\nHigh employment rate of Ukrainian\nrefugees translates into more workers and\nthus additional economic growth. In July\n2022, OECD estimated the contribution of\nUkrainian refugees to the labour force and\nemployment in European host countries,\nbased on a 2014 Labour Force Survey adhoc module that includes refugee labour\nmarket outcomes and 2019 LFS with\noutcomes of recent non-EU migrants.\nThey estimated that Ukrainian refugees\nwould increase employment in Poland by\n1.2-1.8% (Dumont & Lauren, 2022).\nAs refugee employment rates are higher\nthan expected, the actual increase is higher,\nbetween 1.4% and 2.2% (estimates of 0,230,35 million relative to LFS employment).\n\n\n##### To assess the impact of refugees on the Polish economy, a simulation was performed using the Deloitte D.Climate model 33,34 .\n\nIt is a general equilibrium model using consumer and producer optimalisation to\ncalculate changes in the economy in response to shocks. This allows to assess the\nimpact of shocks considering supply and demand channels as well as connections\nbetween different sectors of economy. This provides information about their total\nimpact on many aspects of the economy including the labour market, government\nrevenue as well as main economic aggregates. It is deemed the most appropriate\ntool to account for refugees’ multi-layered impact. To account for the effects of other\nshocks happening in the economy (such as the energy crisis) counterfactual analysis\nwas performed using the newest data for the Polish economy. In other words, we\nwere able to isolate the refugee inflow from all other shocks in the economy, like\nthe other macroeconomic consequences of the war in Ukraine. The results were\ncalculated for 2022 and 2023 with additional long-term analysis to check how the\neconomy would adapt assuming no new shocks including no counter-shocks <sup>35<", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:15:2:0", "start": 411, "end": 435, "surface": "2014 Labour Force Survey", "probe_tag": "confusion", "probe_score": 0.6603, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:15:2:1", "start": 498, "end": 506, "surface": "2019 LFS", "probe_tag": "confusion", "probe_score": 0.7001, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:15:2:2", "start": 1638, "end": 1672, "surface": "newest data for the Polish economy", "probe_tag": "confusion", "probe_score": 0.8279, "luna_label": 1, "luna_reason": null}]}, {"key": "aj-084", "text": " immigrant inflow comprised less educated workers.\n\n\n39\n\n\n\nMacroeconomic modelling generally finds\nthat immigration increases output, while\ncross-country econometric estimates\nfind also positive impacts on labour\nproductivity. The first approach builds\na theoretical model of the economy\ncalibrated to the particular circumstances,\nwhich allows us to simulate counterfactuals\nand observe all changes in the economy.\nThe second approach relies on empirical\ndata, usually over many years and\ncountries, trying to isolate the effect of\nimmigration, but gives no information on\nthe channels through which these effects\noperate. In the present case it would not\nbe practical to follow anything else than the\nfirst approach. Unfortunately, it relies on\nthe canonical labour market model.\nAs Peri (2014) elaborates, that model\nassumes that immigration is simply a\nshift in the labour supply for a given\nlabour demand and given labour supply\nof native workers. It further assumes\nthat immigrants are essentially identical\nto natives in that they enter the same\n\n\n38\n\n\n\noccupations and perform the same tasks,\nthe native workers do not change their\noccupations and tasks, while firms do\nnot adjust (at least in the short term).\nIn effect, immigrants grow output, but\nslightly lower wages. This is the case in\nthe modelling performed (Chapter 3), as\nwell as in the NBP general equilibrium\nmodelling exercise for the pre-2022\nUkrainian workers 2013-2018 performed\nby Gradzewicz, Jabłonowski, Sasiela, and\nŻółkiewski (2021). Conversely, Peri (2014)\nsurveys 270 econometric estimates from\n27 studies published over the 1982-2013\nperiod on the impact of immigration\non native wages. He shows that effects\nfor a very large immigrant inflow of 10\npercentage points (five-times larger than\nthe refugees from Ukraine share in Poland)\non native wages range from -1% to +1%,\nwith most estimates clustered around zero,\nfrom -0.1% to +0.1%. Similarly, Gromadzki\nand Lewandowski (2023) examine\neconometrically the", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000007:19:2:0", "start": 446, "end": 460, "surface": "empirical\ndata", "probe_tag": "drop", "probe_score": 0.0095, "luna_label": 0, "luna_reason": null}]}, {"key": "aj-085", "text": " among\nproductive-age individuals, with women\n18-65 making up 40% and men only 18% of\nUkrainian refugee population. This is largely\ndue to the conscription of military aged\nmen in Ukraine.\n\n\n\n66+\n\n\n56-65\n\n\n46-55\n\n\n36-45\n\n\n26-35\n\n\n18-25\n\n\n<18\n\n\n\nsocial insurance. Second, the statistical\ndefinition of an employed person is wider\nthan just being registered with the Social\nInsurance Institution (Zakład Ubezpieczeń\nSpołecznych, ZUS) on a certain day <sup>3</sup> [^3: Employed person is a person, who during the reference week worked for at least 1 hour for pay or profit, including contributing family workers; had a certain job\nattachment; or produced agricultural goods for sale or barter. A definition according to the Labour Force Survey: <u>https://ec.europa.eu/eurostat/statistics-explained/</u>\n<u>index.php?title=Glossary:Employed_person_-_LFS</u>] . Third,\nsome Ukrainians work in the shadow\neconomy. The number of workers with\nUkrainian citizenship and social insurance\nincreased from only 33 thousand at\nthe end of 2013 to 627 thousand at the\nend of 2021 – just before the refugee\ninflux. Some of this increase reflects\nthe transitioning of Ukrainians to more\nregular work arrangements and securing\nwork permits. Following February 2022,\n\n\n\nthe numbers further increased with the\narrival of Ukrainian refugees. The data for\nmid-2024 shows 771 thousand workers\nwith Ukrainian citizenship registered for\nsocial insurance, including 247 thousand\nrefugees. Ukrainian refugees in Poland\nhave been given PESEL UKR numbers\n\n- a version of an ID number assigned to\nevery Polish national – which made high\nquality data on their demographics easily\navailable, though they may still be missing\nfrom most of the statistics compiled by the\nPolish statistical office.\n\n\n\n20% 19%\n\n\nMen Women\n\n\n\n1%\n\n\n1%\n\n\n2%\n\n\n\n3%\n\n\n4%\n\n\n\n7%\n\n\n8%\n\n\n\n4%\n\n\n4%\n\n\n\n9%\n\n\n\n12%\n\n\n\n7%\n\n\n#### **1.2 Households characteristics**\n\n\n\n**The structure of the Ukrainian**", "source": "jad_paddy_docs", "subset": "annotate_aj", "spans": [{"key": "sample:jad_paddy_docs:000001:4:1:0", "start": 722, "end": 741, "surface": "Labour Force Survey", "probe_tag": "keep", "probe_score": 0.9286, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:4:1:1", "start": 1326, "end": 1343, "surface": "data for\nmid-2024", "probe_tag": "drop", "probe_score": 0.0039, "luna_label": 0, "luna_reason": null}]}, {"key": "aj-086", "text": "000 live births in 2015. Infant mortality is estimated at between 87 to 90 per 1,000 live births.\nSeveral factors help explain the performance of Chad’s health sector, including: (i) limited financial\nresources; (ii) salient shortages of health workers and inadequate infrastructure; and (iii) significant\ngeographic barriers to the delivery of health services. In addition, Chad’s Joint External Evaluation\n(JEE) conducted in 2017 revealed important capacity constraints in all 19 technical areas. This shows\nthe country’s vulnerability to health security threats.\n\n7. **Health financing in Chad is insufficient and highly inequitable** . In 2017, Chad spent 4.5 percent of its\nGDP on health. In per capita terms, the country only spent US$ 32. This is less than countries of similar\nlevels of income and less than other countries in the region <sup>1</sup> [^1: LICs countries spent on average US$ 35, while Sub-Saharan African countries spent US$ 82 (World Bank\n2020)] . Furthermore, health has not been\nsufficiently prioritized in Chad’s public budget and the share of health to general government\nspending has declined over the last decade from over 12 percent in 2009 to less than 4 percent in\n2018, well below the Abuja target of 15 percent. In fact, the largest share (61.2 percent) of Chad’s\ntotal health spending is financed by households through out-of-pocket payments. This poses major\nchallenges in terms of the equity, the efficiency and the sustainability of the country’s health financing\narchitecture.\n\n8. **Health facilities have low readiness levels to deliver quality health services** . According to the most\nrecent SARA survey, one in three health facilities had access to electricity and two in three had access\nto improved water sources. The availability of essential medical equipment (scales, thermometers,\nstethoscopes, etc.) and laboratory capacity were also substandard (WHO, 2019). In terms", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000017:3:1:0", "start": 1637, "end": 1648, "surface": "SARA survey", "probe_tag": "keep", "probe_score": 0.9458, "luna_label": 1, "luna_reason": "Existing SARA survey provides facility electricity and water access findings."}]}, {"key": "aj-087", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n\nbeneficial over the lifecycle in all regions where fire exceeds a 5 percent annual likelihood. For example,\nwhere existing low-voltage and medium-voltage lines will be considered, a fire-retardant application to\nall wood poles reduces wildfire damages. For normal conductor lines in these regions, using steel poles is\ncost-effective to reduce damages. Further, for the highest risk and/or most critical regions, consideration\nof aerial bundled cables reduces the need for vegetation management and damages resulting from fires,\nas well as the likelihood of causing a fire in high-risk regions.\n\n3. **Flooding is considered as ‘high’ risk for Chad** although the risk varies geographically and\nseasonally. Available information for the depth of water under an expected 0.01 annual flood probability\n(100-year flood) <sup>54</sup> [^54: FATHOM Flooding Data [Fluvial]. The World Bank Group 2021.] is shown in Figure 4.1. This means that there is a 1 percent chance in any given year of\na flood occurring at the depths shown in Figure 4.1. In a 30-year project time frame, this amounts to a 26\npercent likelihood of occurring at least once. It should be noted that the data presented in Figure 4.1 are\nbased on historical occurrences, which are limited by the available historical record. Climate change\nmodels show that along the Sahel and Central Africa regions, the current 0.01 annual probability flood\nevent could increase in frequency to a 0.015–0.2 annual probability event or greater (the historical 100year event may become as frequent as a 50–75-year event), although the models vary. <sup>55</sup>\n\n4. **The risk for ‘extreme heat’ in the current climate conditions is rated ‘high’ in Chad.** Extreme heat\nhas impacts on energy demand (cooling for buildings),", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000051:80:0:0", "start": 903, "end": 923, "surface": "FATHOM Flooding Data", "probe_tag": "keep", "probe_score": 0.9445, "luna_label": 1, "luna_reason": "Named flooding dataset underlies Figure 4.1 and flood-depth risk analysis."}]}, {"key": "aj-088", "text": "\npercentage of Syrian refugee households in extreme poverty (under the survival minimum\nexpenditure basket - SMEB) increased from 55 percent in 2019 to 88 percent in 2020 (UNHCR, 2019).\nAccording to a recent WFP assessment, 50 percent of Lebanese and 75 percent Syrians are worried\nabout not having enough food to eat. Loss of income and reduced production of food, essential for\ndietary sufficiency and diversity resulting from increased prices (e.g., eggs, milk, meat and fresh\nvegetables), will translate into greater food insecurity and reliance on external assistance over an\nextended period of time, particularly among the refugee populations and the poorest in host\ncommunities.\n\n\nSectoral and Institutional Context\n\n4. **Agriculture constitutes one of the main livelihood sources for vulnerable communities in rural areas**\n**and is seen as a critical sector for employment creation and poverty reduction in Lebanon** . The\nagricultural sector contributed 3 percent to the country’s gross domestic product (GDP) and\nemployed 11 percent of the active working population in 2019 (World Bank, 2021); the broader agrifood sector contributes to a higher percentage of GDP when related value chain activities are\ncounted. Agriculture has the highest rate of those in poverty (40 percent) and the highest rate of\ninformal employment (85 percent), comprised mostly of women and refugees (FAO,2021). According\nto the General Agricultural Census carried out in 2010, the total utilized agricultural area (UAA) was\n231,000 ha cultivated by 169,512 agricultural holders. Almost half of the agricultural holders relied\nsolely on agricultural activities for their livelihoods and occupied around 63 percent of the UAA (FAO,\n2021). Crop production represents about 60 percent of agricultural output, while livestock production\naccounts for 40 percent (Dal _et al._ 2021). Exports accounted for an average of 21.1 percent of\n\n\nSep 02, 2021 Page 3 of 10", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000036:2:1:0", "start": 208, "end": 222, "surface": "WFP assessment", "probe_tag": "keep", "probe_score": 0.9609, "luna_label": 1, "luna_reason": "WFP assessment provides the cited food-insecurity finding."}, {"key": "jdc_operational:000036:2:1:1", "start": 1416, "end": 1443, "surface": "General Agricultural Census", "probe_tag": "keep", "probe_score": 0.9315, "luna_label": 1, "luna_reason": "Census data support reported agricultural area and holder figures."}]}, {"key": "aj-089", "text": "or overwhelming the health sector response, and bringing\neconomic activity to a halt. Furthermore, Uganda has been advancing plans to start oil production in\n2023, but the decline in oil prices to a projected US$30 in 2020 – half the estimated breakeven price for\noil production in Uganda – and US$40 in 2021 will likely slow oil-related FDI and could shift oil production\nbeyond 2025. This will have a negative impact on Uganda’s longer-term growth trajectory.\n\n5. **The deterioration in macroeconomic conditions due to the COVID-19 pandemic will lead to liquidity**\n**constraints and a rapid rise of non-performing loans in the financial sector.** Stress in global financial\nmarkets has already surged to historical levels and market volatility has already spurred demand for\nliquidity. In the immediate term, households and SMEs in Uganda will face a sharp reduction in income\ndue to business closures under lockdown; disruption in supply chains, deliveries and payments; a fall in\ntourism; a fall in consumption and investment, among others, all leading to an extensive job loss.\n\n\n6. **Uganda is the land “bridge” for the rest of the Great Lakes region** <sup>**4**</sup> [^4: Consists of Burundi, Democratic Republic of Congo, Kenya, Malawi, Rwanda, Tanzania and Uganda] **, connecting its landlocked neighbors**\n**to the coastal countries.** Regional integration is indispensable for Uganda and provides great opportunity\nto foster trade with its neighbors. Uganda is ranked 102 in the Logistics Performance Index 2018 and\ninfrastructure and tracking & tracing are the two areas it is ranked very low with a ranking of 124 and\n123 respectively <sup>5</sup> [^5: https://openknowledge.worldbank.org/bitstream/handle/10986/29971/LPI2018.pdf] . The efficiency of the transit traffic performance in the major road corridors is critical\nfor supporting and sustaining competitive international trade in the", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000001:3:1:0", "start": 1493, "end": 1525, "surface": "Logistics Performance Index 2018", "probe_tag": "keep", "probe_score": 0.9496, "luna_label": 1, "luna_reason": "Named index provides Uganda's ranking and area-specific performance findings."}]}, {"key": "aj-090", "text": "municipalities, the development grants will increase from the current average of about 1 US$ per capita to\nthe proposed US$ 45 (in the peak year) per capita and for the old municipalities from the current average\nof US$ 20 <sup>75</sup> [^75: Using up-dated population figures from FY 2017/18.] per capita to the proposed US$ 45 (peak year) per capita under the USMID AF Program. The\nProgram will provide funding for supporting capacity building/institutional strengthening activities at the\neighteen municipal LGs level so as to strengthen their capacity for urban management, investment and\noperations and maintenance (O&M) of urban infrastructure services. The support will continue to focus on:\n(i) discretionary institutional strengthening – to address the gaps regarding operation skills focusing on\nbridging the gaps identified during the performance assessments, in core areas of importance for urban\ndevelopment and for handling of infrastructure programmes; (ii) tooling – to address the gap of inadequate\ntools, equipment and facilities; and (iii) career development.\n\n44. **USMID AF will maintain the requirement of minimum staffing level before a MLG can access**\n**the MDG.** USMID AF will address capacity issues that if not attended to would pose serious threats to the\nsuccessful implementation of the Program. The widespread vacancies in key professional and technical\ncadres pose a significant risk to the successful implementation of the Program especially in the additional\nMLGs joining the Program from 2018/19. It is therefore necessary for some basic staffing requirements to\nbe met by each of the participating municipal LGs before they access the enhanced MDG under the\nProgram. These requirements are aimed at providing the basic safeguards in ensuring that each participating\nmunicipal LG has in place a core team necessary for effective physical planning, financial management,\nprocurement, execution of infrastructure projects and promotion of local economic development.\n\n45. **As a minimum requirement**, the following core administrative and technical positions should be\nsubstantively filled before", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000062:67:0:0", "start": 249, "end": 292, "surface": "up-dated population figures from FY 2017/18", "probe_tag": "keep", "probe_score": 0.9229, "luna_label": 1, "luna_reason": "Population figures support per-capita grant calculations."}]}, {"key": "aj-091", "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, "luna_reason": "Existing survey reports a concrete violence prevalence finding."}, {"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, "luna_reason": "Named survey cited as evidence for violence prevalence among Ugandan women."}]}, {"key": "aj-092", "text": " 2.50**\n**million):** This component will support the development and testing of social and behavior change\n(SBC) messages and materials to raise awareness, knowledge and understanding among the general\npopulation about the risk and potential impact of the pandemic and to promote prevention measures,\nincluding hand-washing, hygiene and social distancing. Targeted messages will be developed for\nvulnerable groups, including refugees living in refugee camps and internally displaced persons, as\nsocial distancing and other prevention measures will need to be adapted to the different realities of\nrefugees living in refugee camps and people on the move. Partnerships with organizations with\nexperience addressing such vulnerabilities in Chad will be explored.\n\n29. Communication activities will cover the entire country using cost effective channels of\ncommunications such as radio, television and social media as appropriate, as well as SBC campaigns\nin schools, workplaces, and through ongoing outreach activities of various ministries and sectors,\nespecially ministries of health, education, agriculture, and transport. This will be done after a rapid\ncommunity behavior assessment to gather information about the knowledge, attitudes, beliefs and\nchallenged related COVID-19 response. This component will primarily finance the production of SBC\nand mass media products as well as buying the air time of mass media. These materials will be\ntranslated to French, Arabic and local languages. Advocacy communication and community\nmobilization activities through civil society organizations including religious and tribal leaders,\ncommunity health worker and community organization will also be supported, especially in rural\nareas.\n\n30. **Component 3. Implementation Management, Monitoring and Evaluation and Coordination (US$**\n**1.00 million):** This component will finance operational costs of the project implementation Unit (PIU).\nThese include equipment, additional staff and other operational expenses needed to implement the\n\n\nApr 03, 2020 Page 9 of 13", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000017:8:1:0", "start": 1150, "end": 1185, "surface": "rapid\ncommunity behavior assessment", "probe_tag": "confusion", "probe_score": 0.1944, "luna_label": 0, "luna_reason": "Assessment is planned to gather information for communication activities."}]}, {"key": "aj-093", "text": "**The World Bank**\nUganda Secondary Education Expansion Project (P166570)\n\n\nFor information on how to submit complaints to the World Bank Inspection Panel, please visit\n_[<www.inspectionpanel.org>](http://www.inspectionpanel.org/)_ .\n\n120. **Citizen Engagement.** Citizens were engaged at the Project design stage through consultations on the\nproject design on national level and in the districts. On national level, different Civil Society Organizations and\nNon-Government Organizations were engaged by the MoES and the World Bank team from early project\npreparation stage in discussing key issues to be addressed by the Project, expected Project results, activities and\nimplementation modalities. The Project design was presented and discussed with the stakeholders: local\ncommunities, families, teachers, local leaders, students in selected districts during the Safeguards Frameworks\npreparation. During the Project implementation, the consultation process with regular feedback to close the loop\nwill continue, through engaging citizens in early identification of risks and impacts for each school construction\nsite. The following three-stage consultation process was agreed on: (i) preliminary site appraisal / rapid site\nassessment with consultations with LG administrators; (ii) social and environmental screening and wide\nstakeholder consultation; and (iii) detailed site appraisal and assessments in consultations with local communities.\nThe associated tools for carrying out these appraisals are developed and agreed by the MOES and the World Bank.\n\n121. The PCU will develop the Project specific website to facilitate citizens’ access to information. The website\nwill share information about the project implementation progress and provide opportunities for the citizens on\na national level to request information on project interventions. Feedback logs will be available at every district\nand school targeted by the project. LG and school principals will be aggregating the feedback and communication\nto the PCU and MoES on a periodic (e.g. monthly) basis in line with a protocol to be detailed in the POM. Urgent", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000018:44:0:0", "start": 1851, "end": 1864, "surface": "Feedback logs", "probe_tag": "confusion", "probe_score": 0.2703, "luna_label": 0, "luna_reason": "Future project feedback logs will be established and aggregated."}]}, {"key": "aj-094", "text": "**The World Bank**\nSupport for Social Recovery Needs of Vulnerable Groups in Beirut (P176622)\n\n\nof GBV in the country was forced and early marriage of girls. <sup>17</sup> This risk may increase given ongoing pressures on many\nhousehold incomes.\n\n**10.** **GBV also affects men and boys, including within the refugee and migrant communities.** Some of these men and boys\nare at risk of being coerced into unwanted sexual acts or may be forced by circumstances to engage in survival sex. In\nconflict situations, men and boys, just as women and girls, are raped or subjected to other forms of sexual violence.\nIn a 2013 rapid assessment of male refugees from Syria (aged 12-24), 10.8 percent had experienced an incident of\nsexual harm or harassment in the previous three months, of which none had accessed support services. <sup>18</sup> Data in\nBeirut/Mount Lebanon indicates that in 2020, 21% of child sexual abuse survivors are boys under the age of 18 <sup>19</sup> .\n\n\n**11.** **The COVID-19 outbreak increased the risk of GBV, particularly after the first strict lockdown that had been put in**\n**place to prevent the spread of the virus.** This trend has been recorded worldwide in countries which enforced strict\nlockdowns. <sup>20</sup> Lebanese authorities reported a marked increase of 51% in the number of incoming calls to the GBV\nhotline related to domestic violence from February 2020 to October 2020. <sup>21</sup> During strict lockdowns, household\ntensions can spiral as families are confined to their homes with poor job security, causing stress, anxiety, and an\nenvironment where the likelihood of intimate partner violence can be heightened. The pandemic and ensuing\nlockdown", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000054:5:0:0", "start": 618, "end": 662, "surface": "rapid assessment of male refugees from Syria", "probe_tag": "keep", "probe_score": 0.9001, "luna_label": 1, "luna_reason": "2013 assessment supports the reported sexual-harm prevalence finding."}, {"key": "jdc_operational:000054:5:0:1", "start": 836, "end": 864, "surface": "Data in\nBeirut/Mount Lebanon", "probe_tag": "confusion", "probe_score": 0.2599, "luna_label": 1, "luna_reason": "Geographic data are tied to a concrete 2020 survivor finding."}]}, {"key": "aj-095", "text": "**The World Bank**\nRoads and Employment Project (P160223)\n\n\nagriculture from these restrictions therefore allowing Syrians to work in these three sectors. The above\ndecision confirms a situation that pre‐existed the Syria crisis, and which continues today, where Syrians\nrepresent a large part of the workforce in the three above‐mentioned sectors.\n\n\n21. **A US$100 million investment in road works is expected to create 500,000 to 750,000 labor‐days**\n**of direct short‐term jobs for Lebanese host communities and Syrian refugees in Lebanon.** An ongoing\njoint World Bank‐ILO assessment estimates that about 500,000 labor‐days of short‐term direct jobs are\ncreated for every US$100 million invested in roads. These figures were estimated based on actual road\ncontracts within the Greater Beirut region. A similar road investment in rural areas, where wages and cost\nof living are lower, is expected to create a higher number of jobs in the range of 750,000 labor‐days. In\naddition, and while difficult to estimate with confidence, about 100,000 to 500,000 labor‐days of indirect\nand induced jobs will also be created (production of construction materials at local shops and factories,\ntransportation of materials, maintenance of equipment). While having very high labor‐intensive projects\nis difficult in an upper middle‐income country like Lebanon given existing modern construction practices,\nthe labor content of construction projects in Lebanon could be increased if additional necessary civil works\nsuch as culverts and drainage, retaining walls, and sidewalks are included. Meanwhile, routine\nmaintenance have the highest labor content for road works and is essential to increase the durability and\nefficiency of roads investments. An important dimension for job creation programs, especially in the\nemergency context, is to execute projects in a relatively short period of time therefore benefiting more\npeople (a form of “cash transfer” involving more beneficiaries) rather than creating fewer permanent jobs\nover a", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000008:20:0:0", "start": 748, "end": 769, "surface": "actual road\ncontracts", "probe_tag": "confusion", "probe_score": 0.1278, "luna_label": 1, "luna_reason": "Actual road contracts provided the basis for estimating labor-day creation."}]}, {"key": "aj-096", "text": "**The World Bank**\nGreater Beirut Public Transport Project (P160224)\n\n\nproject to be implemented by the CDR are (a) the lack of proper registry of asset lists; (b) delays in\nsubmission of timely audit reports; and (c) complex project where many other agencies are involved such\nas the RPTA and MPWT and that entails PPP and operators’ contributions. and the need to have strong\nFM staff within the PIU to be able to advise and support such activities and assume coordination among\nthe different stakeholders.\n\n\n6. Thus, to mitigate FM-related risks, (a) the CDR will ensure that the assets module of its accounting\nsoftware is well operationalized and is able to capture the work in progress and the assets acquired under\nthe project, (b) it will recruit an acceptable external auditor in the early stages of the project to enable\nconstant audit compliance, and (c) additional staff will be recruited as needed to ensure that FM\nimplementation is well supervised and followed up, in addition to the preparation of a Project\nImplementation Manual including an FM chapter that will detail the FM arrangements to be established\nfor carrying out the project FM implementation and defining the roles and responsibilities. The FM chapter\nwill include a detailed description of the process for expropriation and resettlement and the working\nrelationship with the RPTA, the PPP modalities, and the operators’ contributions mechanisms.\n\n\n7. **Staffing.** The existing CDR FO has adequate experience in managing World Bank-financed projects\nand will thus manage the project FM arrangements. This FO will be supervised by the Head of Funding\nDivision at the CDR and may be assisted by an additional financial staff as needed and as project activities\nbecome more complex.\n\n\n8. **Project accounting software.** The CDR has in place customized accounting software that has been\nused for the FM implementation of the World Bank-financed projects and can be used to record the\nproject’s accounting transactions and generate the", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000022:55:0:0", "start": 135, "end": 158, "surface": "registry of asset lists", "probe_tag": "confusion", "probe_score": 0.0575, "luna_label": 0, "luna_reason": "Asset registry is routine project financial and fixed-asset administration."}]}, {"key": "aj-097", "text": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda (P176747)\n\n\n\n|Col1|including childcare and<br>those community and<br>household members.|Col3|Col4|the infrastructure<br>facilities.|Col6|\n|---|---|---|---|---|---|\n|Women beneficiaries (percentage)|<br>|||<br>||\n|Women in RHD||||||\n|Refugee women||||<br>||\n|Value of credit provided to women<br>enterprises (Amount)|This indicator measures the<br>value of credit provided by<br>the PFIs under the project,<br>disaggregated by refugee<br>status, district, age, and<br>disability status.|Continuous.<br>|PFI data.<br>|The PFIs will maintain<br>databases of the value<br>of the credit disbursed,<br>disaggregated by<br>refugee status, district,<br>age, and disability<br>status.<br>|The MGLSD to collect<br>the data from the PFIs<br>each month, and<br>compile and report it.<br>|\n|Women enterprises in RHDs||||<br>||\n|Refugee-owned enterprises||<br>||||\n|Beneficiaries of job-focused interventions||Enterprise<br>baseline<br>survey,<br>annual<br>surveys from<br>year 2.<br>|Surveys of<br>enterprises.<br>|This indicator measures<br>", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000025:50:0:0", "start": 599, "end": 607, "surface": "PFI data", "probe_tag": "confusion", "probe_score": 0.1566, "luna_label": 0, "luna_reason": "Span is a data-source cell within a project indicator table."}, {"key": "jdc_operational:000025:50:0:1", "start": 805, "end": 823, "surface": "data from the PFIs", "probe_tag": "confusion", "probe_score": 0.5402, "luna_label": 0, "luna_reason": "MGLSD plans to collect and compile this project monitoring data monthly."}]}, {"key": "aj-098", "text": " table-benches (which are the state norm for student furniture), then there was only one seating place\nfor every four students. Approximately one fifth of available furniture was in poor condition.\n\n  - **Learning environments are in poor physical state and often unsanitary.** Only half of classrooms are constructed\nfrom baked or dried bricks, with the same proportion being classified as in a bad physical state. There are on\naverage 188 students per latrine block. Roughly half of schools have a water source from a tap or pump; another\n45 percent have water brought to the school; and 4 percent report no access to water. Only 40 percent have\nhand-washing facilities.\n\n  - **There are insufficient basic teaching-learning materials** . On average, three students share the language textbook;\nfour students share the mathematics textbooks; and thirty students share the science textbook. Shortages of\nconsumables such as chalk, pens, pencils and paper are the rule.\n\n\n11 PASEC. _Qualité des Systèmes Éducatifs en Afrique SubSaharienne Francophone. Performances et Environnement de l’Enseignement-Apprentissage_\n_au Primaire_ . Hereafter referred to as PASEC 2014 and 2019, the years in which the learning outcomes surveys were conducted.\n12 PASEC 2019.\n\n\nAug 03, 2021 Page 7 of 27", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000009:6:2:0", "start": 1200, "end": 1225, "surface": "learning outcomes surveys", "probe_tag": "confusion", "probe_score": 0.7384, "luna_label": 1, "luna_reason": "PASEC 2014 and 2019 surveys support reported education findings."}]}, {"key": "aj-099", "text": "**The World Bank**\nEnhancing Community Resilience and Local Governance Project Phase II (P177093)\n\n\n26. **In recognition of the importance of strengthening the capacity of government institutions, ECRP-II will shift**\n**from a third-party implementation approach to a government-led approach** . Due to concerns about the Government’s\nfiduciary integrity and capacity constraints at the time of project design, ECRP is being implemented by the United Nations\nOffice for Project Services (UNOPS) in partnership with the International Organization for Migration (IOM). <sup>52</sup> [^52: As of the latest Implementation Status and Results (ISR) prepared in March 2021, the rating for project progress towards achievement of Project Development\nObjective (PDO) is Satisfactory while implementation progress is Moderately Satisfactory. As of April 8, 2021, total project disbursement stands at US$8.56 million,\nrepresenting 19 percent of the total project amount.] ECRP-II\nproposes to shift to a government-led approach. This shift reflects several progressive developments: (a) the new CEN’s\nrenewed advocacy of a gradual, differentiated shift toward government-led implementation; (b) recent positive reforms\nwithin Government towards improved public financial management (PFM) and a more robust national budget planning,\napproval, and execution process; and (c) the Government’s successful implementation of the LGSDP. Finally, as ECRP’s\nown preparation and implementation illustrate, Government ownership can be lacking in projects that are implemented\nby third parties. It is thus proposed that the Ministry of Finance and Planning (MoFP) be the implementing agency in\ncollaboration with the Local Government Board (LGB). This is the same arrangement that proved effective under LGSDP,\nwhich performed well under very difficult circumstances. To leverage the institutional memory of ECRP implementation\nand the field offices and teams already established, it is proposed that one of the UN agencies that is implementing ECRP\nbe contracted by the Government to implement the activities on the ground.\n\n27. **The", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000027:11:0:0", "start": 604, "end": 637, "surface": "Implementation Status and Results", "probe_tag": "confusion", "probe_score": 0.6221, "luna_label": 1, "luna_reason": "ISR cited with project progress ratings and disbursement figures"}]}, {"key": "aj-100", "text": "br>internet in the household or at the<br>community level (Number)||0.00|75,000.00|150,000.00|250,000.00|\n|Of which host (Number)||0.00|||125,000.00|\n|Of which refugees (Number)<br>||0.00|||125,000.00|\n|Women assessed as digitally literate post<br>the completion of digital skills training<br>(Percentage)<br> <br>||0.00|40.00|60.00|80.00|\n\n\n\n\n\n\n\n|Monitoring & Evaluation Plan: PDO Indicators|Col2|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Indicator Name **|**Definition/Description **<br>|**Frequency **<br>|**Datasource **<br>|**Methodology for Data**<br>**Collection **<br>|**Responsibility for Data**<br>**Collection **|\n|Broadband penetration (fixed + mobile) in<br>selected areas|Measures the penetration of<br>national broadband<br>connectivity using fixed and|<br>Annually<br>|<br>Data from<br>UCC<br>|<br>Annual surveys used by<br>NITA-U and consistent<br>with methodology used|<br> <br>NITA-U<br>|\n\n\nPage 39 of 76", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000023:51:1:0", "start": 810, "end": 813, "surface": "UCC", "probe_tag": "confusion", "probe_score": 0.8837, "luna_label": 1, "luna_reason": "Named source for broadband penetration indicator data in the monitoring table."}, {"key": "jdc_operational:000023:51:1:1", "start": 822, "end": 836, "surface": "Annual surveys", "probe_tag": "confusion", "probe_score": 0.4511, "luna_label": 0, "luna_reason": "M&E plan specifies recurring future survey-based indicator monitoring"}]}, {"key": "aj-101", "text": "**The World Bank**\nRoads and Employment Project (P160223)\n\n\nlarger group of people, or people who are more vulnerable within a specific geographic region.\n\n\n84. **The project will ensure citizen engagement through multiple instruments.** First, the project will\nensure local participation in the selection of road priorities, and a simple questionnaire will be addressed\nby CDR to all union of municipalities to consult with them on their priorities with regard to the\nrehabilitation of national roads crossing their respective towns/districts. The list of priority roads\nidentified by municipalities will be checked against the results of the road condition survey and will be\nconsidered for selection under the project if justified on sound technical and economic basis. Second, the\nproject will measure citizens’ satisfaction with the implemented projects through a survey to assess the\nlevel of satisfaction of beneficiaries from the implemented projects; and third, the GRM will allow citizens\nto directly voice concerns or grievances to the implementing agency and ensure that these concerns are\nresponded to and addressed in a timely manner.\n\n\n85. **The project will particularly encourage broader participation and benefits for women.** The\nconsultations will engage women in discussions on the types of jobs in construction or related supporting\nsectors they could most benefit from. Specific arrangements will also be made for women to be able to\ntake on work directly and indirectly linked to the project activities. It is important to note, however, that\namong some social groups in Lebanon, including large segments of the refugee population, women’s\nengagement in construction‐related labor is not encouraged according to social and cultural norms.\nChanging these norms goes beyond the scope of the project, but these norms could in fact change due to\nmeasures put in place to encourage women’s labor, if these measures are determined by women\nthemselves. The project will also track the differential impacts of its activities on men and women. Results\nwill be disaggregated by gender whenever feasible and reporting will look at whether gender gaps narrow\nthroughout", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000008:44:0:0", "start": 644, "end": 665, "surface": "road condition survey", "probe_tag": "confusion", "probe_score": 0.073, "luna_label": 1, "luna_reason": "Survey results inform technical selection of priority roads."}]}, {"key": "aj-102", "text": "**The World Bank**\nRoads and Employment Project (P160223)\n\n\n**VI.** **Subproject Approval**\n\n\n8. In the event that a subproject involves acquisition against compensation, the PIU through the\nconcerned implementing entity shall:\n\n\n(a) not approve the subproject unless satisfactory compensation has been agreed between the\n\naffected person and the local community; and\n\n\n(b) not allow works to start until the compensation has been delivered in a satisfactory manner\n\nto the affected persons.\n\n\n7. **Complaints and Grievances**\n\n\n9. Initially, all complaints should be registered by the PIU and the concerned implementing entity as the\ncase maybe, which shall establish a register of resettlement/compensation related grievances and\ndisputes mechanism. The existence and conditions of access to this register (where, when, how) shall\nbe widely disseminated within the community/town as part of the consultation undertaken for the\nsub‐project in general. A committee of knowledgeable persons, experienced in the subject area, shall\nbe constituted at a local level as a Committee to handle first instance dispute/grievances. This group\nof mediators attempting amicable mediation/litigation in first instance will consist of the following\nmembers: (a) Head of District; (b) Legal advisor; (c) Local Representative within the elected Council;\n(d) Head of Community Based Organization; and (e) Community leaders. This mediation committee\nwill be set up at local level by the implementation agency on an “as‐needed” (that is, it will be\nestablished when a dispute arises in a given community).\n\n\n10. When a grievance/dispute is recorded according to above‐mentioned registration procedures, the\nmediation committee will be established, and mediation meetings will be organized with interested\nparties. Minutes of meetings will be recorded. The existence of this first instance mechanism will be\nwidely disseminated to the affected people as part of the consultation undertaken for the", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000008:83:0:0", "start": 671, "end": 750, "surface": "register of resettlement/compensation related grievances and\ndisputes mechanism", "probe_tag": "confusion", "probe_score": 0.2093, "luna_label": 0, "luna_reason": "The mechanism will be established for future grievance registration."}]}, {"key": "aj-103", "text": "**The World Bank**\nUganda: Investment for Industrial Transformation and Employment (P171607)\n\n\n\n\n\n|Col1|supported by the Bank, and<br>the number of businesses<br>that benefited from<br>financial services.|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|Number of SMEs with a loan or line of<br>credit|<br>|Annual<br>collection<br>|Web based<br>platform<br>report<br> <br>|Data down loaded<br>annual from web based<br>platform<br>|Bank of Uganda (BoU)<br>|\n|Number of formally employed in the<br>manufacturing sector according to PAYE<br>data collected by URA TIN numbers (# of<br>jobs)<br> <br>|Number of formally<br>employed in the<br>manufacturing sector<br>according to PAYE data<br>collected by URA TIN|Annual<br>|<br>Uganda<br>Revenue<br>Authority,<br>Commissione<br>r's General<br>Office,<br>Research<br>Dept<br>|<br>Based on annual tax<br>PAYE reports<br>|Project Implementation<br>Teams<br>|\n\n\n|Monitoring & Evaluation Plan: Intermediate Results Indicators|Col2|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Indicator Name **|**Definition/Description **|**Frequency **", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000021:59:0:0", "start": 537, "end": 570, "surface": "data collected by URA TIN numbers", "probe_tag": "confusion", "probe_score": 0.7833, "luna_label": 0, "luna_reason": "Span is embedded in a table indicator definition."}, {"key": "jdc_operational:000021:59:0:1", "start": 673, "end": 682, "surface": "PAYE data", "probe_tag": "confusion", "probe_score": 0.2762, "luna_label": 1, "luna_reason": "PAYE data from Uganda Revenue Authority supports employment indicator calculation."}, {"key": "jdc_operational:000021:59:0:2", "start": 846, "end": 858, "surface": "PAYE reports", "probe_tag": "confusion", "probe_score": 0.651, "luna_label": 0, "luna_reason": "Embedded in a monitoring-plan table rather than presented as standalone data use."}]}, {"key": "aj-104", "text": " robust\nmonitoring, on-the-ground supervision; as well as to track progress, take stock of implementation experience, and\nidentify challenges for timely response and rapid course correction. It will include a strong learning agenda to take stock\nof lessons learned and experiences of particular aspects of the social and economic interventions to inform and facilitate\nthe scale-up of activities, as well as other possible topics such as targeting assessments, process evaluation, and\ncomponent impact assessments, to be further discussed and agreed during the project implementation. Finally, the\ncomponent will provide capacity building to CRA, through which it will support the implementation of project activities\nextended to refugees and host communities. Capacity building support will also enhance inter-government coordination\nbetween the CRA and the project’s implementing agency, as well as other humanitarian and development partners on\nthe ground.\n\n\n**C. Project Beneficiaries**\n\n\n56. **The proposed SNSOP will target 96,000 poor and vulnerable HHs, of which 65,000 will continue to be from the**\n**SSSNP caseload, while the remaining 31,000 will be new HHs, of which 7,000 HHs are refugees and 10,500 HHs are**\n**from host communities.** As such, it is estimated that 672,000 poor and vulnerable individuals will benefit from the various\ninterventions under the project. <sup>34</sup> [^34: This assumes 6.6 members per HHs, calculated as an average from the actual number of HH members under the previous World Bank-funded\nsafety net projects.] The operational scope of SNSOP will increase considerably from that of SSSNP. Under\nSNSOP, cash transfers to a beneficiary household will increase to an 18-month duration (from nine-month duration) and\nthe number of countries will increase to 15 (from 10). The allocation of beneficiary HHs across the different interventions\nwill be as follows:\n\n\na. **Sub-component 1.1 - Cash Transfer:** 70 percent of total caseload for cash assistance (i.e.", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000057:30:1:0", "start": 1111, "end": 1125, "surface": "SSSNP caseload", "probe_tag": "confusion", "probe_score": 0.1382, "luna_label": 1, "luna_reason": "Existing SSSNP caseload informs beneficiary targeting and projected household coverage."}]}, {"key": "aj-105", "text": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n\nproject. This component will also finance coordination activities. These include meetings of Technical\nCoordination committees, Coordination’s meetings at different level of the health system and\noperation costs of Emergency Operation Center.\n\n\n.\n\n\n\n\n\n**Note to Task Teams:** This summary section is downloaded from the PAD data sheet and is editable. It should\nmatch the text provided by E&S specialist. If it is revised after the initial download the task team must manually\nupdate the summary in this section. **_Please delete this note when finalizing the document._**\n\n\n31. The main concerns relate to testing and treatment of infected persons, handling of medical samples\nand waste by medical professionals and local community health and safety. The project will finance\nequipment for selected primary health care facilities and hospitals to improve their ability to deliver\ncritical medical services including testing, treatment and hospitalization. The PIU will prepare an ESMF\nto provide clear guidance regarding the treatment of medical waste, guidelines for community\nengagement and the preparation of subproject ESMPs (if needed). The ESMF will also incorporate\ninternational protocols for community health and safety during a pandemic and measures to address\nGBV/SEA. The ESMF will be consulted with stakeholders using the modified approach currently under\npreparation and publicly disclosed per the requirements of the ESF no later than 30 days after Project\neffectiveness.\n\n\n**Note** : To view the Environmental and Social Risks and Impacts, please refer to the Appraisal Stage ESRS Document.\n**_Please delete this note when finalizing the document._**\n\n\n**E. Implementation**\n\n\nInstitutional and Implementation Arrangements\n\n\n32. **The Government of Chad has established several working groups to coordinate the response to**\n**COVID-19.** The Health Security Committee is a high-level committee formed by ministers and chaired\nby the Secretary General", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000017:9:0:0", "start": 409, "end": 423, "surface": "PAD data sheet", "probe_tag": "confusion", "probe_score": 0.8103, "luna_label": 0, "luna_reason": "Names a template data sheet without showing substantive data use."}]}, {"key": "aj-106", "text": "**The World Bank**\nChad - Refugees and Host Communities Support Project (P164748)\n\n\n**<mark>ANNEX 2: GEO-SPATIAL ANALYSIS</mark>**\n\n\n1. A major obstacle to effective targeting of development projects in many developing\ncountries is the lack of existing datasets. One option to overcome this obstacle is remote sensing.\nFor this project, preliminary work has been done to determine the host population around selected\ncamps using remote sensing imaging analysis. To generate population estimates, the analysis uses\nworld population census data and statistical modeling based on the relationship between\npopulations and physical socioeconomic characteristics such as land uses, dwelling units and\nimage pixel characteristics.\n\n2. The figure shows the population layer within 25 km of selected camps, and the table shows\npopulation estimates at 50 km, 25 km, 15 km, 10 km and 5 km from the camps. Since village\nboundary information for Chad is not available in world population data, satellite imagery and\nestimates of average village size from the most recent census will be used to approximate the\nnumber of host villages around each camp.\n\n\n**Population Layer within 25 km of Selected Camps in the East, South, and Lake Chad**\n\n**Regions**\n\n\nSource: World Bank Geospatial Operations Support Team (GOST).\n\n\nPage 77", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000035:81:0:0", "start": 514, "end": 542, "surface": "world population census data", "probe_tag": "confusion", "probe_score": 0.8737, "luna_label": 1, "luna_reason": "Census data are used in modeling to generate population estimates."}, {"key": "jdc_operational:000035:81:0:1", "start": 958, "end": 979, "surface": "world population data", "probe_tag": "keep", "probe_score": 0.9361, "luna_label": 1, "luna_reason": "Existing population data are cited as insufficient, motivating substitute spatial estimates."}]}, {"key": "aj-107", "text": "d by gender, refugees, hosts) (Text)||Baseline surveys to be done|Rating of 4 out of 5|\n|Local labor among unskilled employment created under the<br>works contracts (dis-aggregated by gender, refugees, hosts)<br>(Text)||0.00|40 percent of total labor (Number of workers)|\n|Percentage increase of beneficiaries who engaged in economic<br>activities (dis-aggregated by refugees/ host community,<br>female/male) (Number)||0.00|30.00|\n|Percentage of women employed in construction and<br>maintenance of Project road (Percentage)||0.00|30.00|\n|Health and Safety Management Plans (Text)||To be developed for the Project road works|Plans are in place, root cause analysis of accidents at<br>project site are undertaken, and mitigation measures are|\n\n\nPage 52 of 80", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000050:56:1:0", "start": 38, "end": 54, "surface": "Baseline surveys", "probe_tag": "confusion", "probe_score": 0.1215, "luna_label": 0, "luna_reason": "Baseline surveys are planned to be conducted, so the data do not yet exist."}]}, {"key": "aj-108", "text": "RR in excess of\nthe social discount rate of 6 percent across all cost-benefit analyses under the baseline scenario and the sensitivity\nanalysis. While data limitations make it difficult to analyze the economic impact of the project on refugees and host\ncommunities, specifically, it is important to note that benefits will flow to these communities similarly as to other targeted\nproject communities. Further, UNHCR has recently initiated a ‘Flagship Survey’ that will provide rich new data on the\nsocioeconomic conditions of refugees and hosts in South Sudan, which could enable focused economic analysis of project\nimpact on these two communities as implementation moves forward.\n\n\nPage 40 of 74", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000057:44:2:0", "start": 442, "end": 457, "surface": "Flagship Survey", "probe_tag": "confusion", "probe_score": 0.1591, "luna_label": 0, "luna_reason": "Survey will provide new data; data production is future and not yet used."}]}, {"key": "aj-109", "text": "- must also comply with the investment menu covering the GoU Transitional Development Grant;\n\n   - be consistent with the guidelines developed for the DDEG and the respective Sector Development\nGrants.\n\n46. The table below reflects the core changes to the DLI tables, whereas the full set of new DLIs,\nverification protocol and disbursement tables are included in annex 2. DLIs 1-6 have been maintained but\nstrengthened, whereas 7-8 are new DLIs targeting the sub-window for refugees and host communities.\n\n**Table 7: DLI status and revisions made under the AF** <sup>43</sup> [^43: Please refer to annex DLI table for detailed breakdown of the DLIs per year.]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|&<br>disbursements)<br>(targets<br>DLIs<br>DLI 1:<br>Program LGs<br>have met all<br>Program<br>Minimum<br>Conditions<br>DLI 2:<br>Program LGs<br>have<br>strengthened<br>institutional<br>performance<br>DLI 3:<br>Program LGs<br>have<br>implemented<br>Infrastructure<br>Action Plans<br>DLI 4:<br>Program LGs<br>have<br>implemented<br>Institutional<br>Strengthening<br>Plans<br>DLI 5:<br>MoLHUD has<br>executed<br>Performance<br>Improvement<br>Plans for<br>Program LGs<br>DLI 6:<br>Program LGs<br>with Town<br>Clerks in Place<br>DLI 7: Results<br>on physical<br>planning, land<br>tenure security<br>and urban<br>infrastructure<br>development<br>in Program|", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000062:25:0:0", "start": 256, "end": 266, "surface": "DLI tables", "probe_tag": "confusion", "probe_score": 0.8582, "luna_label": 0, "luna_reason": "Reference to tables in a table-introducing sentence"}, {"key": "jdc_operational:000062:25:0:1", "start": 599, "end": 614, "surface": "annex DLI table", "probe_tag": "confusion", "probe_score": 0.5141, "luna_label": 0, "luna_reason": "Reference to a project DLI table, not an independently used data resource."}]}, {"key": "aj-110", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\n\n\n\n\n\n\n\n|Col1|within SNSOP project<br>locations that are satisfied<br>or very satisfied with assets<br>created through LIPW<br>divided by the total number<br>of beneficiaries and non<br>beneficiaries surveyed in<br>SNSOP project locations.<br>SNSOP project locations<br>refer to bomas or quarter<br>councils where the SNSOP<br>project is active|a quarterly<br>basis during<br>missions and<br>ISRs|System|and satisfaction surveys<br>carried out by the<br>SNSOP M&E team. In<br>addition, satisfaction<br>will be monitored by<br>the Third Party Monitor<br>(TPM)|Col6|\n|---|---|---|---|---|---|\n|Beneficiary households receiving<br>economic opportunities|Number of total beneficiary<br>households of Component 1<br>that are also receiving<br>economic opportunities<br>under Component 2, in<br>accordance with the Project<br>Operations Manual, and<br>have received at least 1<br>installment of the livelihood<br>grant.|This indicator<br>will be<br>measured at<br>a minimum<br>on a<br>quarterly<br>basis.<br> <br>|SNSOP<br>Management<br>Information<br>System (MIS)<br> <br>|Data on participation in<br>Component 2 will be<br>collected at registration<br>where based on the<br", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000057:57:0:0", "start": 500, "end": 520, "surface": "satisfaction surveys", "probe_tag": "drop", "probe_score": 0.0199, "luna_label": 0, "luna_reason": "Surveys are carried out by the project M&E team, indicating project data production."}]}, {"key": "aj-111", "text": "\nthe activities supported under the Project and the Project’s compliance to environmental, social, technical,\nhealth, and safety requirements. The World Bank will also work closely with the UNHCR to continually\nmonitor the protection environment throughout project implementation. Details of the implementation\narrangements and the Implementation Support Plan are provided in Annex 1.\n\n84. A Project implementation manual will be prepared. It will contain detailed arrangements and procedures\nfor implementation of the Project including inter alia: (i) implementation arrangements including\ndelineation of roles and responsibilities of various entities, institutions and agencies involved in Project\nimplementation and their coordination; (ii) the procurement procedures and standard procurement\ndocumentation; (iii) disbursement arrangements, reporting requirements, financial management\nprocedures and audit procedures; (iv) procedures for preparing and reviewing a consolidated annual work\nplan and budget for each Fiscal Year; (v) the Project performance indicators and monitoring and evaluation\narrangements; (vi) arrangement and procedures for mitigating environment and social risks and impacts;\n(vii) grievance redress mechanism; (viii) information, education and communication of Project activities; and\n(ix) such other administrative, financial, technical and organizational arrangements and procedures as shall\nbe required for the Project.\n\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n\n85. **Results Framework.** The Results Framework lists the indicators to monitor Project outputs and outcomes.\nData will be disaggregated by gender and refugee/host community wherever feasible. UNRA and the World\nBank will jointly assess the achievement of the PDO at least twice a year as part of the World Bank’s\nimplementation support missions. UNRA will prepare quarterly progress reports describing the progress of\nactivities under the Project and the result indicators, the status of legal covenants, compliance with the\nenvironmental and social safeguards, implementation of gender actions, disbursements, results of\nsatisfaction surveys, and citizen engagement, and share with the World Bank. In addition to the quarterly\n\n\nPage 34 of 80", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000050:38:1:0", "start": 2133, "end": 2153, "surface": "satisfaction surveys", "probe_tag": "drop", "probe_score": 0.0406, "luna_label": 0, "luna_reason": "Future project monitoring reports will include survey results; no existing survey use is shown."}]}, {"key": "aj-112", "text": "ary means for fostering improved teaching<br>practices. Successful completion of the training by the<br>awarding of a teacher certificate will serve as the verification<br>means. Data should be disaggregated by gender.|MOE Training and<br>certification records|Third Party|The verification agency will verify<br>training records for teachers'<br>training and will check teacher<br>certification records. The<br>certification mechanism will need<br>to be compliant with the<br>requirements agreed upon with the<br>WB as specified in the Program<br>Operations Manual.|\n\n\n38", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000041:45:3:0", "start": 239, "end": 260, "surface": "certification records", "probe_tag": "drop", "probe_score": 0.0044, "luna_label": 0, "luna_reason": "Future verification machinery checks project training certification records."}, {"key": "jdc_operational:000041:45:3:1", "start": 312, "end": 328, "surface": "training records", "probe_tag": "drop", "probe_score": 0.0044, "luna_label": 0, "luna_reason": "Future verification of training records is planned monitoring machinery."}]}, {"key": "aj-113", "text": "**The World Bank**\nLebanon Health Resilience Project (P163476)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPage 4 of 54", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000032:6:0:0", "start": 19, "end": 52, "surface": "Lebanon Health Resilience Project", "probe_tag": "drop", "probe_score": 0.0412, "luna_label": 0, "luna_reason": "Project title does not identify or use an existing data resource."}]}, {"key": "aj-114", "text": "):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|\n|**4.**Municipal** r**oads built<br>or rehabilitated with<br>related infrastructure<br>using urban LDG|√|3|Km<br>Targets|53.02|Measured<br>Annually|Measured<br>Annually|Measured<br>Annually|Measured<br>Annually|Measured<br>Annually|Annually|Municipal reports|Participating<", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000062:36:4:0", "start": 1379, "end": 1396, "surface": "Municipal reports", "probe_tag": "drop", "probe_score": 0.0017, "luna_label": 0, "luna_reason": "Table cell naming municipal reports, not an independently used data resource."}]}, {"key": "aj-115", "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, "luna_reason": "Data will be collected and used prospectively by the project."}, {"key": "jdc_operational:000032:28:0:1", "start": 1307, "end": 1322, "surface": "hospital claims", "probe_tag": "confusion", "probe_score": 0.2578, "luna_label": 1, "luna_reason": "Hospital claims are identified as an existing source for triangulating program data."}, {"key": "jdc_operational:000032:28:0:2", "start": 1740, "end": 1762, "surface": "registration databases", "probe_tag": "confusion", "probe_score": 0.42, "luna_label": 1, "luna_reason": "Existing registration databases are integrated to track and manage grievances."}]}, {"key": "aj-116", "text": "**The World Bank**\nChad - Refugees and Host Communities Support Project (P164748)\n\n\n\n\n\n\n\n\n\n\n|Col1|Col2|Col3|Col4|committee and the<br>World Bank.|Col6|\n|---|---|---|---|---|---|\n|Classrooms rehabilitated or newly built||Twice a<br>year<br>|Baseline data<br>collected<br>from UNHCR<br>and WFP on<br>number of<br>classrooms<br>built or<br>rehabilitated<br>in target<br>areas. The<br>CFS is<br>launching a<br>baseline<br>study which<br>will help to<br>confirm<br>baseline<br>numbers, to<br>be reviewed<br>at MTR.<br>CFS<br>Management<br>information<br>system -<br>CNARR -<br>Ministry of<br>Education<br>|CFS local offices<br>produce simple reports<br>by region on number of<br>classrooms<br>rehabilitated or built.<br>Reports are then<br>consolidated by CFS<br>centrally and shared<br>with the Project<br>Steering Committee<br>and the World Bank.<br>There will be two<br>reports delivered per<br>year: in June and<br>December. Figures are<br>reported for the period<br>in question (6<br>months) and<br>cumulatively.<br>|CFS<br>|\n\n\n\nPage 49", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000035:53:0:0", "start": 240, "end": 253, "surface": "Baseline data", "probe_tag": "drop", "probe_score": 0.0274, "luna_label": 0, "luna_reason": "Baseline data are being collected and confirmed through a planned project study."}]}, {"key": "aj-117", "text": "*Project Development Objective (PDO)**\n\n**PDO Statement**\n\n30. The PDO is to increase access to electricity and clean cooking in Chad.\n\n**PDO-Level Indicators**\n\n31. PDO-level indicators include\n(a) People provided with access to new or improved electricity service (Corporate Results\n\n\n\nIndicator, number), out of which\n(i) Women (number);\n(ii) Refugees (number); and\n(iii) Host communities (number).\n(b) People provided with access to clean cooking (number), out of which\n\n\n\n(i) Refugees (number); and\n(ii) Host communities (number).\n\n**B.** **Project Components**\n\n32. The project comprises five components. Table 1 summarizes the five components, IDA financing,\nand anticipated division of labor between the public and private sectors.\n\n\n\n17 _[https://documents.worldbank.org/en/publication/documents-reports/documentdetail/844591582815510521/world-bank-](https://documents.worldbank.org/en/publication/documents-reports/documentdetail/844591582815510521/world-bank-group-strategy-for-fragility-conflict-and-violence-2020-2025)_\n_[group-strategy-for-fragility-conflict-and-violence-2020-2025](https://documents.worldbank.org/en/publication/documents-reports/documentdetail/844591582815510521/world-bank-group-strategy-for-fragility-conflict-and-violence-2020-2025)_\n\n\nPage 18 of 87", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000051:23:1:0", "start": 267, "end": 297, "surface": "Corporate Results\n\n\n\nIndicator", "probe_tag": "drop", "probe_score": 0.0431, "luna_label": 0, "luna_reason": "Project indicator label, not existing data use or reported finding."}]}, {"key": "aj-118", "text": "**The World Bank**\nSouth Sudan Emergency Food and Nutrition Security Project (P163559)\n\n\n63. Other risks relate to the rapidly deteriorating macroeconomic situation in South Sudan coupled with\nthe significant depreciation of the local currency relative to the US$, which could further present a risk of\nmisapplication of project resources. These risks are effectively mitigated by the involvement of UN\nagencies in supplying key goods and services to the implementation. The UN agencies have adequate\ntechnical and fiduciary capacity to implement similar types of emergency operations. Each of the UN\nagencies will sign a contract with MAFS as a basis for the engagement. During the course of\nimplementation, the agencies will each submit quarterly funds utilization reports (depicting both physical\nprogress and financial information), which will be validated by the PIU in line with the existing contract,\nbefore sharing with the Bank. The quarterly utilization reports will be submitted to the Bank within 45\ndays after the end of the quarter. Further, the UN has got adequate machinery to access insecure locations\nand project sites.\n\n\n64. Funds disbursed into the Designated Account (DA) for the implementation of component 3 will be\nring‐fenced from ministry‐wide fiduciary risks by ensuring segregated project accounts (DA), cashbooks\nand financial statements, operated, maintained and prepared by the PIU. The PIU will maintain an up to\ndate contract register as well as an assets register. Similarly, the financial management team will prepare\nmonthly bank reconciliation statements to ascertain the accuracy of the cash balances in the DA. Fiduciary\noversight will be effected by the IAD, the NAC and other private audit firms working with the two\nGovernment institutions. Under the EFNSP, IAD in collaboration with a contracted private audit firm, will\nconduct in‐year risk‐based audit of project activities in order to strengthen internal controls. The in‐year\ninternal audit reviews will be conducted at least once a year and the audit reports will be shared with\nMAFS, Ministry", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000038:30:0:0", "start": 1450, "end": 1467, "surface": "contract register", "probe_tag": "drop", "probe_score": 0.0269, "luna_label": 0, "luna_reason": "Routine project contract bookkeeping and fiduciary administration."}, {"key": "jdc_operational:000038:30:0:1", "start": 1482, "end": 1497, "surface": "assets register", "probe_tag": "drop", "probe_score": 0.0225, "luna_label": 0, "luna_reason": "Routine project asset-record bookkeeping, not substantive data reuse."}]}, {"key": "aj-119", "text": " is set based on the<br>assumption that the current<br>proportion in enrollment|Data will be<br>reported at<br>midterm and<br>end of<br>project.<br>|Baseline:<br>UNHS.<br>Monitoring: E<br>MIS<br>|Enrollment data<br>||\n\n\n\nPage 52 of 96", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000018:57:1:0", "start": 196, "end": 211, "surface": "Enrollment data", "probe_tag": "drop", "probe_score": 0.0166, "luna_label": 0, "luna_reason": "Future project monitoring and reporting, not already-used enrollment data."}]}, {"key": "aj-120", "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": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000025:3:0:0", "start": 706, "end": 738, "surface": "Uganda National Household Survey", "probe_tag": "drop", "probe_score": 0.0194, "luna_label": 0, "luna_reason": "Survey is only defined in a glossary, with no data use or finding."}]}, {"key": "aj-121", "text": ") business development services in the long term through\ncollaboration with ILO and UNHCR. This support will also include activities aimed at strengthening\nwomen’s technical skills and will be implemented in cooperation with organizations addressing other\nconstraints (in particular food nutrition and hygiene) of vulnerable households. The training would\ninclude awareness-raising of risks from climate change and benefits of climate change adaptation.\n17. **_Component 2: Poultry value chain input support for improved food and nutrition security and_**\n**_increased incomes (US$ 1.0 million):_** This activity would finance the distribution of egg-laying chicken\nand feed and provision of agro-technical advice to refugees in order to tackle food and nutrition\ninsecurity and contribute to improved livelihoods. The activity would also support a limited value\nchain development, providing opportunities for the poultry producers to sell the surplus products\nthat the family does not require for consumption.\n18. **_Component 3: Supervision and monitoring_** **_(US$ 0.5 million):_** This component would finance overall\nproject management, monitoring and implementation, including the following aspects: (a) project\nmanagement and coordination among different actors and stakeholders; (b) monitoring and\nevaluation, including periodic beneficiary satisfaction survey (at inception, mid-term and project\nconclusion) including ethnicity and gender disaggregated data; (c) project environmental and social\nsafeguards; (d) project fiduciary administration, internal controls and audit, and (e) citizen\nengagement mechanism **.**\n\n\n**Environmental and Social Standards Relevance**\n\n\n**E. Relevant Standards**\n\n\nSep 02, 2021 Page 8 of 10", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000036:7:1:0", "start": 1329, "end": 1369, "surface": "periodic beneficiary satisfaction survey", "probe_tag": "confusion", "probe_score": 0.0517, "luna_label": 0, "luna_reason": "Planned periodic survey is project monitoring data to be generated."}, {"key": "jdc_operational:000036:7:1:1", "start": 1428, "end": 1467, "surface": "ethnicity and gender disaggregated data", "probe_tag": "drop", "probe_score": 0.0479, "luna_label": 0, "luna_reason": "Data will be collected through planned periodic beneficiary satisfaction surveys."}]}, {"key": "aj-122", "text": "### 1.8 11\n\n\n\nCurrent account balance . **.2**\n\n\n\nFinancing items (net)\nChanges in net reserves .. .\n_Memo:_\nReserves including gold _(US$ millions)_\nConversion rate _(DEC, locaW/US$)_ 177.7 177.7 177.7\n\n\n**EXTERNAL DEBT and RESOURCE FLOWS**\n\n**1979** **1989** **1998** **1999**\n_(US$ millions)_ **Compositlon of total debt, 1998 (USS milIlona)**\nTotal debt outstanding and disbursed 26 **179** 288\nIBRD 0 0 0\nIDA 0 26 50 49 G 15 B\n\nTotal debt service 2 15 6\nIBRD 0 0 0 C: 9\nIDA 0 0 1 1\n\nComposition of net resource flows E: 119\nOfficial grants 5 32 48\nOfficial creditors -1 3 2\nPrivate creditors 0 -1 0\nForeign direct investment 0 0 6 D: **95**\nPortfolio equity 0 0 0\n\nWorld Bank program\n\nCommitments 0 9 3 A  - IBRD E - Bilateral\nDisbursements 0 2 2 1 B - IDA D - Other rrultilateral F **-** Private\nPrincipal repayments 0 0 0 0 C- IMF G - Short-term\nNet flows 0 2 2 1\nInterest payments 0 0 0 0\nNet transfers 0 2 1 1\n\n\nNote: This table was produced from the Development Economics central database. 9/13/00", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000062:63:1:0", "start": 960, "end": 998, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.946, "luna_label": 1, "luna_reason": "Named database cited as the source of the presented external debt table."}]}, {"key": "aj-123", "text": "<br>(NTPS)|30,000 K‐12 teachers trained and<br>certified<br>|30,000 K‐12 teachers trained and<br>certified<br>||\n|**Baseline**|**UoM: Yes/No, Number**|No|No|0|0|0||\n|**Allocation**||6|6|7|15 (7,8)|15 (7,8)|**34**|\n\n\n33", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000127:40:4:0", "start": 5, "end": 9, "surface": "NTPS", "probe_tag": "keep", "probe_score": 0.9172, "luna_label": 0, "luna_reason": "Standalone table cell fragment, not an identifiable data resource or use."}]}, {"key": "aj-124", "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:000090:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1, "luna_reason": "Survey data supports concrete enrollment-rate comparisons across expenditure groups."}, {"key": "refugee_pads:000090:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Survey data supports enrollment comparisons and identifies an education-level reporting limitation."}, {"key": "refugee_pads:000090:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1, "luna_reason": "Household survey data support the finding that parents withdraw girls from school."}]}, {"key": "aj-125", "text": "### 1.8 11\n\n\n\nCurrent account balance . **.2**\n\n\n\nFinancing items (net)\nChanges in net reserves .. .\n_Memo:_\nReserves including gold _(US$ millions)_\nConversion rate _(DEC, locaW/US$)_ 177.7 177.7 177.7\n\n\n**EXTERNAL DEBT and RESOURCE FLOWS**\n\n**1979** **1989** **1998** **1999**\n_(US$ millions)_ **Compositlon of total debt, 1998 (USS milIlona)**\nTotal debt outstanding and disbursed 26 **179** 288\nIBRD 0 0 0\nIDA 0 26 50 49 G 15 B\n\nTotal debt service 2 15 6\nIBRD 0 0 0 C: 9\nIDA 0 0 1 1\n\nComposition of net resource flows E: 119\nOfficial grants 5 32 48\nOfficial creditors -1 3 2\nPrivate creditors 0 -1 0\nForeign direct investment 0 0 6 D: **95**\nPortfolio equity 0 0 0\n\nWorld Bank program\n\nCommitments 0 9 3 A  - IBRD E - Bilateral\nDisbursements 0 2 2 1 B - IDA D - Other rrultilateral F **-** Private\nPrincipal repayments 0 0 0 0 C- IMF G - Short-term\nNet flows 0 2 2 1\nInterest payments 0 0 0 0\nNet transfers 0 2 1 1\n\n\nNote: This table was produced from the Development Economics central database. 9/13/00", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000041:63:1:0", "start": 960, "end": 998, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.946, "luna_label": 1, "luna_reason": "Named database cited as the source for the presented external debt table."}]}, {"key": "aj-126", "text": "**The World Bank**\nDjibouti Skills Development for Employment Project (P175483)\n\n\nthrough to the end of the century, although the timing of precipitation is likely to impact both livestock grazing\nperiods tended to by nomadic groups and for the very small part of the country that supports agriculture. The\ncountry is highly vulnerable to droughts, floods, heat waves, and earthquakes. Its strategic coastline also increases\nexposure to risks posed by sea level rise, and thereby potentially affecting the country's strategic port assets and\nits ability to use tourism as means of economic growth. As one of the most water scarce countries in the world,\nthese climate related events would continue to play havoc with human and livestock populations in the coming\ncentury. Further studies are needed to better understand some of these anticipated patterns in Djibouti to\nimprove government’s planning and preparation. For example, recent studies using data from 14 weather stations\ncovering the period 1946-2017, illustrates spatial and temporal variability and specifically identifies two spatially\ncoherent regions - eastern coast and western inland areas of the country, and across January-February (JF); MarchMay (MAM); June-September (JJAS); and October-December (OND) over the year. The study also notes significant\npositive correlation in rainfall variability with Indian Ocean Dipole (IOD) and OND period, and negative correlation\nbetween JJAS and El Niño Southern Oscillations (ENSO).  The Project will emphasize green investments (e.g., water\nharvesting systems, renewable energy options for Technical and Vocational Education Training (TVET) institutions\nwhere possible, etc.) and incorporate foundational classes on the country’s vulnerability to climate change as a\ncore course for all students and trainees in long term (greater than a year old) skills development programs.\n\n\n5. **Djibouti is a lower-middle income economy with a nominal gross domestic product (GDP) equivalent to**\n**", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000195:13:0:0", "start": 951, "end": 980, "surface": "data from 14 weather stations", "probe_tag": "keep", "probe_score": 0.9093, "luna_label": 1, "luna_reason": "Existing station data support cited findings on rainfall variability and climate patterns."}]}, {"key": "aj-127", "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:000072:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1, "luna_reason": "Survey data supports concrete enrollment-rate comparisons across expenditure quintiles."}, {"key": "refugee_pads:000072:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Existing survey data supports enrollment-gap analysis and identifies disaggregation limits."}]}, {"key": "aj-128", "text": "**_0_** Land Task Force\n\n\n\n**_0_** Continuous Social Impact Assessment\n\n\n\n**_0_** Technical Audit\n\n\n\n**_0_** Communications Campaign\nSkills Training\n\n\n\n**_0_** Environmental Audit\n\n\n\nHousing Assessment Survey. This has already been discussed earlier in the Annex.\n\n\nLand Task Force. Land ownership i s not a serious issue in most instances. IDP land ownership has\nshown a significant increase over the past few months. While the U N H C R supervised survey in April,\n2006 recorded that **55.5%** - f the IDP families (i.e. 8,587 families) had legal title to their land, the\nproportion with title to land has increased since then. In addition to common land documentation such as\nDeeds, Permits, Grants, and Lease, documents such as Promissory Notes from the Land Commissioner in\nthe case o f government lands and Notarized Allotment Letters from the Lawyers supporting the\nregistration o f private lands were found. The Land Task Force (LTF) established by the District\nAdministration will examine the legality o f these documents and where possible regularize them.\n\n\nN o n contestable title to land i s a pre-condition for housing assistance. The families in the camps selected\n\nfor Phase 1 either reside on private land or on Land Reform Commission (LRC) land. In camps on\nprivately-owned land, families bought such land on a collective basis and distributed it amongst\nthemselves. They consequently have title. Government would need to ensure that these are non\ncontestable deeds. The Government would need to legally transfer title to land in those instances o f IDPs\nliving on L R C land.\n\n\nThe Ministry o f Resettlement had taken action to address land issues through the creation o f a LTF\nconsisting o f the District Secretary, the Commissioner o f the Secretariat for Northern Displaced Muslims,\na Surveyor (District Secretariat, Put", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000023:41:0:1", "start": 429, "end": 456, "surface": "U N H C R supervised survey", "probe_tag": "confusion", "probe_score": 0.7721, "luna_label": 1, "luna_reason": "Existing UNHCR survey provides the cited land-title finding."}]}, {"key": "aj-129", "text": " limited domestic contracting capacity to conduct\nprojects of this complexity and scope; (b) NITA-U staff and partner agencies not familiar with Procurement\nRegulations (July 2016, revised January 2020); (c) underestimation of the cost of contracts; (d) NITA-U has a\nvacancy rate of 58 percent, resulting in gaps in technical staff to support the project; (e) inadequate storage space\nfor procurement records; (f) delays in commencement of procurement processing due to late preparation of E&S\nsafeguards studies; (g) gaps in the bidding documents leading to many inquiries from bidders prolonging the\nbidding process; (h) heavy workload on Procurement Unit resulting in delays in procurement processing; (i)\ninadequate stakeholder engagements in project area resulting in delays in contract execution; (j) delayed site\nhandover to contractors for construction due to delays in implementation of the Resettlement Action Plan (RAP);\n(k) bid tampering during project implementation; (l) forgery of documentation and misrepresentation of\nqualification requirements in the bids; and (m) delays in implementation at different stages of the procurement\ncycle.\n\n**84.** **Preliminary risk mitigation measures:** These will include, (a) wider dissemination of bidding opportunities to\nreach international and regional markets to elicit participation from the capable providers; (b) trainings for staff\non World Bank Procurement Regulations; (c) prepare and disseminate the Procurement Manual to all project\nimplementation staff; (d) conduct a market survey before procurement processing and update the cost estimate\nin the Procurement Plan if needed; (e) hiring of individual consultants to fill staffing gaps to ensure sufficient inhouse technical capacity (in skills and numbers); (f) purchase lockable cabinets and lockable cupboards for storage\nof both active and archived records; (g) E&S safeguards studies to commence timely; (h) an internal Quality\nAssurance Team shall review all UDAP-GovNet bidding documents to", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000075:43:1:0", "start": 1535, "end": 1548, "surface": "market survey", "probe_tag": "confusion", "probe_score": 0.0548, "luna_label": 0, "luna_reason": "Planned survey to be conducted before procurement, so data does not yet exist."}]}, {"key": "aj-130", "text": " and implementation of the MIS is targeted for the first\nyear. Relevant SWM data such as waste tonnage handled, costs, etc., is currently tracked by\nservice providers; however, the MIS will facilitate the consolidation and reporting of this data\nand allow JSC-H&B to more effectively track progress and performance.\n\n### **B3. Project Design**\n\n\n**_Targeting_**\n\nIn the absence of data on household income, the Project uses geographical targeting at the level of the\ngovernorates where poverty level is estimated at 18.3% on average, 32.5% in Hebron and 21.3% in\nBethlehem (2010) <sup>9</sup> . Both governorates also have the highest unemployment rates in the West Bank with\naverages of 22.8% and 22.4%, respectively <sup>10</sup> . The vulnerability of these groups is exacerbated by mobility\nrestrictions and poor market access.\n\n**_Output-based subsidy payment_**\n\n\n8 These closures are part of the World Bank project and including them in the OBA targets will further incentivize JSC-H&B to achieve closures\naccording to the planned schedule. In addition, these closures are necessary to meet OBA targets for Indicator (3) Waste Managed.\n9 PCBS: West Bank Southern Governorates Statistical Yearbook, 2011\n10 PCSBS Press Release on Labour Force Survey Results, Labour Force Survey (January-March, 2011) Round (Q1/2011).\n\n\n18", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000056:17:1:0", "start": 72, "end": 80, "surface": "SWM data", "probe_tag": "confusion", "probe_score": 0.3388, "luna_label": 0, "luna_reason": "Existing data is merely tracked; no analyzed finding or substantive use is shown."}, {"key": "refugee_pads:000056:17:1:1", "start": 1151, "end": 1203, "surface": "West Bank Southern Governorates Statistical Yearbook", "probe_tag": "keep", "probe_score": 0.9045, "luna_label": 1, "luna_reason": "Named statistical yearbook cited as source for governorate poverty estimates."}, {"key": "refugee_pads:000056:17:1:2", "start": 1236, "end": 1255, "surface": "Labour Force Survey", "probe_tag": "keep", "probe_score": 0.9123, "luna_label": 1, "luna_reason": "Named survey source supports cited unemployment-rate findings."}]}, {"key": "aj-131", "text": ".\n\n19 The dependent variable (volume of subsidized four) was regressed on (1) the number of UNHCR-registered\nSyrian refugees, (2) the gap between the flour import price and the mills price, and (3) lags of the dependent\nvariable. Monthly data from January 2007 to May 2013 were used.\n\n20 This smuggling activity is also likely capturing another impact of the Syrian conflict on Jordan. As supplies,\nbakeries, and logistics in Syria are being disrupted by the conflict, potential shortages and rising prices in Syria\nincrease the incentive to get bread supplies from Jordan. The larger the price gap between Jordan’s subsidized price\nand the “international” price, the stronger the incentive to supply in Jordan.\n\n\n39", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000064:38:2:0", "start": 230, "end": 242, "surface": "Monthly data", "probe_tag": "confusion", "probe_score": 0.8885, "luna_label": 1, "luna_reason": "Existing monthly data were used in regression analysis."}]}, {"key": "aj-132", "text": "**The World Bank**\nCash for Jobs Project (P175327)\n\n\n`o` Of which female\n\n`o` Of which refugees\n\n`o` Of which host communities\n**B. Project Components**\n\n\n**Component 1: Scaling up safety nets to the national territory (US$82 million equivalent)**\n\n44. The objective of this component is to scale up safety nets interventions initiated under the Merankabandi project\nat national level. It will also add an element of shock response by financing emergency cash transfers in selected areas\ntargeting households that have been affected by the impact of COVID-19 and climatic shocks such as floods.\n\n**Sub-component 1.1: Cash transfers for COVID-19 emergency response (US$5 million)**\n\n45. This sub-component will aim to support households economically affected by COVID-19 in six urban centers of the\ncountry, in particular Bujumbura, Gitega, Ngozi, Rumonge, Kayanza and Gatumba. A total of 25,000 households will\nbenefit from this activity and will receive two cash transfers in a period of four months (one transfer every two months)\nthat will allow them to recover economically from the impact of COVID-19 and other shocks like urban flooding. This subcomponent will target households from the informal sector as these are likely to be more affected by the impacts of the\npandemic. The amount of each cash transfer will be equivalent to US$50 (US$100 in total).\n\n46. Different channels and instruments will be used for beneficiary targeting. As the impact of COVID-19 is not only\nfelt on the poorest populations, the application of a Proxy Means Test (PMT) as for the regular cash transfers is not\napplicable. Beneficiaries will initially be targeted through existing data bases from different groups of the informal sector.\nSocio-economic data will be collected from all potential beneficiaries to determine their eligibility", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000130:24:0:0", "start": 1725, "end": 1744, "surface": "Socio-economic data", "probe_tag": "confusion", "probe_score": 0.6867, "luna_label": 0, "luna_reason": "Data will be collected prospectively to determine beneficiary eligibility."}]}, {"key": "aj-133", "text": " Resettlement. The latter committee would have the final word.\n\n\n15. The PPU completed a Housing Assessment Survey in each refugee camp to revalidate the\nenumeration o f permanent, partly-completed and temporary houses in the U N H C R supervised survey,\nassess the extent o f construction needed for partly-completed houses and review the extent o f new title to\nland since the U N H C R supervised census. This would enable it to plan the roll out o f the cash grants.\nThe housing component would be phased over four years with 1,463 houses targeted for\nconstructiodcompletion in 2007; 2,201 houses in 2008; 2,031 in 2009; and 2,190 houses in 2010.\n\n\n16. The PPU would facilitate where requested the procurement o f construction material in bulk to\nmitigate the risk - f price escalation, supply constraints and unsustainable resource extraction.\nBeneficiaries would retain the prerogative to independently procure their own materials if they choose to.\nThe District Secretariat would ensure that Government environmental guidelines are adhered to in the\nextraction o f resources.\n\n\n**Component Two:** Water, Sanitation, Environment Mitigation, and Settlement Plans (US$ 15.9 million)\n\n\n17. Background: The Puttalam district i s situated on the North West coast and dry zone o f Sri Lanka.\nThe annual rainfall o f 15 to 30 centimeters i s largely limited to the months o f November and December.\nThe main source o f drinking water i s situated in a series o f deep and shallow aquifers. The quality of\nwater in these aquifers ranges widely depending on location. Studies indicate that ground water i s often\ncontaminated with nitrates in areas where intensive agriculture takes place. The excessive use o f\ngroundwater for agriculture and prawn farms has led to a saline intrusion in other areas. The inappropriate\ndesign o f latrines leads to the bacteriological contamination o f ground water in several refugee camps.\nThe average depth o f most shallow well", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000023:9:1:0", "start": 226, "end": 253, "surface": "U N H C R supervised survey", "probe_tag": "confusion", "probe_score": 0.5789, "luna_label": 1, "luna_reason": "Existing UNHCR survey data revalidated house enumeration for housing planning."}, {"key": "refugee_pads:000023:9:1:1", "start": 379, "end": 406, "surface": "U N H C R supervised census", "probe_tag": "confusion", "probe_score": 0.7787, "luna_label": 1, "luna_reason": "Existing UNHCR census informs land-title review and housing grant planning."}]}, {"key": "aj-134", "text": " training or access to mentoring schemes. The project will leverage the existing\ncollaboration framework with the National Advanced School of Public Works in Yaoundé, under the PDST Project,\nto continue the partnership with the MINTP to support activities aiming at promoting women’s entry in the\nTransport sector. Thus, the project will continue to support efforts to increase the number of women in STEM <sup>42</sup>,\nthen facilitate the transition from universities to work in the Transport sector in the long term.\n\n\n**(e) The Douala–N’Djamena Intra–Interregional Transport Corridor (1,842 km)**\n\n26. **The Far North of Cameroon is a trade crossroads; however, cross-border trade is adversely impacted by the**\n\n\n39 https://www.roadsafetyfacility.org/country/cameroon\n40 Global Health Observatory data repository accessed on February 1, 2022. http://apps.who.int/gho/data/node.main.A997?lang=en\n41 2021 data from the International Labour Organization:\n[https://data.worldbank.org/indicator/SL.TLF.ACTI.MA.ZS?locations=CM&name_desc=false](https://data.worldbank.org/indicator/SL.TLF.ACTI.MA.ZS?locations=CM&name_desc=false)\n42 Science, Technology, Engineering, and Mathematics.\n\n\nPage 21 of 82", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000003:20:2:0", "start": 903, "end": 955, "surface": "2021 data from the International Labour Organization", "probe_tag": "confusion", "probe_score": 0.7485, "luna_label": 1, "luna_reason": "Named 2021 ILO data citation underlying the referenced indicator."}]}, {"key": "aj-135", "text": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n\n**Monitoring & Evaluation Plan: PDO Indicators by PDO Outcomes**\n\n|An inclusive digital ID ecosystem is established|Col2|\n|---|---|\n|**Number of people in Ethiopia who have received a Fayda ID (Number)**|**Number of people in Ethiopia who have received a Fayda ID (Number)**|\n|Description|A number of unique individuals registered in the Fayda system and who have been assigned a Fayda number|\n|Frequency|Biannual|\n|Data source|Fayda registration data|\n|Methodology for Data Collection|Fayda data analytics platform|\n|Responsibility for Data Collection|NIDP|\n|**Percentage of whom are women and girls (Number)**|**Percentage of whom are women and girls (Number)**|\n|Description|Percentage of women and girls among the total number of unique individuals registered in the Fayda system<br>and who have been assigned a Fayda number|\n|Frequency|Biannual|\n|Data source|Fayda registration data|\n|Methodology for Data Collection|Fayda data analytics platform|\n|Responsibility for Data Collection|NIDP|\n|**Number of whom are individuals living in refugee host communities (Number)**|**Number of whom are individuals living in refugee host communities (Number)**|\n|Description|Number of individuals who have been registered in areas tagged as a refugee host community|\n|Frequency|Biannual|\n|Data source|Fayda registration data|\n|Methodology for Data Collection|Fayda data analytics platform|\n|Responsibility for Data Collection|NIDP|\n|**Number of whom are refugees (Number)**|**Number of whom", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000005:39:0:0", "start": 514, "end": 537, "surface": "Fayda registration data", "probe_tag": "confusion", "probe_score": 0.1375, "luna_label": 1, "luna_reason": "Named registration data source for Fayda ID indicator measurement."}]}, {"key": "aj-136", "text": " role in facilitating the sustainable return of IDPs.\n\n10. **The IDP survey and lessons learned paper on livelihoods described above have informed**\n**the design of the proposed project by summarizing the current living conditions of IDPs as well**\n**as lessons from implementation of previous livelihood programs** . Though both studies are in\nthe process of being finalized, they have provided valuable inputs to the design of this project.\nThe study of lessons from other livelihoods and job training programs revealed the need for close\nsupport for training participants to ensure the sustainability of their achievements. This has\nresulted in the incorporation of mentors into the project design from the time of project launch\nthrough completion and an emphasis on community-based support to address the unique\ncontext of each IDP settlement. The household survey found that 22 percent of household\nmembers are unemployed and 30 percent of respondents are looking for work. There remains a\nreliance on state support with 90 percent of respondents receiving an IDP allowance. To address\ntheir income generation needs, respondents identified various skills they would like to acquire\nwith males wanting to have skills in the agriculture/fishery, automotive and land transport, and\nconstruction sectors while women preferred garments/sewing, health care and community\ndevelopment. While the data collected through the survey on job and skills provides useful\nbenchmarking information, more localized labor market surveys will need to be undertaken to\nidentify targeted opportunities in the communities where IDPs are living to support livelihoods\nthat provide greater incomes over sustained periods.\n\n\nPage 9 of 34", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000007:11:1:0", "start": 853, "end": 869, "surface": "household survey", "probe_tag": "keep", "probe_score": 0.9298, "luna_label": 1, "luna_reason": "Survey is attributed to unemployment and job-seeking findings."}, {"key": "refugee_pads:000007:11:1:1", "start": 1421, "end": 1445, "surface": "survey on job and skills", "probe_tag": "confusion", "probe_score": 0.7139, "luna_label": 1, "luna_reason": "Existing survey data provide benchmarking information for project design."}]}, {"key": "aj-137", "text": "**The World Bank**\nUganda: Roads and Bridges in the Refugee Hosting Districts Project (P171339)\n\n\npurpose, stakeholder and community consultations, accompanied by a market system assessment will be\nconducted to inform the development of a Gender Action Plan that will define implementation. Based on\nthe experience of DRDIP, investments can include, among others, trainings on negotiations, financial skills\nand business plans development for women in refugee camps and host communities, and engagement of\npartners through gender dialogue groups. Under Component 3, the Project will ensure that Road Accident\nDatabase Management System is disaggregated by gender, as well as by refugees/host community\nmembers.\n\n64. **Citizen Engagement.** The Project will implement a selection of citizen engagement interventions to ensure\nactive and focused dialogue between citizens, refugees, and UNRA. During the preparation of the\nenvironmental and social impact assessment and the detailed design, citizens and refugees were engaged\nand feedback from these consultations have influenced the project design. These consultations targeted\ncurrent and future road users that included (i) roadside vendors – particularly women, refugees, and other\nmembers of vulnerable groups, (ii) boda-boda (motorcycle taxi) operators, Traffic Police, (iii) taxi operators,\n(iv) general road users, including pedestrians, and (v) all relevant stakeholders. These groups were further\nengaged during the UNRA led High Level Mission of February 19-20, 2020 that was meant to carry out a\nground truthing exercise, and included teams from UNRA, the World Bank, OPM, UNHCR, and relevant Local\nGovernment Representatives. The Project will undertake regular citizens/refugee/road user satisfaction\nsurveys to gauge the perception of road users and communities on the performance of the contractors and\nthe project overall. The Project will also have a grievance redress and beneficiary feedback mechanism. To\nenhance transparency and accountability, the Project will use and further strengthen the earlier initiatives\nin UNRA including (a) procedures", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000146:32:0:1", "start": 1722, "end": 1769, "surface": "citizens/refugee/road user satisfaction\nsurveys", "probe_tag": "confusion", "probe_score": 0.3713, "luna_label": 0, "luna_reason": "Project will undertake these future satisfaction surveys."}]}, {"key": "aj-138", "text": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\n60. **Subcomponent 4A: Project management support for high-quality implementation.** This subcomponent will\nfinance the Project Implementation Teams (PITs) at the MGLSD and the PSFU. it will finance capacity building activities,\nincluding (a) of the national, district, subcounty, parish, refugee settlement stakeholders and implementation support\nteams. It will finance the development of key partnerships including of quarterly review meetings for all stakeholders\ninvolved in the project at the regional and district levels. The project as part of its Monitoring and Evaluation (M&E)\nactivities will design and develop an MIS that collects and stores detailed data from project applicants during the\nregistration process (i.e.,, before beneficiaries have accessed any project-financed activities). As indicated in component\n1, the MIS is expected to assign a unique identifier to each registered applicant (GROW_ID) that will be shared with the\napplicant. The MIS will collect information from applicants during registration, such as (a) key contact information,\ngender, age, marital status, education status, refugee status, employment; (b) business level outcomes such as business\npartners, business age, monthly profits, number of employees; and (c) access to other programs and loans. The MIS\ndatabase will be updated as program applicants make use of specific components. The GROW_ID can be entered to\nupdate the database with any additional information on services received to ensure the applicant is only registered once.\nFor example, dates of business trainings, service provider, or amount of funds will all be entered into the MIS. This\ndatabase will help support operations through feedback loops as it can track who is accessing which services in real time.\nThe MIS will also be important in being able to establish a sample of study participants to draw on for an impact\nevaluation", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000088:30:0:0", "start": 1413, "end": 1425, "surface": "MIS\ndatabase", "probe_tag": "confusion", "probe_score": 0.5232, "luna_label": 0, "luna_reason": "Project MIS database will be updated as applicants use program components."}]}, {"key": "aj-139", "text": " (WHO; US$2.50 million: US$0.93 million**\n**equivalent IDA [including US$0.63 million WHR] and US$1.57 million Trust Funds [US$0.17 million SDTF and**\n**US$1.40 million MDTF]).** This subcomponent will focus on developing systems and procedures for the national\nHMIS, with an emphasis on supporting the collection of routine data through DHIS2, to standardize data collection,\nentry and cleaning, as well as instituting data quality improvement practices. This will enhance targeting and data\ntracking for refugees and provide regularly updated information to understand the evolving needs on the ground\nthat will aid further in the decision-making process. The subcomponent will: (a) finance procurement of\ninformation communication technology equipment at the national level and train staff on data entry and use; (b)\ntrain trainers to develop health facility staff data entry, management, and use capacity; (c) create interoperability\nand integration between data systems and ensure data sharing, storage and backup; (d) develop, print, and\ndisseminate Standard Operating Procedures for HMIS data entry, cleaning, quality improvement, and use at all\nlevels; (e) conduct data review meetings and generate data use tools; (f) establish and operate the National and\nState level HMIS and Monitoring and Evaluation (M&E) Technical Working Groups; (g) conduct data quality\nimprovement activities at the facility and national level; (h) operationalize a national and state level research\ncommittee, building on existing structure; (i) conduct an annual health sector review meeting; and (j) maintain\nand institutionalize the Health Service Functionality (HSF) Database.\n\n\n39. **Subcomponent 2.5: Health Sector Stewardship and Financing (WHO implemented; US$2.00 million: US$0.77**\n**million equivalent", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000006:22:1:0", "start": 338, "end": 343, "surface": "DHIS2", "probe_tag": "confusion", "probe_score": 0.3726, "luna_label": 0, "luna_reason": "DHIS2 is cited for supporting future routine data collection, not using existing data."}, {"key": "refugee_pads:000006:22:1:1", "start": 1621, "end": 1664, "surface": "Health Service Functionality (HSF) Database", "probe_tag": "confusion", "probe_score": 0.5968, "luna_label": 0, "luna_reason": "Planned maintenance and institutionalization of a database, without existing data use."}]}, {"key": "aj-140", "text": "\nprovincial (four provincial focal points) and commune-level (16 commune focal points) teams\nresponsible for implementation in the field. Payment activities will be contracted to payment\nagencies while the behavior change activities will be contracted to NGOs. Relevant government\nentities (Ministry of Health (MSPLS), Ministry of Education, and Ministry of Communal\nDevelopment) are engaged at the national level (at the strategic and technical levels through the\nCNPS, its technical committee and the relevant thematic groups), the provincial level (through the\nCPPS) and at the local level where present. In particular, the focus of the Health System Support\nproject ((KIRA, P156012) on community health and nutrition provides for potential synergies. Deconcentrated administrative structures will also support the program’s implementation in the\n_collines_ .\n\n9. **The program will be implemented in phases** . The program will prioritize the poorest\nareas (with a combination of monetary poverty rate and chronic malnutrition at the province level\nand extreme poverty rate at the commune level). The project will support a first phase of the\nprogram to establish transparent and rule-based processes and allow for the gradual fine-tuning of\nthese processes and the instruments for program operation. For example, the involvement of\ncommunities in the targeting may evolve as better household survey data becomes available; cash\npayments may evolve, from cash to electronic or mobile payments; the enforcement of\nconditionalities may evolve with the availability of services and administrative capacity. The\nregistry will start in 16 communes of the four provinces with the largest rates of monetary poverty\nand chronic malnutrition.\n\n\n**_Sub-component 1.1. Cash Transfers (US$19.5 million equivalent)_**\n\n\n10. **Payment amount and schedule.** The level of transfers will be BIF 20,000 (US$ 12\nequivalent) per household and per month for 30 months. This corresponds to approximately 19\n\n\n38", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000157:48:1:0", "start": 1387, "end": 1408, "surface": "household survey data", "probe_tag": "confusion", "probe_score": 0.7796, "luna_label": 0, "luna_reason": "Future availability is anticipated, not existing data actually used."}]}, {"key": "aj-141", "text": "br>number of people who<br>were in the target groups<br>and were fully vaccinated<br>with 2 doses, and the<br>numerator will be the<br>number of women<br>vaccinated with 2 doses in<br>the target groups.|3 months<br>|NDVP, digital<br>vaccination<br>registry,<br>national<br>paper-based<br>vaccination<br>registry<br>|Administrative data<br>|PMU/MOHE<br>|\n|Number of project-supported COVID-19<br>vaccinations sites with adequate health<br>care waste management for vaccination|The project will invest in<br>providing adequate waste<br>management equipment at<br>the facility level.<br>Monitoring of the|3 months<br>|TPMA reports<br>|Survey by TPMA<br>|MOHE/TPMA<br>|\n\n\n\nPage 42 of 54", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000058:47:1:0", "start": 632, "end": 646, "surface": "Survey by TPMA", "probe_tag": "confusion", "probe_score": 0.1327, "luna_label": 0, "luna_reason": "Standalone monitoring-table entry, not an independently used data source."}]}, {"key": "aj-142", "text": "**233.** In a series of micro-simulations on existing SWF beneficiary and survey data from **2008,** the\nPMT targeting accuracy in increasing the coverage of the poor was compared to the previous Score Card\ntargeting method used by SWF. The SWF **2008** survey included income level information as well as\nPMT/Score Card indicator data, allowing comparison of actual income reported with the PMT and Score\nCard scores for each applicant.\n\n\nThe simulations showed that using the Score Card targeting method would extend coverage to\nonly **8.4** percent of the population and reach only 10.8 percent of the lowest HBS decile (Le., the\npoorest 10 percent).\nBy applying the PMT weights, the coverage for Group A remained approximately the same **(9**\npercent), but reach was extended to cover **26.9** percent of the poorest 10 percent.\nBy including Group A&B data in the simulation, **35** percent of the Yemeni population would be\ncovered and the program would reach _77.5_ percent of the poorest 10 percent.\nIn considering budget allocation to the extreme poor, the Score Card method was found to reach\napproximately 19 percent of the poorest, while the PMT Group A method reached approximately\n**50** percent of the poorest.\n\n\nThese results suggest higher targeting errors (inclusion and exclusion errors) under the Score\nCard targeting method, leading to the recommendation that SWF use the PMT method to increase the\ntargeting accuracy of the program to include poor and vulnerable households.\n\n**_Process of beneficiary selection and enrollment_** - **_reaching out to the poor_**\n\n\n**235.** Reaching the ultra-poor is a challenge: they are often widely dispersed in small settlements, have\nno means of communication with authorities and lack basic skills (i-e., literacy", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000080:78:0:0", "start": 54, "end": 85, "surface": "SWF beneficiary and survey data", "probe_tag": "confusion", "probe_score": 0.842, "luna_label": 1, "luna_reason": "Existing SWF survey and beneficiary data support targeting simulations and findings."}, {"key": "refugee_pads:000080:78:0:1", "start": 241, "end": 260, "surface": "SWF **2008** survey", "probe_tag": "keep", "probe_score": 0.938, "luna_label": 1, "luna_reason": "Existing survey data informed targeting simulations and accuracy comparisons."}]}, {"key": "aj-143", "text": " each year. It will include the\nannual social audit and impact assessment aimed at highlighting implementation weaknesses, social\nissues and grievances and provide feed back with specific recommendations for actions to district and\nprovincial level authorities. Some o f the specific objectives o f are as follows:\n\n\n\n**_0_**\n\n\n**_0_**\n\n\n**_0_**\n\n\n**_0_**\n\n\n\nRecord public opinion, concems and mevances and present them in formal decision makmg forums\nsuch as the Provincial Program Coordinating Committee and NEHRU.\nIdentify social risk mitigation measures.\nGuide and conduct independent social and community audit to mitigate any possible negative\neffects and enhance positive effects.\nReview social safeguard issues and prepare checklists to ensure social soundness to minimize social\nexclusion.\n\n\n\nThe Village Social Profile (as detailed in Annex 10) to be collated by NEHRU will provide baseline\nvillage data for the continuous social impact assessment.\n\n\n_Follow-up Environmental Impact Assessment: US$O.1 million_\n\n\nThis will ensure proper management o f environmental issues explained in previous sections. It will not\nonly identify measures to bring in environmental sustainability to the program but also identify\nopportunities to gain economic benefits through the use o f local raw material without causing\ndetrimental effects on the environment. This exercise will be out-sourced to a private consultancy\n\n\n44", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000115:48:1:0", "start": 806, "end": 828, "surface": "Village Social Profile", "probe_tag": "confusion", "probe_score": 0.1068, "luna_label": 0, "luna_reason": "Profile will be collated by NEHRU to provide future baseline data."}]}, {"key": "aj-144", "text": "Annex 6\nPage 5 of 6\n\n\n\n**Table B: Thresholds for Procurement Methods and Prior Review 1**\n\n\n\n**Expenditure Category** **Contract Value** **Contracts Subject to**\n**Threshold** **Procurement** **Prior Review**\n(US$ thousands) Method (US$ millions)\n1. **Works** - US$50,000 NCB All ICB if any.\n< US$50,000 Simplified NCB NCB contracts above US$150,000\nFirst 5 contracts regardless of\nvalue; first 3 contracts for each\n\n\n\nyear starting January 1.\n**2. Goods** - US$100,000 ICB All ICB.\n< US$100,000 NCB NCB contracts above US$70,000\n< US$50,000 IS or NS where there are at First 5 contracts regardless of\n\n\n\nleast 3 capable national value; first 3 contracts for each\nsuppliers. year starting January 1.\n<US$10,000 DC\n**3. Services** - US$50,000 QCBS Contracts above US$200,000\n(Firms) would be advertised in the UNDB.\n\n              - US$25,000 Contracts for firms above\n(Indiv.) CQ US$50,000, and for individuals\nSS above US$25,000.\n< US$25,000 First 5 contracts regardless of\n=< US$15,000 value; first 3 contracts for each\nyear starting January 1.\nAll TORs\n_____________________________________________________________IAll Sole Source\nThresholds generally differ by country and project.", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000102:47:0:0", "start": 809, "end": 813, "surface": "UNDB", "probe_tag": "confusion", "probe_score": 0.223, "luna_label": 0, "luna_reason": "Procurement advertisement reference embedded in a table, not data use."}]}, {"key": "aj-145", "text": "br>Data aggregated by PIU|\n|**Additional annual revenue generated by supported MSMEs (Percentage) **|**Additional annual revenue generated by supported MSMEs (Percentage) **|\n|Description<br>Increase in the average annual revenue that is generated by supported MSMEs.|Description<br>Increase in the average annual revenue that is generated by supported MSMEs.|\n|Frequency<br>Annually|Frequency<br>Annually|\n|Data Source<br>Firm-level reporting by beneficiary MSMEs, verified by implementation partners|Data Source<br>Firm-level reporting by beneficiary MSMEs, verified by implementation partners|\n|Methodology for Data<br>Collection <br>Periodic surveys|Methodology for Data<br>Collection <br>Periodic surveys|\n|Responsibility for Data<br>Collection|Implementation partners, PIU|\n\n\n\n~~Page 38 of 55~~", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000004:41:3:0", "start": 3, "end": 25, "surface": "Data aggregated by PIU", "probe_tag": "confusion", "probe_score": 0.1753, "luna_label": 0, "luna_reason": "Standalone table metadata describing project data aggregation"}, {"key": "refugee_pads:000004:41:3:2", "start": 637, "end": 653, "surface": "Periodic surveys", "probe_tag": "confusion", "probe_score": 0.0859, "luna_label": 0, "luna_reason": "Table cell describing a data-collection methodology, not an existing data use."}]}, {"key": "aj-146", "text": " 4) Storage Department – supervises the transport and storage of wheat and barley between\nstorage platforms/silos, mills and feed centers. There is a stock unit in all silos for supervision. There is also a truck control system that\nmonitors the transfer of grains to all silos/storage platforms and then to the mills and feed centers. All vehicle and cargo data is monitored.\nIn addition, there is a Directorate of Trade in MoITS that oversees the procurement arrangements for grain. The Contracts Department is\nin charge of the signifying and implementation of contracts with shipping and inspection until the cargo reaches the port of Aqaba in Jordan.\nThe Department of Insurance oversees any delays, insurance, unloading operations and investigations of received quantities.\n\n\nPage 24 of 54", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000078:28:2:0", "start": 340, "end": 362, "surface": "vehicle and cargo data", "probe_tag": "drop", "probe_score": 0.0473, "luna_label": 0, "luna_reason": "Generic monitored data is named without showing substantive use or an attributed finding."}]}, {"key": "aj-147", "text": "During my review, I observed some **inade** acies in the system; however, they are not senrous\n\n\n\nenough to withhold certification. hav detailed these inadequacies in the attachment, and\nincluded an action plan for remedy thsituation that was agreed upon with the Borrower.\n\n\n\nSigned by: _0 1_\nProcurement Specialist _\"_ i **°S** **TQI** 3 _1_ _,L_\nChristi fitAccraiT&~ Date\nProcureme taff, MNSt\n\n\n\n**Part III: Physical Monitorable Indicators and Overall Assessment**\n\nI have reviewed the project's system for monitoring physical implementation progress, including\nits monitorable indicators for major outputs. In my view, the system cannot provide the\nappropriate data on physical progress (PMR-Section 2) required by IDA.\n\nDuring my review, I observed some inadequacies in the system; however, they are not serious\nenough to withhold certification. I have detailed these inadequacies in the attachment, and\nincluded an action plan for remedying the situation that was agreed upon with the Borrower.\n\nSigned by: i A\n### Task Team Leader 1/ 4 -L- UJ 2, dO\n\nQaiser Khan, MNSHD Date\n\n\n**Part** IV: **Concurrence of** LOA for Eligibility of Project for PMR-Based Disbursements\n\n\nI have conducted a reasonable review of the process followed by the Task Team in assessing the\nproject, and I concur with its recommendation that this project is not eligible for PMR-Based\nDisbursements.\n\nSigned by: C____ __a _\nFMS-LOAADO Andrina Ambrose, LOAEL Date\n\n\nT.\n\n\nThu-Ha Nguyen, LOAEL Date", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000147:55:0:0", "start": 665, "end": 690, "surface": "data on physical progress", "probe_tag": "drop", "probe_score": 0.0178, "luna_label": 0, "luna_reason": "States needed data are unavailable; no existing data are analyzed or substituted."}]}, {"key": "aj-148", "text": " a unique provider ID, a unique product ID, an appropriate<br>or common clinical code, and/or a unique service coding.|\n\n\n\n**<u><mark>DLI 7 on the digitalization of student assessment</mark></u>**\n\n\n23 Foundational registries are accessible, common, and utilized across multiple health information systems. These shall include: (1) a client registry (that\nsupports the unique identification and management of patient identities); (2) one or more terminology registries accessible as a service (that provide a\nstandardized classification or a query-able source for health information exchange, including clinical terminologies, ontologies, dictionaries, code\nsystems, and value sets); (3) a universal facility registry (that sets the unique identifier for locations where health services are provided); (4) a health\nprofessional registry (that sets the unique identifier for health workers that provide services within a country); and (5) a common product catalogue\n(that properly defines and categorizes medical products or commodities).\n24 Core standards include: (1) content standards (that dictate the structure of electronic documents and types of data they must contain by ensuring\ndata is properly organized and represented in a clear manner); (2) terminology standards (that ensure that all parties will be able to understand and\nuse it while exchanging health data); (3) transport standards (that facilitate data exchange between different systems by defining what formats,\ndocument architecture, data elements, methods, and application programming interfaces to use for achieving interoperability); and (4) security\nstandards (that establish administrative and technical rules to protect sensitive data from misuse, unauthorized access, or disclosure).\n25 Health information systems could include the national EMR platform, supply chain management information system, and surveillance systems\nmanaged by the MOH. Selected health information systems will be described in the Program Operational Manual (including the verification protocol).\n\nPage | XLVI", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000181:55:2:0", "start": 334, "end": 349, "surface": "client registry", "probe_tag": "confusion", "probe_score": 0.1075, "luna_label": 0, "luna_reason": "Names a registry system without showing its data used for analysis, targeting, or findings."}, {"key": "refugee_pads:000181:55:2:1", "start": 690, "end": 717, "surface": "universal facility registry", "probe_tag": "drop", "probe_score": 0.0316, "luna_label": 0, "luna_reason": "Registry is defined, but its data are not shown informing analysis or decisions."}]}, {"key": "aj-149", "text": " a v\nManufacturIng 5 7 3.7 4 7 5 0 / s \\\\t oo  Services 475 272 190 201\n\nPrfvate consumpffon 90 7 81 9 938 95 1 20\nGeneral govemment consumption 7 0 9 6 146 17.2 =c ~GDFDl\nImpons of goods and servrcea 39 7 23 6 33 4 373 -3GW_G_____\n\n\n_(average_ _annual growth)_ 1981-9 1991401 2000 2001 Growth ot exports and Imports (%)\n\nAgrutumre -0 6 -2 6 2 2 3 8 oo\nIndustry 0.1 -41 51 5 6 s0\nManUnacturIng 6.9 . .\nServices -5 7 -5.4 4 0 51 \nPrivate oonsumpffon -2 0 -19 10 4 100 -50\nGeneral govemment consumption -5.1 -0 2 41.3 27 9 -100\nGross domestic Investment -06 3 0 50 - EOpois -tr-ports\nImports of goods and services -2 2 -151 85 0 61 3\n\n\nNote 2001 data are pretirrinary eastliates\n'The diamonds show four Key Indicators in the country (in bold) conipared with itS income-group average It data are missing, the diantond wiUt be rrconrrlte\n\n\n-56", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000176:60:2:0", "start": 639, "end": 648, "surface": "2001 data", "probe_tag": "drop", "probe_score": 0.0128, "luna_label": 0, "luna_reason": "Bare date-only data qualifier without an identified source or attributed finding."}]}, {"key": "aj-150", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\n\n\n\n\n\n\n\n|Col1|within SNSOP project<br>locations that are satisfied<br>or very satisfied with assets<br>created through LIPW<br>divided by the total number<br>of beneficiaries and non<br>beneficiaries surveyed in<br>SNSOP project locations.<br>SNSOP project locations<br>refer to bomas or quarter<br>councils where the SNSOP<br>project is active|a quarterly<br>basis during<br>missions and<br>ISRs|System|and satisfaction surveys<br>carried out by the<br>SNSOP M&E team. In<br>addition, satisfaction<br>will be monitored by<br>the Third Party Monitor<br>(TPM)|Col6|\n|---|---|---|---|---|---|\n|Beneficiary households receiving<br>economic opportunities|Number of total beneficiary<br>households of Component 1<br>that are also receiving<br>economic opportunities<br>under Component 2, in<br>accordance with the Project<br>Operations Manual, and<br>have received at least 1<br>installment of the livelihood<br>grant.|This indicator<br>will be<br>measured at<br>a minimum<br>on a<br>quarterly<br>basis.<br> <br>|SNSOP<br>Management<br>Information<br>System (MIS)<br> <br>|Data on participation in<br>Component 2 will be<br>collected at registration<br>where based on the<br", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000153:57:0:0", "start": 500, "end": 520, "surface": "satisfaction surveys", "probe_tag": "drop", "probe_score": 0.0199, "luna_label": 0, "luna_reason": "Surveys are carried out by the project M&E team as project monitoring."}]}, {"key": "aj-151", "text": "gating the Impact of COVID-19 with a Focus on the Manufacturing and Exporting**\n**Sectors Driving Economic Transformation, including Refugee and Host Districts (IDA US$79 million and US$5**\n**million from the IDA-19 Window for Host Communities and Refugees, WHR).** The objective of this component is\nto ease liquidity constraints on MSMEs, including women led and refugee MSMEs. For the reasons discussed\nabove, the component will seek to prioritize the manufacturing and exporting sectors driving economic\ntransformation, with the vision of connecting lower income regions, like RHDs with more viable and sustainable\nmarkets. This component will operate three different windows designed to assist MSMEs to better manage the\nCOVID crisis by easing the cost of finance and the availability of liquidity by working with Participating Financial\nInstitutions (PFIs) to reach the MSMEs. PFIs will be required to provide gender-disaggregated data on eligible\nMSMEs in order to address the lack of data on women-led firms as well as data on refugee or host community\nstatus to ensure that intersectional issues of exclusion are sufficiently addressed.\n\n38. **Window 1.1** will support loans that have been restructured under the Bank of Uganda COVID-19 response\napproach, primarily in the manufacturing and exporting sectors by covering part of the added financial cost due\nto the restructuring. The window will be available to qualifying MSMEs who received an extension of the\namortization period on their loans, to reduce the MSMEs incremental cost or debt servicing liability. The cost for\nthis rebate will be shared with PFIs to ensure that PFIs participate in burden sharing through lower margins than\nusual. <sup>38</sup> The window will focus on MSMEs in the manufacturing and exports sectors, and strongly encourage the\ninclusion of women-led firms. The window can also be used by Microfinance Institutions (MFIs) or Savings and", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000073:25:1:0", "start": 916, "end": 959, "surface": "gender-disaggregated data on eligible\nMSMEs", "probe_tag": "confusion", "probe_score": 0.6084, "luna_label": 0, "luna_reason": "PFIs are required to provide this data, indicating planned future data production."}, {"key": "refugee_pads:000073:25:1:1", "start": 1027, "end": 1067, "surface": "data on refugee or host community\nstatus", "probe_tag": "drop", "probe_score": 0.019, "luna_label": 0, "luna_reason": "PFIs will provide this data as a planned project requirement."}]}, {"key": "aj-152", "text": " capita -O 2 -0 5 Lower-middle-income _group_\nESports of goods and servic\"s\n\n\n**STRUCTURE ofthe ECONOMY**\n\n**1979** **1989** **1998** **1999** **Growth rates of output and Investment ()**\n_{%I ol GOP)_\nAgriculture 3.4 _.._ **_2._**\nIndustry 21.0 O.Manufacturing 5.6 **_-2_** **_94_** _as_ _se_ _s_\nServices **75.6** 6\n\nPrivate consumption **-r.**\nGeneral government consumplion **G**\nImports of goods and services\n\n\n\n**1979-89** **1989-99** **1998** **1999**\n_(average annual orowth)_\nAgriculture\n\n\n\nIndustry\n\n\n\nManufacturing\nServices\n\n\n\nPrivate consumption\nGenerai government consumption\nGross domestic investment\nImports of goods and services\nGross national product 1 7 **1.4**\n\n\nNote. 1999 data are preliminary estimates.\nThis table was produced from the Development Economics central database.\n\nThe diamonds show four kev midicators in the country (in bold) compared with its income-group average. 11 data are missing, Ihe diamond will", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000093:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "drop", "probe_score": 0.0227, "luna_label": 0, "luna_reason": "Bare date-qualified data phrase lacks an eligible named source or attributed finding."}]}, {"key": "aj-153", "text": "2. Broader social capital building outcome: Capacity building interventions for implementing\npartners and local authorities would include conflict resolution training, and emphasize sub-project\ndesign and supervision processes which build social capital and mutual accountability between\ncommunities and the array of support organizations and institutions.\n\n\n3. Intra-community equity outcomes: To address intra-community equity issues in a more\nsystematic manner, NaCSA would revise its Operations Manual to enhance procedures for community\nneeds identification and sub-project selection. The enhanced guidelines would include, inter alia, a\nrequirement for implementing partners to fill out a Community Assessment Form. This would include\ninformation on community population (by gender and age group), basic infrastructure, main income\ngenerating activities, housing, organization, forms and levels of outside assistance, and particularly\nvulnerable groups.\n\n6.5 How will the project monitor performance in terms of social development outcomes?\n\nA Social Assessment is under preparation. It is being designed so as to ensure that the project\nresponds to social development concerns by identifying the opportunities, constraints and social risks\n\n\n\ninherent in the project and translating these ideas into practical design and implementation measures.\nThe World Bank's Social Capital Assessment Tool (SOCAT) and instruments for analyzing local\ninstitutions will be used in the Social Assessment. Subsequent iterative rounds of social analysis using\nsimple locally adapted variants of these tools would continue during the duration of the project to collect\nand analyze data on social development issues. These would include associational memberships and\ntrust, the process of creation and destruction of social capital and data on groups and networks,\n\n\n\nsubjective well-being, political engagement, sociability, community activities, cohesion and\ncommunication. The Assessment would thus provide baseline data on social issues, recommend and\n\n\n\napply social capital measurement tools, and provide a framework for continuing social analysis\nthroughout the project.\n\n\n\n7. Safeguard Policies:\n7.1 Are any of the following safeguard policies triggered by the prcect?\n\n     - '& **~** P19 ~ f _-", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000104:24:0:0", "start": 1670, "end": 1703, "surface": "data on social development issues", "probe_tag": "drop", "probe_score": 0.0226, "luna_label": 0, "luna_reason": "Project plans to collect and analyze this data during implementation."}]}, {"key": "aj-154", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000129:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0, "luna_reason": "Table-cell source listing without an analyzed finding or demonstrated data use."}, {"key": "refugee_pads:000129:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": 0.1197, "luna_label": 0, "luna_reason": "Administrative data is listed without a concrete finding or demonstrated analytical use."}, {"key": "refugee_pads:000129:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Named administrative data listed as an M&E source for program indicators."}]}, {"key": "aj-155", "text": "**The World Bank**\nDjibouti Health System Strengthening (P178033)\n\n\nacceptable.\n(iv) An IVA will be recruited to verify the results achieved under the PBF before payment.\n(v) The policies and procedures for subcomponent 1.1 “patients’ transportation”, and subcomponent 2.2\nfor supplies and maternal waiting homes will be developed during implementation and will be\ndocumented in the Project Operations Manual.\n(vi) A fixed assets register will record the purchased goods/assets, their location, price, and condition. All\ngoods purchased by the project will be labeled.\n(vii) The existing accounting system will be used to record daily transactions and produce IFRs.\n(viii) The IFRs will be submitted to the World Bank no later than 45 days after the end of each quarter.\n(ix) A Designated Accounts (DA) in US Dollars will be opened in an acceptable commercial bank to receive\nthe project proceeds subject to resolution of lapsed loan under the country portfolio. Health facilities\nwill open special accounts to receive project funds from the DA and make disbursements. The DA and\nspecial accounts will be audited by the project’s external auditor.\n(x) Hospital level health facilities will need to install inventory management system.\n(xi) A Project Operations Manual will be developed, including the rules, guidelines, standard documents,\nand procedures for carrying out the project. The Project Operations Manual will consist of a\ndesignated chapter on FM and disbursement functions.\n(xii) Safeguard measures for the Mobile clinics and buses have been included in environmental and social\nsafeguards instruments.\n(xiii) The Project will be annually audited by a qualified audit firm following TORs acceptable to the World\nBank. The external auditor’s scope of work will be extended to conduct annual stocktaking of\ninventory of items purchased under the project. The auditor will also validate the purchase of\nequipment. All equipment procured under the scheme is within the contract’", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000131:63:0:0", "start": 417, "end": 438, "surface": "fixed assets register", "probe_tag": "drop", "probe_score": 0.0268, "luna_label": 0, "luna_reason": "Project asset register for routine bookkeeping and compliance, not substantive data use."}]}, {"key": "aj-156", "text": "|\n|Methodology for Data<br>Collection|Monitoring project implementation including reporting from beneficiary insitutitons. BRD data fed to<br>MINEMA.|\n|Responsibility for Data<br>Collection|BRD, BDF and MINEMA.|\n|**Micro-finance institutions and Savings and Credit Cooperatives that become project participating financial institutions**<br>**(Number)**|**Micro-finance institutions and Savings and Credit Cooperatives that become project participating financial institutions**<br>**(Number)**|\n|Description|Quantitative indicator counting number of MFIs and SACCOs that become project participating<br>financial instutions.|\n|Frequency|Quarterly.|\n\n\n\nPage 31", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000188:46:2:0", "start": 123, "end": 131, "surface": "BRD data", "probe_tag": "drop", "probe_score": 0.0258, "luna_label": 0, "luna_reason": "Names project monitoring data without an attributed finding or substantive analytical use."}]}, {"key": "aj-157", "text": " children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)**|**Percentage of HC children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)**|\n|Description|Proportion of surviving infants who have received 1st dose of the combined diphtheria, tetanus toxoid, pertussis,<br>Hepatitis B and Homophiles influenza type b vaccine|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH and UNICEF; Measures subcomponent 1.1 Under UNICEF|\n|**Percentage of facilities reporting stock out of tracer medicines (Percentage)**|**Percentage of facilities reporting stock out of tracer medicines (Percentage)**|\n|Description|This indicator measures whether facilities experienced a stockout of one or more tracer medicines and<br>laboratory reagents at any point during the reporting period being assessed. The result is expressed as a<br>percentage of the total number of facilities.|\n|Frequency|Quarterly|\n|Data source|Pharmaceutical agency/ Quarterly Health Facility Assessment|\n|Methodology for Data<br>Collection|Pharmaceutical agency, to be verified quarterly by TPM|\n|Responsibility for Data<br>Collection|TPM; PMU; pharmaceutical agency|\n|**Percentage of refugee facilities reporting stock out of tracer medicines (Percentage)**|**Percentage of refugee facilities reporting stock out of tracer medicines (Percentage)**|\n|Description|This indicator measures whether refugee facilities experienced a stockout of one or more tracer medicines and<br>laboratory reagents at any point during the reporting period being assessed. The result", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000006:48:1:1", "start": 443, "end": 448, "surface": "DHIS2", "probe_tag": "drop", "probe_score": 0.0195, "luna_label": 0, "luna_reason": "Names DHIS2 as a source without showing its data being analyzed or used."}, {"key": "refugee_pads:000006:48:1:2", "start": 1086, "end": 1122, "surface": "Quarterly Health Facility Assessment", "probe_tag": "drop", "probe_score": 0.0432, "luna_label": 0, "luna_reason": "Planned recurring assessment listed as an indicator monitoring source, not existing data used."}]}, {"key": "aj-158", "text": "# **BACKGROUND AND OVERVIEW**\n\ntraveling with their mothers is comparable: recent US Government statistics show\nthat over 66,000 families arrived to the United States in fiscal year 2014. <sup>13</sup>\n\n\nSince 2014, countries of asylum have exerted significant efforts to intensify border\ncontrol measures with a view to containing this phenomenon. However, at the end\nof August 2015, the United States Government recorded more unaccompanied\nchildren arriving to the United States than in the same month in 2014, and the\nnumber of family arrivals at the close of financial year 2015 is the second largest\non record. <sup>14</sup>\n\n\nThis report provides first-hand accounts of the severity of the protection crisis\nin the NTCA and Mexico. The United Nations High Commissioner for Refugees\n(UNHCR) interviewed 160 women from these countries in the US from June\nto August 2015. Though these women do not represent a statistical sample of\nrefugees from this region, they have all been either recognized as refugees or\nhave been screened by the US Government to have a credible or reasonable\nfear of persecution or torture. <sup>15</sup>\n\n\n**FIRST-HAND ACCOUNTS OF REFUGEES FLEEING EL SALVADOR, GUATEMALA, HONDURAS, AND MEXICO** **3**", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001063:4:0:0", "start": 82, "end": 106, "surface": "US Government statistics", "probe_tag": "keep", "probe_score": 0.9488, "luna_label": 1, "luna_reason": "US Government statistics support a concrete family-arrival figure."}]}, {"key": "aj-159", "text": "COMPOUNDING MISFORTUNES\nChanges in Poverty since the onset of Covid-19 on Syrian Refugees and 42\nHost Communities in Jordan, the Kurdistan Region of Iraq and Lebanon\n\n# Annex\n\n\nThis Annex describes some of the technical approaches and assumptions used when preparing the report.\nThis Annex discusses the following aspects:\n\n\n**1. Transforming the income distribution to a consumption distribution**\n\n**2. Imputations: sectors of work and assistance**\n\n**3. Poverty Gap**\n\n**4. Quarterly estimates**\n\n\n### 1. Transforming the income distribution to a consumption distribution\n\nAs discussed in the main text, the SRHCS is a dataset highly\nsuitable for this exercise, primarily because it is comparable\nacross the three countries and comparable between the\nrefugees and their host communities. As such, for a crosscountry report that compares these two communities,\nit provides clear advantages over other datasets which\nmay either be older than the SRHCS or capture only one\nof the two communities, or sacrifice representativity of\nthe underlying population. In addition, the data includes\nan income module that captures eight different income\nsources: wage income, business earnings, pensions, asset\nearnings, government/UN/NGO assistance, remittances,\nauto-consumption, and other income sources.\n\nOne drawback in relation to this analysis and in the effort\nto producing credible poverty estimates, is that the SRHCS\nlacks a consumption module. To address this impediment,\nthe income distributions in the SRHCS were transformed\nto reflect relevant consumption distributions obtained\nfrom respectively the 2017-18 HEIS in Jordan, the 20172018 SWIFT in Iraq, and the 2012 HBS in Lebanon. One\noption is to transform the distributions using survey-tosurvey imputation techniques. For example, per capita\nconsumption in the 2017-18 SWIFT in Iraq could be\nregressed upon common indicators of welfare in both\nSWIFT and SRHCS and the result coefficients would then\nbe used to predict the per capita consumption in SRHCS.", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000383:41:0:2", "start": 1604, "end": 1616, "surface": "2017-18 HEIS", "probe_tag": "keep", "probe_score": 0.9312, "luna_label": 1, "luna_reason": "Existing Jordan survey provides consumption distributions for poverty estimation."}]}, {"key": "aj-160", "text": "**Jordan Home Visits Report 2014 - Living in the shadows**\n\n\n\n\n\n_Source: UNHCR/IRD Home Visits 2014 and UNHCR Registration Database_\n\n\nOver one third (34.6%) of households are headed by females, <sup>4</sup> of which 2.7% are women living alone.\nWhere home visits are conducted with female refugees living alone, the interview is always conducted\nby a female enumerator. For other households, interviews may be conducted by either male or female\nenumerators. Over half (57%) of IRD home visits enumerators are male, and 43% are female.\n\n\nWhile this study is not based on random sampling, no significant systematic bias is apparent in terms of\nage and gender profile, as seen from the below population pyramids comparing home visits data with\ndata from the UNHCR registration database (ProGres). In terms of governorate of origin, Aleppo and\nHama are over-represented in the home visits sample, and Dar’a is under-represented when compared\nto the overall non-camp Syrian refugee population in Jordan.\n\n\n\n\n\n_Source: UNHCR Refugee Registration database_\n\n\n**4** Throughout this analysis, female-headed households are defined as cases in which the Principal Applicant is female.\n\n\n16", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001174:15:0:1", "start": 720, "end": 736, "surface": "home visits data", "probe_tag": "keep", "probe_score": 0.9143, "luna_label": 1, "luna_reason": "Home-visit data are compared with UNHCR registration data to assess sampling bias."}, {"key": "reliefweb:001174:15:0:2", "start": 756, "end": 783, "surface": "UNHCR registration database", "probe_tag": "keep", "probe_score": 0.9533, "luna_label": 1, "luna_reason": "Database data are compared with home visits data to assess sample representation."}]}, {"key": "aj-161", "text": "Nombre|%|\n|De 1 à 4 ans|337|9.20|353|9.63|690|18.83|\n|5 à 11 ans|469|12.80|526|14.36|995|27.16|\n|12 à 17 ans|238|6.50|202|5.51|440|12.01|\n|18 à 59 ans|767|20.93|445|12.15|1,212|33.08|\n|60 ans et Plus|212|5.79|115|3.14|327|8.92|\n|Grand Total|2,023|55.21|1,641|44.79|3664|100.00|\n\n\n\nSource: Enregistrement du 22 au 28 juillet 2010, UNHCR\n\n\n8 Voir carte en annexe\n\n3\nJAM TOGO SEPT 2010", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "sample:reliefweb:001429:9:2:0", "start": 289, "end": 328, "surface": "Enregistrement du 22 au 28 juillet 2010", "probe_tag": "keep", "probe_score": 0.9994, "luna_label": 1, "luna_reason": "Dated UNHCR registration record provides the table’s underlying demographic figures."}]}, {"key": "aj-162", "text": " group._ **|**_*NB – Grey cells indicate that there is at least one assessment available on the specific area or target_**<br>**_group. However, the data may not cover the situation for the entire geographic area or target group._ **|**_*NB – Grey cells indicate that there is at least one assessment available on the specific area or target_**<br>**_group. However, the data may not cover the situation for the entire geographic area or target group._ **|\n\n\n<u>Syrian refugees registered, awaiting registration and unregistered</u>\n\n\n_National_\n\nA survey on the livelihoods of Syrian refugees in Lebanon in October found that a great majority of people\nhave had to change career and seek less skilled work to find work in Lebanon. The same survey, which\nassessed 260 households across Lebanon, found that Syrian refugees feel that they are not maximising on\ntheir skills to find work, as more than half the jobs done by respondents were non-skilled. At the same time,\nsome 23% of the assessed refugees admitted that they are not skilled enough to find jobs in Lebanon.\n\nAn ILO study in March 2013 showed that most Syrians employed in Lebanon continued to work in similar jobs\nto those they had in Syria. Unskilled workers constitute the highest share of Syrians working in Lebanon (45%),\nfollowed by semi-skilled labour (43% of those found working in Lebanon). Skilled professions such as teaching,\nfinancial management and trade, constitute the smallest share of Syrian refugee labour at 13%. Geographical\nvariations were reported, with skilled labour constituting the highest proportion of employed Syrians in Tripoli\n(at 17%) and the lowest in the South (2%).\n\nDuring the ILO study, only 16% of respondents who were working at the time of the assessment expressed a\nneed for training, mostly on new agricultural methods", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000441:23:3:0", "start": 549, "end": 593, "surface": "survey on the livelihoods of Syrian refugees", "probe_tag": "keep", "probe_score": 0.906, "luna_label": 1, "luna_reason": "Existing survey produced findings on refugee livelihoods and employment."}]}, {"key": "aj-163", "text": "-19.pdf)</u>\nby the OECD in collaboration with UNHCR, and\nin which participation was on a voluntary basis. <sup>**23**</sup>\nFurther information on the survey, including the list of\nparticipants, is provided in the note on data sources\nand methodology at the end of this report.\n\n\n**The total volume of bilateral official**\n**assistance to refugee situations**\n**increased between 2016 and 2019**\n\n\nAccording to the 2020 OECD survey data, donors\ncontributed a cumulative total of USD 22.8 billion in\nbilateral Official Development Assistance (ODA) to\nrefugee situations in countries with lower incomes\nover 2018 and 2019. <sup>**24**</sup> This total is USD 24.2 billion\n\n\n\nwhen core contributions to refugee-mandated\nagencies (USD 1.44 billion), such as UNHCR and\nUNRWA, are included. The total amount of ODA\nincreased by 9 per cent (or 8 per cent when the core\ncontributions are included) from USD 10.9 billion\nin 2018 to USD 11.9 billion in 2019. <sup>**25**</sup> This growth in\nbilateral ODA to refugee situations (Figure 8) in host\ncountries with lower incomes continues the positive\ntrend observed in the previous survey conducted\nby the OECD in 2018. <sup>**26**</sup> Despite comparability\nlimitations between the two surveys (with several\nmethodological improvements made in the 2020\nsurvey), the data previously collected by the OECD\nrevealed an increase in bilateral ODA to refugee\nsituations of 23 per cent between 2015 and 2017. <sup>**27**</sup>\n\n\n**Figure 8:** Bilateral ODA to refugee situations, by\n\ntype of recipient, 2018 – 2019 (OECD Financing\n\nfor Refugee Situations Survey 2020, gross\ndisbursement, 2019 constant prices, US dollars)\n\n\n\n12", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000803:11:1:0", "start": 416, "end": 437, "surface": "2020 OECD survey data", "probe_tag": "keep", "probe_score": 0.9729, "luna_label": 1, "luna_reason": "OECD survey data supports reported bilateral ODA totals."}, {"key": "reliefweb:000803:11:1:1", "start": 1307, "end": 1344, "surface": "data previously collected by the OECD", "probe_tag": "keep", "probe_score": 0.9157, "luna_label": 1, "luna_reason": "OECD-collected data supports the reported increase in bilateral ODA."}]}, {"key": "aj-164", "text": " A discussion paper drawing on this material formed the basis for a workshop in Geneva at\nwhich the vast majority of participants were from UNHCR headquarters and the Brussels based\nEurope Bureau, with the addition of some key NGO partners. Following the workshop,\ninterviews were held at UNHCR headquarters with relevant staff in DIP, the bureau and\nmanagement.\n\n\n**Caveats**\n\n\n11. The scope of this review did not allow for in-depth case studies of any resettlement\nsituations. Efforts were made to interview UNHCR staff in the field in various locations\nhowever they were unsuccessful for logistical reasons. This review has been conducted for\nUNHCR, thus the concept, its relevance, achievements and the future are viewed primarily\nfrom UNHCR’s perspective, although the view from and of resettlement countries and the\ngeneral situation regarding SUR also play a role. An important missing perspective is that of\n\n\n6 Interviews were conducted by phone or Skype in most cases. For UNHCR staff, the voluntary survey offered a\nmeans for greater inclusion of views and opinion. However, the answers are not necessarily representative across the\norganization. They cover several situations of SUR, but not necessarily all of them, nor from every angle even within\nthe agency.\n\n\n7", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000512:9:1:0", "start": 1001, "end": 1017, "surface": "voluntary survey", "probe_tag": "confusion", "probe_score": 0.4441, "luna_label": 1, "luna_reason": "Survey answers inform inclusion of staff views and opinions in the review."}]}, {"key": "aj-165", "text": " a final de 2017.**\n\n**13 Un número limitado de países registra sus estadísticas sobre personas**\n**refugiadas y asilo por país de nacimiento en lugar de por país de origen.**\n**Esto afecta al número de refugiados reportados como provenientes de**\n**Estados Unidos de América.**\n\n**14 La cifra de refugiados iraquíes en la República Árabe Siria es una**\n**estimación del Gobierno. ACNUR registró 15.700 iraquíes a final de 2018.**\n**La población refugiada en Jordania incluye a 34.600 iraquíes registrados**\n**con ACNUR. El Gobierno jordano estimó el número de iraquíes en 400.000**\n**a final de marzo de 2015. Esto incluía a refugiados y otras categorías de**\n**iraquíes.**\n\n**15 La cifra de desplazados internos en Myanmar incluye a 120.000 personas**\n**que se encuentran en situación similar a la de desplazados internos.**\n\n**16 Se refiere a personas palestinas refugiadas solamente bajo el mandato de**\n**ACNUR.**\n\n**17 Según el Gobierno argelino, hay cerca de 165.000 refugiados saharauis**\n**en los campamentos de Tinduf. Los datos estadísticos que conciernen a**\n**los refugiados son únicamente para propósitos humanitarios. Se estima**\n**que el número total", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000519:37:1:0", "start": 1033, "end": 1051, "surface": "datos estadísticos", "probe_tag": "confusion", "probe_score": 0.5294, "luna_label": 0, "luna_reason": "Generic statistics are mentioned without an attributed finding or demonstrated analytical use."}]}, {"key": "aj-166", "text": "The authors analyze the impact of the refugee camp on host communities through\n\nboth market and non-market mechanisms. Market mechanisms are those that affect\n\nwelfare through prices of goods, services, labor, and other factors of production. Non\nmarket mechanisms are those that affect welfare through goods and services for which\n\nprices do not exist, such as environmental spillovers from the camp, as well as social,\n\ncultural, and security changes. The authors assess market-based welfare changes\n\nusing a multi-sector general equilibrium model and an empirical approach that focuses\n\non channels of transmission and the aggregate impact by using a set of\n\ncounterfactuals. Non-market welfare effects are assessed through ethnographic\n\nresearch.\n\n\nThe economic analysis relies on a variety of data sources including the Kenyan\n\ncensus, the registration census conducted by the Hunger Safety Net Program (HSNP),\n\nprice data from the Famine Early Warning System (FEWSNET) and Livestock\n\nInformation Network Knowledge System, UNHCR refugee registration data, and WFP\n\nstatistics.\n\n\nMain findings:\n\n- **The refugee presence has a beneficial and permanent impact on**\n\n**Turkana’s economy.** It boosts Turkana’s Gross Regional Product (GRP) by over 3\n\npercent, income per “local” person increases by 0.5 percent, and total employment\n\nincreases by about 3 percent. However, the impact of the refugee presence on the\n\nrest of Kenya is negligible.\n\n- **Unlike tradable sectors, non-tradable sectors (which constitute a much**\n\n**larger share of the economy) benefit from the refugee presence as measured by**\n\n**their impact on prices, wages, and employment.** In the long-term, income in non\ntradable sectors (such as housing, land, restaurants, and hotels) grows by over 7\n\npercent, whereas it shrinks by about 7 percent in tradable sectors. Employment in non\ntradable sectors increases by 6.5 percent compared to a contraction of 6.3 percent in", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001300:33:0:1", "start": 845, "end": 864, "surface": "registration census", "probe_tag": "keep", "probe_score": 0.9347, "luna_label": 1, "luna_reason": "Existing HSNP registration census is declared among data sources used in economic analysis."}, {"key": "reliefweb:001300:33:0:2", "start": 937, "end": 964, "surface": "Famine Early Warning System", "probe_tag": "keep", "probe_score": 0.963, "luna_label": 1, "luna_reason": "Named system supplies price data used in the economic analysis."}, {"key": "reliefweb:001300:33:0:5", "start": 1065, "end": 1080, "surface": "WFP\n\nstatistics", "probe_tag": "confusion", "probe_score": 0.8904, "luna_label": 1, "luna_reason": "WFP statistics are declared as a source for the economic analysis."}]}, {"key": "aj-167", "text": "**Different indicators are used to acknowledge the**\n**various dimensions of educational access as well as**\n**to address diverse approaches to measuring access.**\nMoreover, some access indicators have been found\nto vary according to the type of DCE, with household\nsurveys capturing the greatest variety of information\nconcerning access. Although not all household\nsurveys capture information in every single domain,\nwhen analysed together the 381 household survey\nquestionnaires (354 DCEs) included in the analysis cover\nthe ten sub-indicators addressing access identified for\nthis report. Despite EMIS being considered as a key\nsource of educational data by many actors, public access\nto EMIS questionnaires is often restricted. The findings\nsuggest that, in the absence of open access to EMIS data,\nhousehold surveys hold the most potential to provide\ninformation about the educational situation of refugees.\n\n\n**Educational attainment, attendance, and enrolment**\n**are the most common indicators on educational**\n**access.** The questions that were most covered across\nDCEs were those related to ‘educational attainment’\n\n\n\n**Key Findings**\n<u>Paving pathways for inclusion: A global overview of refugee education data</u>\n\n\nof the respondent (n=515) or of the child or youth\n(n=318), followed by ‘attendance in the current year’\n(n=356). As attainment questions typically ask about\nthe highest level of attainment irrespective of where\nthe education was attained, they are imperfect proxies\nfor access in the host country and, depending on the\nlength of the displacement and age of respondent,\nmay reflect access in the sending rather than the host\ncountry. This means that questions on attendance and\nenrolment are those that can ensure that access to\neducation in the host country is being captured. While\neducational attainment of the respondent is the most\ncommon access indicator among all questionnaires\nregardless of target population (over 40% for both),\nthere are some", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000382:36:0:0", "start": 256, "end": 273, "surface": "household\nsurveys", "probe_tag": "confusion", "probe_score": 0.0973, "luna_label": 1, "luna_reason": "Existing household surveys are attributed with capturing the greatest variety of access information."}]}, {"key": "aj-168", "text": "**_Recommendations for increasing political will and coordination_**\n\n\nTo national governments, donors, and humanitarian and development partners:\n\n  - Implement awareness-raising campaigns to challenge prejudices and highlight refugee contributions to\nthe host society;\n\n\n  - Identify and work with key political champions for refugee education.\n\n\n  - Engage in advocacy efforts with policymakers and stakeholders to emphasize the benefits of including\nrefugees in national education systems;\n\n\n**3. International coordination**\nEffective coordination at national, regional and international levels is critical to ensure efficient use of\nfunding and coherent outputs for all children. International cooperation, along with technical assistance\nand financial contributions supporting government-led plans and assistance frameworks, is vital to ensure\nthe utilization of capacities to enable inclusive policies and implementation plans.\n\n\n**_Barriers contributing to the lack of coordination between stakeholders_**\n\n\n  - A key finding that emerged from our research is that the absence of good coordination mechanisms for\ndata collection on refugee education leads to the duplication of efforts across humanitarian partners and\noften means that existing humanitarian data cannot be used to inform government responses\n\n\n**_Recommendations for strengthening international coordination on refugee educational inclusion_**\n\n\nTo national governments, donors, and humanitarian and development partners:\n\n  - Inform new responses by drawing on pre-existing knowledge and skills as well as existing coordination\nplatforms (e.g., Comprehensive Refugee Response Framework, Regional Monitoring Framework for\nPeople on the Move);\n\n\nTo national donors and humanitarian and development partners:\n\n  - Increase international technical assistance, accompaniment, and financial contributions to work with\ngovernment-led coordination mechanisms (e.g., Rwanda, Ecuador);\n\n\n  - Capitalize on pre-existing coordination platforms with humanitarian partners and governments to reduce\nfragmentation in data collected and improve data sharing.\n\n\n**4. Building education systems’ capacity for refugee inclusion**\nExisting efforts to strengthen education systems, which enhance overall accessibility, safety, and the\nquality of education in refugee-hosting countries (e.g", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000120:5:0:0", "start": 1254, "end": 1271, "surface": "humanitarian data", "probe_tag": "confusion", "probe_score": 0.2943, "luna_label": 1, "luna_reason": "Research finding reports existing humanitarian data cannot inform government responses."}]}, {"key": "aj-169", "text": "\nMinistry of Social Affairs and Solidarity. There are also ongoing discussions with the National Agency for\nPersons with Disabilities that build on the Refugee Law and the Social Protection Decree to give refugees with\ndisabilities new access to national activities, including a registry managed by ANPH.\n\n\nONARS, MASS, WFP and UNHCR are holding discussions on how to align aid and humanitarian assistance with\nsocial protection systems. The aim of the socioeconomic profiling exercise described in section 2.3 is precisely\nto facilitate refugee inclusion in the social registry. However, these discussions are progressing slowly including\nbecause of coordination challenges among public institutions and, as well as capacity and resource gaps as\nregards comprehensive analysis and policymaking. Delays have been exacerbated by COVID-19 impacts.\n\n\n**4.4** **Protection for vulnerable groups**\n\n\nA range of policies, standards and services exists for the protection of Djiboutian children, including\nunaccompanied and separated children, victims of trafficking in persons, survivors of gender-based violence\n[and other children with special needs. In conjunction with the 2017 Refugee Law and the 2017 Decree on](https://www.presidence.dj/texte.php?ID=159&ID2=2017-01-05&ID3=Loi&ID4=1&ID5=2017-01-15&ID6=n)\n<u>[Refugee Fundamental Rights,](https://www.presidence.dj/texte.php?ID=2017-410&ID2=2017-12-07&ID3=D%E9cret&ID4=23&ID5=2017-12-14&ID6=n)</u> these policies apply equally to refugee groups in the same situation.\n\n\nHowever, access to relevant services is limited for both nationals and refugees because of shortcomings\nin policies and resources as well as in implementation. There are child", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001282:11:2:0", "start": 279, "end": 303, "surface": "registry managed by ANPH", "probe_tag": "confusion", "probe_score": 0.3863, "luna_label": 0, "luna_reason": "Registry is mentioned as an access mechanism, without showing its data being used."}, {"key": "reliefweb:001282:11:2:1", "start": 563, "end": 578, "surface": "social registry", "probe_tag": "confusion", "probe_score": 0.7187, "luna_label": 0, "luna_reason": "Registry is mentioned as an inclusion mechanism, without use of its data."}]}, {"key": "aj-170", "text": " opération.\n\n\nC. EVALUATION DES BESOINS DANS LES ZONES DE RETOUR:\n\n\nCet exercice vise, au niveau des villages identifiés, à mieux appréhender et quantifier les besoins des populations\naffectées par la crise: populations déplacées, retournées et communautés d’accueil. Les informations collectées\nconcernent les secteurs alimentation, eau/hygiène/assainissement, abri, moyen de subsistance et éducation.\n\n\n     - Formation: Les formations pour les évaluations des besoins sont menées par des représentants de la DNDS\ndans les régions de Gao, Tombouctou et Mopti au profit des staffs DNDS.\n\n\n     - Collecte des données : Ces évaluations sont menées dans des villages des régions de Gao, Tombouctou et\nMopti où une forte concentration de personnes déplacées et retournées ont été identifiées.\n\n\n     - Saisie des données : Les données collectées sur le terrain seront vérifiées puis entrées dans la base de données\nde la DNDS à travers des tablettes androïdes depuis le terrain par les agents de collecte. Ces données seront mises\nen commun avec les partenaires qui réalisent des évaluations similaires au nord afin de permettre une analyse\ncommune des besoins identifiés dans ces régions.", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000937:43:1:0", "start": 897, "end": 923, "surface": "base de données\nde la DNDS", "probe_tag": "confusion", "probe_score": 0.3347, "luna_label": 0, "luna_reason": "Database entry and subsequent analysis are planned future activities."}]}, {"key": "aj-171", "text": "**PART II:** **POST TSUNAMI PROPERTY RIGHTS**\n\n\n_were receiving and verify Government statements. This would also increase understanding and_\n_inform local authorities as to how the overall relief and reconstruction process would impact their_\n_specific area. Given that the ARN would be involved in physical spatial planning, a participant_\n_noted that it was important for the central government structure responsible for planning to link up_\n_with local government and local authorities._ <sup>_146_</sup> [^146: Centre for Policy Alternatives 2005. email. 08/02/05]\n\nHowever, unless this leveraging is handled carefully, States are likely to perceive this\napproach as a direct attack against their hegemony and will consequently use their\ngeneral foreign affairs powers and their specific controls over visas and inflows of\nforeign currency to thwart both the supervision and execution of projects delivered in\nthis way.\n\nThe critical principle which should determine which is the most appropriate level of\ngovernment to effectively deliver a specific kind of public service is whether their\nintervention synchs rather than suppresses complementary private and civil activity in\nthe sector. In relation to land delivery and land administration services, the lowest\nlevel of government is not invariably the most participatory, effective or appropriate\nactor.\n\nCentral and regional governments have important responsibilities in relation to the\nprotection of minority land rights, ensuring local land management is resourced so as\nto be able comply with national and international norms. Regional governments have\nimportant fiscal equalisation responsibilities in relation to delivery of land based\nservices. One formula for the intra-government arrangements for reparation of\nproperty rights for the whole region will not work. Each context will have to be\ncarefully assessed and international assistance ordered accordingly.\n\n#### **Central governments**\n\n_Executives_\nThe Executive branches of tsunami-affected States will have to deal with the specific\ncapacity challenges thrown up by the need to provide restitution of property rights to\nsurvivors and their descendents where property records have been destroyed.\n\n\n_54. One interim solution to", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001386:44:0:0", "start": 2185, "end": 2201, "surface": "property records", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Records are described as destroyed, without actual data use or analysis."}]}, {"key": "aj-172", "text": " Working Group analysis, June 2025.\n\n108 GBV Area of Responsibility trend analysis, December 2024 to May 2025.\n\n109 GBV Area of Responsibility trend analysis, December 2024 to May 2025.\n\n110 ACAPS, Impact of the conflict on mental health and psychosocial needs in Gaza, September 2024.\n\n111 [OPT Protection Cluster Protection Monitoring Snapshot, June 2025. Gaza Protection Cluster Snapshot: Protection Monitoring | 1 April – 10 June](https://reliefweb.int/report/occupied-palestinian-territory/gaza-protection-cluster-snapshot-protection-monitoring-1-april-10-june)\n\n112 Consultation with Gaza Community Mental Health Program, March 2025.\n\n113 [UNICEF, State of Palestine Humanitarian Situation and Needs, June 2024. 2024-HAC-State of Palestine-revised-June.pdf](https://www.unicef.org/media/158391/file/2024-HAC-State%20of%20Palestine-revised-June.pdf)\n\n114 Atfaluna Society for Deaf Children, Gaza Situation Report 2025, March 2025.\n\n115 Key in-field observations shared by HI during structured consultations, June 2025.\n\n116 Key in-field observations shared by HI during structured consultations, June 2025.\n\n117 Key observations shared through surveys with OPDs in Gaza by the Protection Cluster conducted in May and June 2025.\n\n118 [UNRWA, Protection Brief: Situation of Older Persons in Gaza, 24 June, 2025. Protection Brief: Situation of Older Persons in Gaza | UNRWA](https://www.unrwa.org/resources/reports/protection-brief-situation-older-persons-gaza)\n\n119 Key in-field observations shared by HI during structured consultations, June 2025.\n\n120 [HelpAge International, A lifetime of suffering: The challenges faced by older people in Gaza, 20 February 2024. A", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000539:17:2:0", "start": 292, "end": 345, "surface": "OPT Protection Cluster Protection Monitoring Snapshot", "probe_tag": "confusion", "probe_score": 0.7824, "luna_label": 1, "luna_reason": "Named protection monitoring snapshot cited as an existing external source."}, {"key": "reliefweb:000539:17:2:1", "start": 1149, "end": 1174, "surface": "surveys with OPDs in Gaza", "probe_tag": "confusion", "probe_score": 0.5755, "luna_label": 1, "luna_reason": "Surveys provide the source of observations shared for protection analysis."}]}, {"key": "aj-173", "text": "### 2022 BLUEPRINT INITIATIVE POLICY BRIEF | LIBYA\n\n\n\nLaw, allowing Libyan women to pass on nationality to\ntheir children. Children born to Libyan women and nonLibyan fathers are therefore, in legal terms, considered\nLibyan nationals. They can obtain Libyan nationality\nfrom birth through their mothers, and do no longer\nhave to go through naturalisation when they reach\nadulthood.\n\n- It is also recommended that the MoSA **furthers its**\n**efforts to enforce all provisions of Law No. 27 of**\n**2013** <sup>**11**</sup> **on the Wife’s and Children’s Grant**, and\nespecially Article 3 that prescribes the coverage of\nchildren of Libyan mothers and non-Libyan fathers:\nthe identification of these children is done through the\n**Database for Foreigners** at the Civil Registry Authority\n(CRA). However, key informants reported no families\nwere registered with this database, which they attributed\nto a **lack of awareness** among the population on the\nneed to register and how to register. Thus, to increase\nawareness of this database, **it is encouraged to**\n**consider the use of mass media campaigns**, targeting\nfamilies with a Libyan mother and a non-Libyan spouse,\nabout the CRA’s Department for Foreigners and the\nregistration process.\n\n- Moreover, the MoSA is also encouraged to **issue**\n**executive regulations to enforce Article 4 of Law No.**\n**27 of 2013** to **allow married and unmarried Libyan**\n**women to effectively receive the grant** . Based on this\nArticle as well, the programme’s implementation would\nbenefit from clarifications in the executive regulations\nin regard to **which “unemployed” wives and adult**\n**women are to receive the grant** and whether this", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001687:3:0:0", "start": 728, "end": 751, "surface": "Database for Foreigners", "probe_tag": "confusion", "probe_score": 0.4836, "luna_label": 1, "luna_reason": "Database used to identify children; registration findings are reported."}]}, {"key": "aj-174", "text": "2 \nUNHCR - CUAMM / April 2023 \n \n \nI. Introduction & context \nGender-based violence (GBV) disproportionately affects women and girls, and in situations of conflict \nand displacement, their risk of exposure to GBV increases. Patriarchal and sexist beliefs of gender \nrelations lie at the root of GBV. Poverty is also a driving force behind women and girls’ engagement in \nexchanging sex, which exposes them to significant GBV risks. Women and adolescent girls, as well \nas single women and women with disabilities, have been identified as the groups at highest risk of \nGBV. Community prevention and response mechanisms may exist, although they do not always \nadopt survivor-centred approaches. Information about GBV prevention and response and protection \nfrom sexual exploitation (PSEA) is often lacking among the population, particularly among women and \ngirls. \n \nThe United Nations Refugee Agency (UNHCR) and Doctors with Africa (CUAMM), both active \npartners of the GBV AoR, are committed to continuously strengthening coordination, programming, \nand advocacy across sectors to protect host, displaced, and returned communities from GBV and \nSEA, both in terms of prevention and response. \n \nThis report presents the main findings of the UNHCR-CUAMM April 2023 GBV Safety Audit in \nMassingiri site, Montepuez, Cabo Delgado, Mozambique, which was guided by the 2020 UNHCR \nPolicy on The Prevention of, Risk Mitigation, and Response to Gender-Based Violence. The aim of \nGBV Safety Audits is to understand the specific GBV risks, community response and prevention \nmechanisms, and relevant gaps regarding access to quality services for GBV survivors, through \ncommunity participatory assessments. The findings of Safety Audits inform UNHCR’s, partners’, and \nthe wider GBV AoR’s programming and interventions for GBV prevention and response. \n \nAt the time of writing, Massingiri resettlement site is home to 1,455 internally displaced people (I", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000270:1:0:0", "start": 1243, "end": 1282, "surface": "UNHCR-CUAMM April 2023 GBV Safety Audit", "probe_tag": "confusion", "probe_score": 0.6166, "luna_label": 1, "luna_reason": "Report presents findings from the named April 2023 safety audit."}]}, {"key": "aj-175", "text": "**3.3.2 Access to Safe Latrine**\n\n\nIn contrast to the qualitative findings from the field observations, KIIs, and FGDs, the majority of the HH survey\nrespondents of this study (Rohingya female - 83%, Rohingya male - 89%; host community female - 84%, and male - 82%)\nreported that they had access to safe latrine facilities.\n\n\nHowever, for those who responded negatively, the reasons for not being able to access safe latrine facilities were\ngendered. For instance, the key reasons for Rohingya women included no locks on the door, no lighting, and not secure\nat night (Figure 15). The main reasons for not being able to access safe latrine facilities for the host community women\nincluded that latrines were not secured at night; no sex-segregated toilets; and, no lighting (Figure 16).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe HH survey result contradicts qualitative findings\nthat particularly women and girls have access to safe\nlatrine facilities, especially in the camps. Conforming to\nprevious studies, <sup>99</sup> this research also suggests unpacking\nthe idea of “safety” to make sure that the humanitarian\nactors, refugees and the host community share a\nsimilar understanding of the concept, and this can help\nstrengthen inclusive service provision.\n\n\nUnlike the quantitative results, the qualitative data\nshows that Rohingya women and girls, in particular those\nliving with disabilities, experience barriers to safe access\nto latrine facilities. The most common reasons cited in\n\n\n\nqualitative findings include fear of SGBV, particularly\nat night, and shyness in using toilets during the day.\nFindings from FGDs and the mobility analysis on the\nuse of toilets by women and girls at night, highlight two\nconcerns which include the location of toilets beside\nthe road, and a lack of privacy due to men and boys\nhanging around. These issues, the fear of SGBV, and the\nrestricted mobility Rohingya women and girls’ experience,\ncreate", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000732:40:0:0", "start": 140, "end": 149, "surface": "HH survey", "probe_tag": "keep", "probe_score": 0.9298, "luna_label": 1, "luna_reason": "Household survey supplies reported access percentages supporting the finding."}, {"key": "reliefweb:000732:40:0:2", "start": 1308, "end": 1324, "surface": "qualitative data", "probe_tag": "confusion", "probe_score": 0.8418, "luna_label": 1, "luna_reason": "Qualitative data supports concrete findings about barriers to safe latrine access."}]}, {"key": "aj-176", "text": "Regular Surveys on Social Tensions throughout Lebanon: Wave IV September 2018\n\n\n3.2.7 Inter-Communal Contact ........................................................... 32\n\n\n4 Results: Impact of Assistance ................................................................ 34\n\n\n4.1 Refugee Population Pressure on Services (RPP-S) ...................... 35\n\n\n4.2 Quality of Relations (QoR) ............................................................ 36\n\n\n4.3 Propensity for Negative Collective Action (PNCA) .................... 37\n\n\n5 Conclusions and Recommendations ..................................................... 39\n\n\nAppendix A: Survey Instrument .......................", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000696:2:0:0", "start": 0, "end": 53, "surface": "Regular Surveys on Social Tensions throughout Lebanon", "probe_tag": "confusion", "probe_score": 0.7701, "luna_label": 1, "luna_reason": "Named survey series identified with Lebanon and a specific survey wave."}]}, {"key": "aj-177", "text": "UNHCR MPCA TO NEW SUDAN ARRIVALS PDM REPORT - 2024\n\n# **Acknowledgements**\n\n\nSagaci Research would like to thank the UNHCR Egypt CBI team for their professional support and facilitation of\nthe Post-Distribution Monitoring reporting exercise. The team would also like to thank Sagaci Research Egypt\nfield team who managed the quantitative data collection. Our sincere appreciation goes to the cash assistance\nbeneficiaries themselves who gave their valuable time to participate in the data collection process. UNHCR would\nlike to thank UNICEF who funded this cash assistance .\n\n**Sagaci Research** is an Africa-focused analytics firm. They provide data and insights you can trust and fuel your\ngrowth in Africa. They provide brand trackers, online panels, retail audits and other consumer research and data\nacross the African continent.\n\nThis publication was commissioned by UNHCR, the UN Refugee Agency in Egypt, and was prepared and\nconducted by Sagaci Research.\n# **Summary**\n\n\nUNHCR provided a multipurpose cash assistance funded by UNICEF to the most vulnerable new arrivals from\nSudan, who were on UNHCR waiting list, to support them in covering their immediate needs, and mitigate\npotential protection risks, and also to reduce their resorting to negative coping strategies. The cash assistance\nwas meant to cover four months and was funded by UNICEF. It was distributed through Egypt Post Office\nbranches that cover all Egypt’s governorates. To assess the impact of the assistance, and beneficiaries’ views on\nthe cash distribution process, UNHCR conducted a post-distribution monitoring survey (PDM) for MPCA\nprovided in 2023/2024.\n\nData collection for this quantitative assessment was carried out by a third-party, Sagaci Research, between 11\nand 24 February 2024. Telephone interviews were conducted with a representative sample of beneficiary\nhouseholds from Sudanese new arrivals who were randomly selected from Financial Service Provider (FSP) cash\ncollection reports in September and December 2023. The sample size was calculated using confidence level of\n95 per cent and confidence", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000339:1:0:0", "start": 1566, "end": 1601, "surface": "post-distribution monitoring survey", "probe_tag": "confusion", "probe_score": 0.0989, "luna_label": 0, "luna_reason": "The sentence states UNHCR conducted the survey as part of this assessment."}]}, {"key": "aj-178", "text": ">Q2-Q1<br>Q3-Q2<br>Q4-Q3<br>Q1<br>Q2<br>Q3<br>Q4<br>Italy|**Table 8. Origin of asylum applicants in 43 industrialized countries by quarter, 2007**<br>Covering 43 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<br>Q3-Q2<br>Q4-Q3<br>Q1<br>Q2<br>Q3<br>Q4<br>Italy|**Table 8. Origin of asylum applicants in 43 industrialized countries by quarter, 2007**<br>Covering 43 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<br>Q3-Q2<br>Q4-Q3<br>Q1<br>Q2<br>Q3<br>Q4<br>Italy|**Table 8. Origin of asylum applicants in 43 industrialized countries by quarter, 2007**<br>Covering 43 countries which provided monthly data to UNHCR (excluding Italy", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000427:20:2:0", "start": 187, "end": 199, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.716, "luna_label": 1, "luna_reason": "Existing monthly data supplied to UNHCR underlies the table's asylum-applicant analysis."}]}, {"key": "aj-179", "text": "homme et des points clés du\nplaidoyer et n'inclut pas tous les incidents et violations survenus au cours de\nla période. Les chiffres du suivi de la protection peuvent ne pas correspondre\naux derniers développements pour diverses raisons, notamment l'insécurité\ndans de nombreuses zones de conflit, qui rend impossible la collecte de\ndonnées. Les chiffres finaux seront publiés à travers les différents\nmécanismes de rapportage établis.\n\n- Si vous avez des commentaires ou des informations pour compléter et\naméliorer le rapport, merci de bien vouloir nous contacter.\n\n\nSi vous avez des commentaires, questions, ou données supplémentaires, veuillez contacter :\n[Steve Ndikumwenayo (ndikumwe@unhcr.org) ou Lorraine de Limelette (lorraine.delimelette@nrc.no)](mailto:ndikumwe@unhcr.org) 14", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001004:13:1:0", "start": 124, "end": 158, "surface": "chiffres du suivi de la protection", "probe_tag": "confusion", "probe_score": 0.6663, "luna_label": 0, "luna_reason": "Generic monitoring figures are mentioned without an attributed finding or analytical use."}]}, {"key": "aj-180", "text": " conflit armé interne, troubles et tensions intérieurs, violence_\n_généralisée, violations massives des droits de l’homme, infractions au droit international_\n_humanitaire ou toutes autres circonstances qui émaneraient des situations antérieures et_\n_qui pourraient altérer ou altèrent de façon drastique l’ordre public_ ».\n\n\n   - En premier lieu, le déplacement est une modalité normale du processus de\npeuplement en Colombie. Au fur et à mesure que les latifundia se sont\ndéveloppées, les paysans se sont déplacés, s’installant toujours plus aux marges des\nzones fertiles.\n\n\n   - Au XXe siècle, un exode rural très rapide se réalise entraînant une urbanisation tout\naussi rapide. De rurale, la société devient majoritairement urbaine entre les années\n1950 et 1970. Bogota est passée de 715.220 habitants en 1951 à 1,6 millions en 1964,\net à plus de 3 millions en 1973. A partir de 1973, les migrations des campagnes vers\nles plus grandes villes diminuent. Ce sont les villes moyennes qui attirent le plus\nles flux migratoires.\n\n\n   - Cependant, la croissance de Bogota se poursuit, alimentée par le croît naturel et\nplus encore par des arrivées de populations. Au recensement de 1993,\nl’agglomération compte 6,2 millions d’habitants. En 1938, la société colombienne\nest à 69% rurale, en 1993, elle est à 69% urbaine.\n\n\n   - Au processus historique de déplacement et d’exode, est venu s’aj", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000466:4:1:0", "start": 1166, "end": 1185, "surface": "recensement de 1993", "probe_tag": "confusion", "probe_score": 0.8255, "luna_label": 1, "luna_reason": "1993 census provides the population figure for Bogotá's agglomeration."}]}, {"key": "aj-181", "text": ",484|0.02|\n|Sweden|3,346|9,910,701|2,962|0.34|\n|Switzerland|610|8,476,005|13,895|0.07|\n|United Kingdom|6,202|66,181,585|10,671|0.09|\n|United States|24,559|324,459,463|13,211|0.08|\n|Uruguay|16|3,456,750|216,047|0.00|\n\n\n\n- \u0007Departure figures reported by UNHCR may not match resettlement statistics published by States as Government figures may include submissions\nreceived outside of UNHCR resettlement processes.\n\n** \u0007Source: United Nations, Population Division, World Population Prospects: The 2017 Revision, New York, 2017. For the purpose of this analysis, the 2017\npopulation projections (medium fertility variant) have been used. (See: https://esa.un.org/unpd/wpp/).\n\n\n\n**79**", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001640:79:2:0", "start": 272, "end": 295, "surface": "resettlement statistics", "probe_tag": "confusion", "probe_score": 0.3458, "luna_label": 1, "luna_reason": "Existing State-published statistics are compared with UNHCR departure figures."}, {"key": "reliefweb:001640:79:2:2", "start": 563, "end": 590, "surface": "2017\npopulation projections", "probe_tag": "confusion", "probe_score": 0.7776, "luna_label": 1, "luna_reason": "Existing 2017 projections are explicitly used for the analysis."}]}, {"key": "aj-182", "text": " compiles information on primary and lower secondary examinations for 63\ncountries, identifying the availability of national exams, the grades in which they are implemented, as well as country-level pass rates (Rossiter and Konate, 2022). While\nthe geographical scope is limited, these databases are valuable resources to access information not only on learning outcomes, but also on educational opportunities\n(e.g. when national exams are high stakes and determine advancement to the next educational level). Moreover, this database can be used to track the evolution\nof national exams (e.g. frequency, purpose). However, it does not yet offer analysis or references to different population groups, a disaggregation that is relevant to\npromoting refugee data inclusion\n\n\n**43**", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000382:42:1:0", "start": 747, "end": 759, "surface": "refugee data", "probe_tag": "drop", "probe_score": 0.038, "luna_label": 0, "luna_reason": "Describes a desired disaggregation, not existing data used for analysis or evidence."}]}, {"key": "aj-183", "text": "Executice Summary\n\n\nStrengthening Advocacy and Partnership :\n\nUNHCR seeks sustainable partnerships with key government actors as primary partners in SGBV\nprevention and response throughout the MENA region. UNHCR also collaborates and coordinates\nwith other key stakeholders including sister UN agencies, NGOs as well as communities and\nrefugees themselves to maximise the effectiveness and efficiency of SGBV prevention and\nresponse through complementary interventions, standards and tools, joint programming, and\ncommon advocacy interventions\n\n\nImproving Data Collection and Analysis :\n\nData collection and analysis are the backbone of results-based SGBV programming. It is critical to\nthe effectiveness of targeted service delivery, advocacy, policy development, and accountability\nand monitoring. UNHCR has supported the rollout of the Gender-Based Violence Information\nManagement System (GBVIMS) to ensure the safe, ethical, and confidential collection,\nmanagement and sharing of SGBV data in various operations.\n\n\nAdvancing Global Initiatives :\n\nUNHCR is committed to advancing the global initiatives “Safe from Start” and “Call to Action” to\nreinforce SGBV prevention and response programming in its operations across the world. “Safe\nfrom the Start” is an initiative supported by the United States of America (US) Department of\nState to ensure quality services are available for SGBV survivors at the onset of an emergency\nthrough timely and effective humanitarian action. The “Call to Action on Protecting Girls and\nWomen in Emergencies (Call to Action)” initiative was launched by the United Kingdom’s\nDepartment for International Development (DFID) to mobilize donors, UN agencies, NGOs, and\nother stakeholders in better protecting women and girls in humanitarian emergencies.", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000871:6:0:0", "start": 984, "end": 993, "surface": "SGBV data", "probe_tag": "drop", "probe_score": 0.0139, "luna_label": 0, "luna_reason": "Generic data mentioned for collection and management, without concrete analytical use."}]}, {"key": "aj-184", "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, "luna_reason": "Disaggregated data enriched an assessment and analysis of needs and programming gaps."}, {"key": "reliefweb:000271:16:0:1", "start": 173, "end": 195, "surface": "AGD-disaggregated data", "probe_tag": "drop", "probe_score": 0.0406, "luna_label": 1, "luna_reason": "Existing disaggregated data were analyzed in consultations and assessment processes."}, {"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, "luna_reason": "ProGres data informed prioritization of assistance to high-risk refugee households."}, {"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, "luna_reason": "Analysis of disaggregated data highlighted school enrollment needs."}]}, {"key": "aj-185", "text": "**1.** **Theft, extortion, forced eviction or destruction of personal property**\n**2.** **Attacks on civilians and other unlawful killings, and attacks on civilian objects**\n**3.** **Forced recruitment and association of children in armed forces and groups**\n**4.** **Psychological/emotional abuse or inflicted distress**\n\n\nThe Protection Analysis Update for Jowhar focuses on the Humanitarian Country Team’s (HCT) priority districts and as part of\nthe Area Based Coordination (ABC) for 2024. The PAU aims to understand the existing protection risks observed within the\ndistrict, and to inform humanitarian actors in developing measures that can help to mitigate identified risks. Jowhar district\nhosts a large population of minorities and marginalized groups who often face heightened protection risks, creating a need for\ntailored response. Therefore, this joint protection analysis conducted by the Protection Cluster and its partners aims to define\nthe protection response strategy and priorities in the district.\n\n\n1 _<u>[Protection Risks: Explanatory Note](https://www.globalprotectioncluster.org/publications/994/training-materials/template/protection-risks-explanatory-note)</u>_\n2 _[Protection and Solutions Monitoring Network (PSMN)) is a project implemented by UNHCR, the Norwegian Refugee Council (NRC) and partners. It is a](https://prmn-somalia.unhcr.org/)_\n_platform for identifying and reporting on displacements (including returns) of populations in Somalia as well as protection incidents and risks underlying such_\n_movements. PSMN as a tool has been adopted at the interagency and inter-cluster level as a source of both displacement and protection data in Somalia._", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000933:0:0:0", "start": 1641, "end": 1673, "surface": "displacement and protection data", "probe_tag": "drop", "probe_score": 0.0225, "luna_label": 1, "luna_reason": "PSMN is identified as an adopted source of displacement and protection data."}]}, {"key": "aj-186", "text": "/www.climatecentre.org/wp-content/uploads/RCCC-ICRC-Country-profiles-Yemen.pdf)</u>\n\n- <u>[Climate Change Risk Profile Yemen, Global Environmental Management Support Project](https://www.climatelinks.org/sites/default/files/asset/document/2016_USAID%20GCC%20Office_Climate%20Risk%20Profile_Yemen.pdf)</u>\nUSAID.\n\n\n**5.5. Disaster History**\n\n|Reported Disasters – Yemen – 1900 t0 2023|Col2|Col3|Col4|\n|---|---|---|---|\n|**Disaster**|**Number**|**Disaster**|**Number**|\n|Flood, of<br>various types|47|Earthquakes|3|\n\n\n\n[8 Contacts for the report, Mr. Kamal S. Al-Kharmeri, kamal.alkhameri@gmail.com or UNICEF Yemen.](mailto:kamal.alkhameri@gmail.com)\n\n\nYemen Environment Profile - 21 – September 2023", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000976:22:2:1", "start": 651, "end": 676, "surface": "Yemen Environment Profile", "probe_tag": "drop", "probe_score": 0.0272, "luna_label": 0, "luna_reason": "Standalone report title does not show cited or analyzed data use."}]}, {"key": "aj-187", "text": "**<u>2.3 Challenges and Limitations</u>**\n\n\n**The research instruments were translated**\n**from English to Bangla. The enumerators were**\n**unable to use Rohingya language to conduct**\n**research at field level. However, using Bangla**\n**questionnaires, the enumerators articulated**\n**the questions in the** **_Chittagonian_** **dialect,**\n**and took notes in Bangla. Some richness and**\n**nuances in data might have been lost due**\n**to applying different languages in the data**\n**collection process.**\n\n\n**Limited time was allocated for reviewing**\n**and adapting quantitative and qualitative**\n**tools from CARE’s gender analysis toolkit and**\n**customising training for this research. Limited**\n**comprehension of the tools could have led**\n**to misinterpretation of the survey questions**\n**by the enumerators as well as respondents.**\n**This could also have been due to the limited**\n**understanding of gender concepts.**\n\n\n**Qualitative data from the host community**\n**was very limited. More effort could also have**\n**been placed on identifying people living with**\n**disabilities in the host community, as well as**\n**from the camps.**\n\n\n\nWhile the experiences and views of people living with\ndisabilities from the host community were absent in the\ndata gathered, data collected from people living with\ndisabilities in the refugee communities gave limited\ninsight into their experiences. Out of four participants,\none pregnant woman was included as a person living\nwith a disability, indicating a limited understanding of the\ntopic among enumerators or little time to identify and\nrecruit people living with", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000732:28:0:0", "start": 934, "end": 950, "surface": "Qualitative data", "probe_tag": "drop", "probe_score": 0.0357, "luna_label": 1, "luna_reason": "Qualitative data are reported as very limited in the host community."}, {"key": "reliefweb:000732:28:0:1", "start": 1276, "end": 1327, "surface": "data collected from people living with\ndisabilities", "probe_tag": "confusion", "probe_score": 0.2422, "luna_label": 1, "luna_reason": "Collected disability data provided limited insight into refugee communities' experiences."}]}, {"key": "aj-188", "text": "Chapter 8\n\n\nFigure 18 **\u0003|** **Coverage of sex and age disaggregated data for UNHCR’s population of concern** | 2000-2017\n\n\n\n70\n\n\n60\n\n\n50\n\n\n40\n\n\n30\n\n\n20\n\n\n10\n\n\n0\n\n\n\n‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09 ‘10 ‘11 ‘12 ‘13 ‘14 ‘15 ‘16 ‘17\n\n\nTotal pop. of concern Sex-disaggregated data available Age-disaggregated data available\n\n\n\nresourced. Furthermore, disaggregated data\ncoverage is variable by population groups, with data\non some groups such as IDPs being particularly poor.\n\n\nDemographic Characteristics\n\n\nDespite UNHCR’s efforts to improve data availability,\nit has continued to be difficult to obtain\ndisaggregated data in many countries where the\nagency is not involved in primary data collection,\nwith a substantial number of countries not reporting\ndisaggregated data to UNHCR.\n\n\nThe number of countries reporting at least some\nsex-disaggregated data has remained steady in 2017\nat 147 countries. Similarly, the population covered by\nsex-disaggregated data has decreased slightly from\n2016, now at 59 per cent as opposed to 60 per cent\nin the previous year [Figure 18]. According to the\navailable data, overall men and women were almost\nequally represented in the population of concern,\nwith 21.3 million men and 21.0 million women. <sup>**74**</sup>\n\n\nCoverage of the population of concern by age was\nlower than for sex. In 2017, 136 countries reported at\nleast some age-disaggregated data but which\n\n\n\ncovered only 38 per cent of the entire population of\nconcern, an increase on the 25 per cent of the\nprevious year but when a higher number of countries\n(141) reported data. Out of the 27.0 million people for\nwhich age-disaggregated data is available, 14.2 million\nor 53 per cent were children under the age of 18.\n\n\nRefugees and", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000669:57:0:0", "start": 261, "end": 283, "surface": "Sex-disaggregated data", "probe_tag": "drop", "probe_score": 0.037, "luna_label": 0, "luna_reason": "Figure legend label, not an independent data-use mention"}]}, {"key": "aj-189", "text": " above<br> <br>|3)<br>Establish<br>savings (including<br>assets)<br>a) Has (X) in savings<br>b) Assets worth (X) amount<br>To be convened with<br>partners<br>As above<br> <br>|\n|4) Establish self-<br>confidence/agency<br>a) Has a plan for the future<br>b) Knows his/her rights<br> <br>To be convened with<br>partners<br>Two evaluations required for comparison purpose and<br>decision making on refugee status:<br>a) ” beneficiary end-line survey” where the direct<br>beneficiary self-report”- applied to all the population – all<br>the direct beneficiaries<br>b) Independent external evaluation applied on a sample<br>of the beneficiaries expected to graduate<br> <br>|4) Establish self-<br>confidence/agency<br>a) Has a plan for the future<br>b) Knows his/her rights<br> <br>To be convened with<br>partners<br>Two evaluations required for comparison purpose and<br>decision making on refugee status:<br>a) ” beneficiary end-line survey” where the direct<br>beneficiary self-report”- applied to all the population – all<br>the direct beneficiaries<br>b) Independent external evaluation applied on a sample<br>of the beneficiaries expected to graduate<br> <br>|4) Establish self-<br>confidence/agency<br>a) Has a plan for the future<br>b) Knows his/her rights<br> <br>To be convened with<br>partners", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001259:85:7:1", "start": 418, "end": 445, "surface": "beneficiary end-line survey", "probe_tag": "drop", "probe_score": 0.0341, "luna_label": 0, "luna_reason": "Required end-line survey is planned for future comparison and decision-making."}]}, {"key": "aj-190", "text": "**</sup>\n\n\n\nTable 3 **| High frequency surveys conducted** | 2020-2021\n\n\n\n\n\n\n\n\n\n|Country|FDP Rounds<br>available|Months|Defined Survey Population(s)|\n|---|---|---|---|\n|Bangladesh|2|Apr/May 2020<br>Oct/Dec 2020|Refugees: Cox’s Bazar camps<br>Host: Cox’s Bazar district residents|\n|Djibouti|1|Dec/Jan 2020/2021|Refugees: camps and urban non-camp<br>Host: national urban|\n|Ethiopia|2|Sep/Oct 2020<br>Oct/Nov 2020|Refugees: camps and urban non-camp<br>Host: national|\n|Iraq*|4|Monthly:<br>Oct 2020 – Jan 2021|IDPs (camps, non-camps): Kurdistan Region of Iraq, North<br>Returned IDPs (IDPs in location of return): Kurdistan<br>Region of Iraq, North<br>Host: non-displaced in Kurdistan Region of Iraq, North|\n|Yemen*|9|Monthly:<br>Apr – Dec 2020|IDPs: national<br>Host: non-displaced national|\n\n\nNot receiving JDC support:\n\n\n\n\n|Kenya|3|May/Jun 2020<br>Jul/Sep 2020<br>Oct/Nov 2020|Refugees: camps and urban non-camp<br>Host: national|\n|---|---|---|---|\n|Uganda|2|Oct/Nov 2020<br>Nov/Dec 2020|Refugees: in camps<br>Host: national|\n\n\n\n\n    - Surveys in Iraq and Yemen executed as partnership between World", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000757:59:1:0", "start": 24, "end": 46, "surface": "High frequency surveys", "probe_tag": "drop", "probe_score": 0.0465, "luna_label": 0, "luna_reason": "Standalone table header naming survey category, not a data-use mention."}, {"key": "reliefweb:000757:59:1:1", "start": 1035, "end": 1060, "surface": "Surveys in Iraq and Yemen", "probe_tag": "confusion", "probe_score": 0.7173, "luna_label": 0, "luna_reason": "The sentence states these surveys were executed, indicating data production."}]}, {"key": "aj-191", "text": "An example was shared from Dadaab, where only annual funding has been available, which makes it very\n\ndifficult to plan for long term challenges associated with climate change related displacement. Predictability and\n\na forward-looking approach across both Compacts are needed to be better prepared and anticipate how climate\n\nchange will impact people’s resilience, ability to maintain livelihoods, and their protection needs.\n\n\n**Meeting joint commitments to refugees and migrants**\n\n\n_“In the absence of regular mobility, human smugglers have become vectors of irregular migration. They_\n_shape who moves, how they move, and in what quantity they move. Those with responsibilities to better_\n\n_manage migration and to implement systems for international protection, need to be more sensitive to the_\n\n_vector and drivers of human mobility that smugglers are.”_\n\n\nTuesday Reitano, Global Initiative Against Transitional Organized Crime\n\n\n_“The CRRF gives us the opportunity to look at refugee protection from a whole-of-society approach. This_\n\n\n_includes consultations directly with children and youth. “_\n\n\nRez Gardi, Empower Youth Trust\n\n\nThe New York declaration specifically acknowledges that, though their treatment is governed by separate legal\n\nframeworks, refugees and migrants have the same universal human rights and fundamental freedoms. They\n\nalso face many common challenges and have similar vulnerabilities, including in the context of large movements.\n\nThus seven joint commitments were established in the New York declaration, including commitments to tackle\n\nxenophobia, smuggling and trafficking, to protect children and improve data on mixed flows. The discussion\n\nwas structured to take participants along the shared journey of a refugee or migrant. In doing so, this session\n\nused UNHCR’s 10 Point Plan of Action on Refugee Protection and Mixed Migration to highlight good practices\n\nof successfully responding to the common needs of persons in mixed flows at various points in that journey and\n\nreflected on ways in which States can meet the aforementioned joint commitments to refugees and migrants.\n\n\nThe New York declaration also foresees the development of two global", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000146:52:0:0", "start": 1650, "end": 1669, "surface": "data on mixed flows", "probe_tag": "drop", "probe_score": 0.0283, "luna_label": 0, "luna_reason": "Commitment describes improving future data, not using an existing dataset."}]}, {"key": "aj-192", "text": " only 43 respondents out of 151 having a**\n**generator as a second source of electricity.** The low number\nof respondents who reported having a backup power supply in\ncase of power outage suggests that respondents have limited\naccess to electricity, considering the frequent disruptions to the\nelectrical grid observed in the west and south of the country. <sup>26</sup>\n\n\n\nWhen asked about the amount of time spent without electricity\nin the last 7 days, the average was 7.38 hours per day, with\ndiscrepancies between the assessed mantikas. Sebha was\nfound to be the most affected by power outages. Refugees\nand migrants interviewed in Sebha reported not having\nelectricity between 12 to 14 hours per day in the week prior\nto the assessment. Refugees and migrants in Zwara reported\nnot having electricity for an average of 5 to 11 hours per day.\nThe least affected regions were Misrata (mainly 3 to 8 hours/\nday), Tobruk (mainly 3 to 8 hours/day) and Ejdabia (mainly 0\nto 5 hours/day). Finally, 27 refugees and migrants interviewed\nreported not having access to electricity for 15 hours or more\nper day, compared to only 1% of Libyans reporting this in the\nMSNA.\n**Access to fuel**\n\n**Cooking fuel**\n\nThe majority of respondents reported having regular access to\ncooking fuel (90/151), followed by 49 individuals who reported\nonly having irregular access to cooking fuel. Eleven individuals\nreported not needing cooking fuel, as they ate food at their\nemployer’s.\n**Vehicle fuel**\n\nAlmost two thirds of respondents (98/151) reported not using\nor needing vehicle fuel, as they did not have a vehicle. Among\nthose who reportedly used vehicle fuel, 37 reported having\nregular access, with 11 reporting having irregular access. Two\nrespondents reported not having any access to vehicle fuel.\nRespondents who used vehicle fuel were predominantly", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000824:5:2:0", "start": 1158, "end": 1162, "surface": "MSNA", "probe_tag": "drop", "probe_score": 0.0117, "luna_label": 1, "luna_reason": "MSNA is cited as the source of a comparative electricity-access finding."}]}, {"key": "aj-193", "text": "**UNHCR • UNICEF • IOM** September 2019\n\n\n\nEndnotes\n\n1 European States used for this report include EU\nMember States, as well as Serbia.\n\n2 Age groups used as reference for school-age children\ndepend on national legislation and education systems:\n5-18 years old in Bulgaria, 3-17 years old in France,\n6-18 years old in Germany, 5-17 years old in Greece,\n6-18 years old in Italy, 7-18 years old in Serbia, etc.\n\n3 Compulsory school varies across countries, e.g. 5-16\nyears old in Bulgaria, 6-16 years old in France, 6-15\nyears old in Germany, 5-17 years old in Greece, 6-16\nyears old in Italy, 7-15 years old in Serbia.\n\n4 EU+ refers to EU Member states, Iceland, Liechtenstein,\n[Norway and Switzerland. Data source: Eurostat](http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_pop5ctz&lang=en)\n\n5 European database <u>[(Eurostat) does not allow for](http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_pop5ctz&lang=en)</u>\ndisaggregation by age up to 17 years. For the purposes\nof this analysis therefore the age bracket 5 to 19 has\nbeen used.\n\n6 Based on a sample of 364 children between 14 and 17\nyears old.\n\n7 Apart from Pakistan, the remaining four listed\ncountries were among the top 10 origin countries of\narrival between January and November 2017, when\nsurveys were conducted\n\n8 Based on <u>[the UNICEF REACH report ‘Children on](https://www.unicef.org/eca/reports/children-move-", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000457:15:0:3", "start": 782, "end": 794, "surface": "migr_pop5ctz", "probe_tag": "drop", "probe_score": 0.0425, "luna_label": 0, "luna_reason": "Dataset identifier fragment lacks an eligible source noun or standalone data-use claim."}, {"key": "reliefweb:000457:15:0:4", "start": 1322, "end": 1341, "surface": "UNICEF REACH report", "probe_tag": "keep", "probe_score": 0.955, "luna_label": 1, "luna_reason": "Report is explicitly used as the basis for analysis."}]}, {"key": "aj-194", "text": "outcomes of individuals in this narrow age group below and above the cut-off age would have\n\nevolved similarly in the absence of the reform.\n\n\nOur sample contains DI recipients belonging to the affected benefit categories who were\n\naged 56 or 57 in December 2011. Those who were 56 (just below the cut-off) in December\n\n2011 make up the treatment group, while those who were 57 (just above the cut-off) make up\n\nthe control group. We restrict the sample to individuals claiming DI throughout 2011 who\n\nwere alive in January 2012. We restrict the control age group to age 57 at the end of 2011\n\nto exclude individuals close to the old-age retirement age in order to improve comparability\nacross the control and treatment age groups. <sup>9</sup> We focus on men below 62, the statutory\n\nretirement age for the oldest cohorts, allowing us to use data up to 2015. Our focus on men\n\nis motivated by the “Women 40” policy which since 2011 gives an early retirement option to\n\nwomen with 40 years of work credits, regardless of age. This policy could affect the control\n\nand treatment age groups differently, potentially confounding our results for women. Finally,\n\nthose who died during the observed time period are included in the sample until the last\n\nyear they were alive.\n\n\nSummary statistics for the control and treatment groups are displayed in Table 2. The\n\ntwo groups are quite similar to each other on most dimensions. They have approximately the\n\nsame employment rate (24.3% vs 24.9%) while receiving benefits in 2011 and each group has\n\nbeen receiving benefits for 11 years on average. Despite being a year younger, the 56-year\nold treatment group may be slightly less healthy with average prescription drug spending of\n\n533 euros vs 512 euros among the 57-year-old control group. Importantly for labor market\n\noutcomes, the two groups live in geographic areas with similar economic environments as\n\nevidenced by the average unemployment rate of their micro-", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000704:10:0:0", "start": 844, "end": 859, "surface": "data up to 2015", "probe_tag": "confusion", "probe_score": 0.0864, "luna_label": 0, "luna_reason": "Generic data availability timeframe without an identified source or analyzed finding."}]}, {"key": "aj-195", "text": "Policy Research Working Paper 8872\n\n### **Abstract**\n\nMental health, well-being, and lasting economic outcomes\nare intimately connected. However, in geographies marked\nby fragility, conflict, and violence (FCV), entrepreneurs\nof small and medium size enterprises (SMEs) experience\nchronic stress and poor mental health on a regular basis.\nThese issues can hamper performance and quality of life\n\nfor the entrepreneurs, and can dampen the benefits of\nexisting financial and business assistance programs. Few\nproven rigorous interventions are known. This study tests\nthe hypothesis that a five-week group Cognitive Behavioral\nTherapy (CBT) training called Problem Management Plus\n\nfor Entrepreneurs (PM+E), in combination with financial\nassistance, could be more effective at reducing psychological stressors of SME entrepreneurs in FCV contexts\nthan financial assistance alone. Meaningful and statistically\nsignificant improvements in mental health were achieved,\nwith improvements persisting and increasing beyond the\n\n\n\nimmediate post-intervention period. Based on analysis of\npooled data across two follow-up rounds (at five weeks and\nthree months post-intervention), entrepreneurs in the treatment group experienced statistically significant reduction\nin the intensity and prevalence of depression and anxiety\nsymptoms (measured by the Patient Health Questionnaire Anxiety and Depression Scale) and higher levels of\nwell-being (measured by the World Health Organization\nWell-Being Index) compared with the control group. The\n\neffect was marked for those experiencing mild/moderate\nlevels of depression and anxiety, suggesting the clinical\nvalue of such low touch interventions. Overall, the study\ndemonstrates that empirical research through Randomized\nControl Trials (RCTs) can be conducted in challenging,\nFCV settings through appropriate rapid training of local\nresearchers and non-specialist providers (NSPs) at a low\ncost, yielding scalable programmatic and policy level lessons.\n\n\n\nThis paper is a product of the Finance, Competitiveness and Innovation Global Practice in collaboration with the Mind,\n\nBehavior and Development Unit. It is part of a larger effort by the World Bank to provide open access to its research", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000496:1:0:0", "start": 1078, "end": 1089, "surface": "pooled data", "probe_tag": "confusion", "probe_score": 0.7498, "luna_label": 1, "luna_reason": "Pooled follow-up data were analyzed to support treatment-group outcome findings."}]}, {"key": "aj-196", "text": " outcomes in the 1996/97 IAF:\nhospital visits, health facility visits, child immunizations, pregnancy controls, and\nmedically supervised deliveries. In all cases, the variables are of a binary nature, simply\nreflecting whether an individual reports having used a particular service or not. Sample and\nsubsample means are reported in Table 1 below.\n\n\nThe variables on hospital or health centre visits refer to a recall period of four\nweeks. Individuals were only asked about the use of curative care if they had reported an\nincidence of illness or injury during the recall period. <sup>23</sup> This suggests the use of selfreported illness as a criteria for need. However, similar to many other surveys in\ndeveloping countries, richer households (as measured by consumption) are more likely to\nreport having been ill in the last four weeks. This may be because richer households have a\nlower tolerance threshold for their definition of “ill” than do poorer households. Also,\nrecall of illness episodes may be related to education and formal treatment episodes. Both\n\n\n21 See Datt and others (2000) for details on the construction of consumption aggregates for the IAF.\n\n22 The IAF contains a number of other variables on assets and household characteristics that could\npotentially be used to construct a more discriminating index, or to better predict household expenditure.\nHowever, the purpose of this exercise is to look at how the use of a simple asset index, including the type\nthat can be constructed with DHS data, affects measured inequality relative to the use of consumption as a\nwelfare measure. For this reason, more complex indices are not considered.\n\n23 The questionnaire only permitted one care-seeking episode. If several consultations were made in the\nlast month, answers refer to the last consultation.\n\n\n10", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002612:11:1:0", "start": 1512, "end": 1520, "surface": "DHS data", "probe_tag": "keep", "probe_score": 0.9447, "luna_label": 1, "luna_reason": "DHS data are used to construct an index affecting measured inequality."}]}, {"key": "aj-197", "text": "In all regressions with female data, unless noted otherwise, the individual character\n\nistics included in _Xict_ are age, highest grade completed, and binary indicators for the\n\n\nfrequency of communication with mothers about SRH topics and whether the female’s\n\n\nhousehold (i.e., parents) owns the house in which she lives. We included these controls\n\n\nbecause they are strongly correlated with sexual activity and relationship status and im\n\nprove the precision of the estimates (Bruhn and McKenzie, 2009); however, the results\n\n\nare qualitatively similar if we do not include them. We estimate equation 1 for the whole\n\n\nanalysis sample and for sub-populations of interest, such as females who had ever had sex\n\n\nat baseline and females who had partners in the last two years at baseline.\n\n\n**4.1** **Baseline** **Balance** **and** **Follow-up**\n\n\nThe underlying identification assumption needed to interpret the results from an RCT\n\n\nas causal is that sample characteristics are balanced at baseline. We show balance for\n\n\nour baseline survey sample of 3,178 females for our primary outcomes in Table 1. In\n\n\ncolumns 1–2, we show balance for the cluster randomization, and we show balance for the\n\n\nindividually-randomized _Goal_ intervention in columns 3–4. Overall, the RCT appears to\n\n\nbe balanced across observed outcomes at baseline. Appendix Table B2 replicates Table\n\n\n1, including the _Supply_ arm differences. Of the 3,178 females in our baseline sample,\n\n\n2,591 were successfully tracked to the endline survey, an overall tracking rate of 81.5%.\n\n\nThis tracking rate is similar across survey treatments (81% in the control arm, 85% in\n\n\n_Boys_, and 80% of females invited to _Goal_ ). Table B3 presents baseline balance for this\n\n\nsub-sample. The treatment balance is maintained within this sub-sample. We", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001093:16:0:0", "start": 24, "end": 35, "surface": "female data", "probe_tag": "confusion", "probe_score": 0.4529, "luna_label": 0, "luna_reason": "Generic data phrase lacks an attributed finding or identified source."}]}, {"key": "aj-198", "text": ". A few observations:\n\n- Malthusian saving has the lowest proportion of false positives, but in fact the vast majority of\nthe countries with positive Malthusian saving are developed countries – the result is therefore\nunsurprising. This saving measure also has the highest proportion of false negatives, which is\nconsistent with the results of the quantitative analysis.\n\n- Gross and net saving have relatively low proportions of false negatives, but this represents\nvery few countries (only one in the case of gross saving) across all years. There are simply\nvery few countries with negative gross or net saving.\n\n- Genuine saving has lower proportions of false positives than either gross or net saving, but\nthis is balanced by a much higher proportion of false negatives.\n\n**Conclusions**\n\nGrowth theory provides the basis for a stringent test of whether saving does in fact\ntranslate into future welfare. This paper confronts the theory with ‘real world’ data, with positive\nresults at least for measures of gross and genuine saving. Even without appealing to theoretical\n\n5 This is clearly a rather _ad hoc_ test, but one that policy makers may care about.", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002791:6:1:0", "start": 947, "end": 963, "surface": "real world’ data", "probe_tag": "confusion", "probe_score": 0.1788, "luna_label": 1, "luna_reason": "Existing real-world data are used to test theory and support positive results."}]}, {"key": "aj-199", "text": "liability (=max(output VAT - input VAT, 0)). We use annual data for our main analysis and\n\n\nlater show robustness of our results using monthly data. This is because firms outside the large\n\n\ntaxpayer unit report taxable sales - a key outcome variable - only annually, and they report\n\noutput VAT and net liability monthly but retrospectively at the end of each year. <sup>21</sup> In our\n\n\npreferred specifications, we winsorize the outcome variables at the 99th percentile within each\n\n\ntreatment group _×_ year, and we confirm robustness of the results to alternative top-coding\n\n\napproaches.\n\n###### **5.2 Results**\n\n\nOur main DiD results are shown in Figure 3. Each column pertains to a different outcome\n\n\nvariable. In the top row, we show the normalized trends over time in the treatment and control\n\n\ngroup, and the DiD point estimate _β_ <sup>ˆ</sup> on the _Retaileri ·_ _PostReformt_ interaction from equation\n\n\n2. In the bottom row, we plot the period-specific _βk_ estimates from Equation 3 to confirm that\n\n\nwe cannot reject the parallel trends assumption.\n\n\nIf the expansion of electronic transactions triggered an improvement in tax compliance,\n\n\nit should first manifest through an increase in reported taxable sales. However, we observe\n\n\nparallel trends in this outcome and hardly any divergence between the treatment and the control\n\n\ngroup. We estimate that taxable sales in the treatment group increased only by an additional\n\n\n3.2 percent after the reform, compared to the control group, an effect which is statistically\n\n\nindistinguishable from zero (Figure 3, column A). The fact that reported sales do not change\n\n\ndifferentially in the treatment group after the reform, and that the statutory VAT rates did\n\n\nnot change, would imply that the output VAT remitted should also be unchanged. Indeed, we\n\n\nfind that the DiD point estimate on reported output", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000355:18:0:0", "start": 52, "end": 63, "surface": "annual data", "probe_tag": "confusion", "probe_score": 0.5579, "luna_label": 1, "luna_reason": "Existing annual data are used for the main analysis."}]}, {"key": "aj-200", "text": ") varying levels of water scarcity. Revenue is approximated using\ndata on utility drinking water and wastewater tariffs for a sample of over 80 countries in 2012 from the\nGWI. The authors estimate that water and sanitation subsidies provided through public utilities were\nabout $456 billion, or 0.6 percent of global gross domestic product (GDP), in 2012. Across regions,\nsubsidies range from between 0.3 percent and 1.8 percent of GDP. Note that these estimates include\nChina and India, the former with an estimate of around $130 billion, or about 1.5 percent of its GDP in\n2012. Without China and India, this estimate becomes 0.5 percent of global GDP, or, adjusted for general\nprice inflation from 2012 to 2017, $347 billion. Both of these numbers fall within our estimation range.\n\n\nWhile our estimates of subsidies for OPEX are relatively straightforward—they predominantly represent\nexplicit expenditures required to sustain service provision at current levels of efficiency and quality—our\nestimates of subsidies for CAPEX, or that required for the major repair and/or replacement of existing\ninfrastructure, require additional nuance. <sup>26</sup> [^26: It is again important to acknowledge that our estimation does not include CAPEX for infrastructure expansion. Since\ninfrastructure expansion tends to be fully subsidized, the actual global magnitude of networked WSS subsidies is much greater\nthan our estimation.] Because of a lack of data on most countries’ direct\nexpenditure on networked water and sewered sanitation, our model instead estimates the CAPEX\nrequired for the replacement of existing infrastructure. However, there have been several recent attempts\nto extrapolate direct expenditure from countries with more comprehensive and transparent expenditure\ndata to regional, and even global, levels of expenditure.\n\n\nPrior estimations of global and regional direct CAPEX on WSS services in low- and middle-income\ncountries, making use of data available from a limited number of countries, are between 0.4 and 0.5\npercent of GDP. Fay et al. (2017)", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000381:17:1:3", "start": 1960, "end": 2009, "surface": "data available from a limited number of countries", "probe_tag": "keep", "probe_score": 0.94, "luna_label": 1, "luna_reason": "Existing country data support prior CAPEX estimates of 0.4–0.5 percent of GDP."}]}, {"key": "aj-201", "text": "2011) and Ecuador (Calero, Bedi, and Sparrow 2009).\n\n#### **5 Financial capital and entrepreneurship**\n\n\nTemporary migration is also linked to increased investment and entrepreneurship back in\n\nthe home country. While there are studies on the increase in investment in assets, such\n\nas land in Pakistan (Adams Jr 1998) and El Salvador (Damon 2010), most papers focus\n\non the link between temporary migration and entrepreneurship. Seeking self-employment\n\nand entrepreneurship opportunities after return has been argued to be among the main\n\ndrivers of temporary migration: in the presence of credit constraints at home, temporary\n\nmigration allows individuals to accumulate savings faster and to engage in self-employment\n\nactivities when they return (Dustmann and Kirchkamp 2002; Rapoport 2002; Djaji´c 2010;\n\nBossavie, Gorlach, Ozden, and Wang 2021). This motive is especially relevant in developing\n\neconomies where credit constraints to entrepreneurship can be quite restrictive. Temporary\n\nmigration can thus allow to overcome the institutional void of missing capital markets in\n\norigin countries to accelerate business creation (Bossavie, Gorlach, Ozden, and Wang 2022).\n\n\nEmpirical evidence from various countries is consistent with the entrepreneurship chan\nnel. A first set of descriptive studies examine the relationship between temporary migration\n\nand self-employment after return. Savings levels have been found to be a significant factor\n\nin the choice of self-employment over waged employment among return migrants in Pak\nistan (Ilahi 1999) and Egypt (McCormick and Wahba 2001). In Tunisia, the majority of\n\nentrepreneurial projects started by returnees were totally financed through overseas savings\n\n(Mesnard et al. 2004). In Turkey, more than half of returnees from Germany are economically\nactive and engaged in entrepreneurial activities (Dustmann and Kirchkamp 2002). <sup>17</sup> Using\n\nhistorical data on return migrants to Norway from the United States, Abramitzky, Boustan,", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000712:28:0:0", "start": 1911, "end": 1945, "surface": "historical data on return migrants", "probe_tag": "confusion", "probe_score": 0.8517, "luna_label": 1, "luna_reason": "Historical migrant data are explicitly used for analysis in a named study."}]}, {"key": "aj-202", "text": " have\nlarger impacts in periods of slow growth and higher unemployment.\n\nA handful of papers have studied the impact of employment subsidies in the Turkish context.\nThe most closely related study to ours is the study by Betcherman et al. (2010) who estimate\nthe effect of two employment subsidies targeted to firms with more than 10 employees in\nTurkey. Similarly, as the subsidy studied in this paper, the subsidy was geographically\ntargeted which allowed the authors to carry out a difference-in-difference estimation to\nestimate the effects of the subsidy. However, contrary to our study, they use aggregate data\non labor market outcomes at the regional level to study those impacts. In addition, the subsidy\nwas at the time only targeted to firms of more than 10 employees. In addition, it was applicable\nonly to additional hires while the subsidy we study in this paper is applied to all employees.\nThey find that the subsidies had a sizeable and positive impact on registered employment.\n\nUysal (2013), Ayhan (2013), and Balkan et al. (2016) study the effect of another employment\nsubsidy in Turkey targeted to women and to youth introduced in 2018. All three papers carry\nout a difference-in-difference estimation to estimate the employment effects of the subsidy,\nwith Ayhan (2013) conducting a triple difference-in-difference strategy. They use nationally\nrepresentative survey data on workers from the Household Labor Force Survey (HLFS). The\nthree studies report different results on the impact of the subsidy, although they all find that\ntreatment effects tend to be larger for women. Uysal (2013) uses aggregate labor market data\nby demographic groups and emphasizes that the program has been effective for older rather\nthan younger women, while Ayhan (2013) uses micro-level data in order to control for\n\n\n6", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002194:7:1:0", "start": 601, "end": 615, "surface": "aggregate data", "probe_tag": "confusion", "probe_score": 0.8469, "luna_label": 1, "luna_reason": "Existing aggregate labor-market data are used to study subsidy impacts."}]}, {"key": "aj-203", "text": "expressed in terms of 1996 dollars. <sup>2</sup> We then divide it by 2000 GDP at PPP at constant 1996\n\n\ndollars. <sup>3</sup> The log of these ratios are then used as our measures for trade openness.\n\n\nWe use a rule-of-law index that is constructed by Kaufmann, Kraay and Zoido-Lobaton\n\n\n(2002) for the period 2000-01 and taken from the Dollar and Kraay dataset. The index is\n\n\nconstructed for 175 countries. It is constructed from indicators reflecting “the extent to which\n\n\nagents have confidence in and abide by the rules of society. These include perceptions of the\n\n\nincidence of both violent and non-violent crime, the effectiveness and predictability of the\n\n\njudiciary, and the enforceability of contracts” (Kaufmann, Kraay, and Zoido-Lobaton (2002),\n\n\npage 8).\n\n\nWe add as explanatory variables in the regression the natural logarithm of population as a\n\n\nproxy for market size, with data taken from the WDI. In addition we have a dummy variable for\n\n\ncountries that are land-locked and a variable on the distance of these countries from the equator,\n\n\nmeasured as absolute value of latitude of the country’s capital city.  Both are taken from the\n\n\nDollar and Kraay dataset (2002). We also use data on the legal origin of countries as well as data\n\n\non their proportions of English and European-languages speakers, drawn from Doing Business\n\n\ndataset and Dollar and Kraay, respectively. We start with the Doing Business dataset of 133\n\n\ncountries and delete those countries for which data are not available for our explanatory\n\n\nvariables. <sup>4</sup> This leaves us with a maximum of 108 countries in the levels regressions, listed in\n\n\nthe Appendix _(Table A1)_ .\n\n\n2 We deflate the data using the US GDP deflator for years 1996 and 2000. Dollar", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002557:8:0:2", "start": 338, "end": 362, "surface": "Dollar and Kraay dataset", "probe_tag": "keep", "probe_score": 0.9402, "luna_label": 1, "luna_reason": "Named dataset supplies the rule-of-law index used in regression analysis."}]}, {"key": "aj-204", "text": "#### Figure 5. International trade costs relative to domestic trade costs\n\nA. Average trade costs in 1995 and 2019 B. Average trade costs in EMDE regions\n\n\n\nPercent 1995 2019\n\n200\n\n\n150\n\n\n100\n\n\n50\n\n\n\n200\n\n\n150\n\n\n100\n\n\n50\n\n\n0\n\n\n\nPercent 1995 2019\n\n250\n\n\n\nEAP ECA LAC MNA SAR SSA\n\n\n\n0\n\n\n\nAdvanced economies EMDEs\n\n\n\nC. Average trade costs for agriculture in 1995\nand 2019\n\n\n\nD. Average trade costs for agriculture for\nEMDE regions in 1995 and 2019\n\nPercent 1995 2019\n\n400\n\n\n300\n\n\n200\n\n\n100\n\n\n\nPercent 1995 2019\n\n300\n\n\n\n250\n\n200\n\n150\n\n100\n\n50\n\n0\n\n\n\nAdvanced economies EMDEs\n\n\n\nEAP ECA LAC MNA SAR SSA\n\n\n\n0\n\n\n\nE. Average trade costs for manufacturing in F. Average trade costs for EMDE regions for\n1995 and 2019 manufacturing in 1995 and 2019\n\n\n\nPercent 1995 2019\n\n200\n\n\n\nPercent 1995 2019\n\n250\n\n\n\nEAP ECA LAC MNA SAR SSA\n\n\n\n150\n\n\n100\n\n\n50\n\n\n0\n\n\n\nAdvanced economies EMDEs\n\n\n\ns\n\n\n\n200\n\n\n150\n\n\n100\n\n\n50\n\n\n0\n\n\n\nSources: Comtrade (database); ESCAP-World Bank Trade Costs Database; World Bank; World Trade\nOrganization.\nNote: EMDEs = emerging market and developing economies, EAP = East Asia and Pacific, ECA =\nEurope and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North\nAfrica, SAR = South Asia, SSA = Sub-Saharan Africa. Bilateral trade costs (as defined in the\nUNESCAP/World Bank database) measure the costs of a good traded internationally in excess of the\nsame good traded domestically and are expressed as ad valorem tariff equivalent. Bilateral trade costs are\naggregated into individual country measures using 2018 bilateral country exports shares from the\nComtrade database. Regional and sectoral aggregates are averages of individual country measures. Bars\nshow unweighted averages, whiskers show interquartile ranges. Sample in 1995 includes 33 advanced\neconomies and 46 EMDEs (4 in EAP, 8 in ECA, 15 in LAC, 4 in MNA, 2 in SAR, and 13 in SSA).\nSample in 2019 includes", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001145:37:0:3", "start": 1588, "end": 1605, "surface": "Comtrade database", "probe_tag": "keep", "probe_score": 0.9723, "luna_label": 1, "luna_reason": "Comtrade data provide export shares used to aggregate trade-cost measures."}]}, {"key": "aj-205", "text": "8\n\n\n\neconomic characteristics as well as their residential status (migrant/nonmigrant), the\n\nduration of migrants’ stay (short/long-term) and their origin (rural/urban). The data cover\n\n\n\neconomic characteristics as well as their residential status (migrant/nonmigrant), the\n\nduration of migrants’ stay (short/long-term) and their origin (rural/urban). The data cover\n\nsix countries and come from censuses <sup>8</sup> [^8: The census data are obtained from Integrated Public Use Microdata Series (IPUMS), which publicly provides] and nationally representative household surveys\n\n\n\nduration of migrants’ stay (short/long-term) and their origin (rural/urban). The data cover\n\nsix countries and come from censuses <sup>8</sup> and nationally representative household surveys\n\n(Table 1).\n\n\n\n(Table 1).\n\n\n\nEmployment data are available for all six countries. The survey data for Ethiopia,\n\nTanzania, and Uganda have additional information on hours worked and individual wages\n\n\n\nEmployment data are available for all six countries. The survey data for Ethiopia,\n\nTanzania, and Uganda have additional information on hours worked and individual wages\n\n(for those employed). Household income and consumption data are further available for\n\n\n\nTanzania, and Uganda have additional information on hours worked and individual wages\n\n(for those employed). Household income and consumption data are further available for\n\nTanzania and Uganda. Each individual-level dataset also contains information on age, sex,\n\n\n\n(for those employed). Household income and consumption data are further available for\n\nTanzania and Uganda. Each individual-level dataset also contains information on age, sex,\n\neducation, and sector of employment (agriculture, manufacturing, or service).\n\n\n\neducation, and sector of employment (agriculture, manufacturing, or service).\n\n\n\neducation, and sector of employment (agriculture, manufacturing, or service).\n\nData on urban agglomerations come from Africapolis (OECD, 2020). It consistently\n\ndefines urban agglomerations as continuously built-up areas with a total population of", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001221:9:0:0", "start": 397, "end": 405, "surface": "censuses", "probe_tag": "confusion", "probe_score": 0.4411, "luna_label": 1, "luna_reason": "Census data are used and identified in the footnote as IPUMS sources."}]}, {"key": "aj-206", "text": " We retrieved historical tax filing records of household businesses from the official online portal of\n\n\nthe Department of Taxation. <sup>8</sup> Pending reporting delays, this information from the Tax Department allows us to\n\n\ntabulate a complete set of tax filings in the country at the commune-month level, between January 2018 and August\n\n\n2020. <sup>9</sup>\n\n\nEach household business is responsible to file and pay one annual lump-sum tax within a fiscal year. We use the\n\n\n(change in) tax filing numbers as a proxy for the (changes in) number of tax-registered household businesses each\n\n\nmonth. For brevity, we refer to this data set as the “household business tax census” (“HBTC”). Beyond the regular\n\n\nannual filings reported in January each year, the tax portal also keeps separate retrievable records of several types\n\n\nof irregular filings. We focus on two such indicators directly related to firm exit: the number of tax fillings for (i)\n\n\npermanent business closure, and (ii) temporary suspension. While the former provides a measure of firm survival\n\n\n6See Article 1, Decree 92/2015/ND-CP.\n7The license tax is a fixed proportion based on revenue brackets. VAT and PIT rates are set by the central government and vary by\nrevenue and industry. See Le et al. (2020) for further details on household business tax information.\n\n8The link to the online portal is `[http://www.gdt.gov.vn/wps/portal/home/hct](http://www.gdt.gov.vn/wps/portal/home/hct)` (accessed January 2022). The records can be retrieved\nby querying an administrative location up to the commune level (a third-tier administrative unit).\n\n9While several provincial tax offices have published household businesses tax records until the end", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001160:6:1:0", "start": 649, "end": 678, "surface": "household business tax census", "probe_tag": "confusion", "probe_score": 0.2799, "luna_label": 1, "luna_reason": "Named administrative tax dataset used to proxy household-business changes"}]}, {"key": "aj-207", "text": "### Contents\n\n1 Introduction 3\n\n\n2 Sources of Trade-offs 6\n\n\n3 Econometric Techniques 13\n3.1 A VAR Framework . . . . . . . . . . . . . . . . . . . . . . . . 13\n3.2 Cross-Country Regressions . . . . . . . . . . . . . . . . . . . . 18\n\n\n4 A Structural Approach 22\n4.1 Production and the Labor Market . . . . . . . . . . . . . . . . 22\n4.2 Link with a Household Survey . . . . . . . . . . . . . . . . . . 24\n4.3 Policy Shocks . . . . . . . . . . . . . . . . . . . . . . . . . . . 25\n4.3.1 Reduction in the minimum Wage . . . . . . . . . . . . 25\n4.3.2 Cut in Payroll Tax on Unskilled Labor . . . . . . . . . 28\n\n\n5 Concluding Remarks 30\n\n\nAppendix A: Variables Definition and Data Sources 32\n\n\nAppendix B: Other Features of Mini-IMMPA 34\n\n\nReferences 37\n\n\n2", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002590:1:0:0", "start": 349, "end": 365, "surface": "Household Survey", "probe_tag": "confusion", "probe_score": 0.0678, "luna_label": 0, "luna_reason": "Section heading merely names a generic survey without showing data use or findings."}]}, {"key": "aj-208", "text": "Again, our results are not sensitive to the use of different assumptions regarding the actual FCS increase\n\ngenerated by social protection programs since our focus here is on targeting accuracy.\n\n\nFinally, we simulate alternative selection mechanisms: random targeting and universal selection. Random\n\ntargeting will consist in selecting randomly X% of the households. The threshold is adjusted to provide a\n\nrelevant comparison with PMT or CBT selection in each case. Universal selection on the other hand will\n\ninclude all households but will deliver the same total amount of benefits as with other methods to keep\n\nprogram budget constant. This means that each household will receive a smaller amount of benefit\n\ncompared to PMT or CBT, but everyone will receive benefits.\n\n\n_4.3 Initial conditions and descriptive statistics_\n\n\nTable 2 shows initial FGT poverty indices in our nine data sets, confirming that most data sets have\n\nremarkably high levels of poverty, and poverty indices much higher than national poverty rates. This is\n\nlikely, in part, a result of geographical targeting, although comparisons based on different consumption\n\naggregates may be difficult. These initial conditions are important to keep in mind, as they may contribute\n\nto explain targeting results. Indeed, it is more difficult to select the poorest where “everyone is poor” (Ellis,\n\n2012). The data sets are relatively homogeneous in terms of consumption and food insecurity levels <sup>25</sup> .\n\nHowever, Senegal 2 households are better off, followed by Burkinabe 2 households. In these two data sets,\n\ngeographical targeting is not as narrow as in other data sets, which focus on smaller, poorer geographic\n\nareas. <sup>26</sup> The FCS is not as straightforward to compare across countries, but its highest values are also in\n\nSenegal 2 and Chad, followed by Niger 2. Cameroon is the country with the lowest average per-capita\n\nconsumption, but food security levels are even lower in Mali (HDDS).\n\n\n**5 Results**\n\n\n_5", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002140:16:0:0", "start": 886, "end": 895, "surface": "data sets", "probe_tag": "keep", "probe_score": 0.9417, "luna_label": 1, "luna_reason": "Table reports poverty indices and findings from the nine existing datasets."}]}, {"key": "aj-209", "text": "T_</sup> 1 <sup>+1</sup> _,_\n\n\n\n\n<sup>_T_</sup> 1 <sup>+1</sup> _, F_ ( _X_ 1 <sup>_T_</sup>\n\n\n\nThis setup makes the imputation a fully recursive process in which successive updates to the overall\ndata set after new observed data has become available is linked by initializing the new MCMC using the\noutput of the previous MCMC result. This makes the estimation highly suitable for real-time monitoring.\nThe initialization is only needed once when the price imputation process is deployed. After this, the\nmachine-learning imputation can run as a continuous MCMC process that integrates new observations\ngenerated by co-deployed surveys on the fly, and use the information to update imputations on an ongoing\nbasis.\n\n\n_A3._ _Volatility_ _estimation_\n\n\nThe ARMA-fiGARCH model is derived from the following time-varying density\n\n\n(A7) _Ft_ = ( _µt, σt, ϑ_ )", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000776:30:9:0", "start": 617, "end": 636, "surface": "co-deployed surveys", "probe_tag": "confusion", "probe_score": 0.8136, "luna_label": 0, "luna_reason": "Surveys generate new observations on the fly, so the data is being produced."}]}, {"key": "aj-210", "text": ":\n\nHucitec.\nWaldfogel, J., W. Han, and J. Brooks-Gunn. 2002. “The Effects of Early Maternal Employment\n\non Child Cognitive Development.” _Demography_ 30(2): 369-92.\nWalker SP, S.M. Grantham-Mcgregor, C.A. Powell, S.M. Chang. 2000. “Effects of Growth\n\nRestriction in Early Childhood on Growth, IQ, and Cognition at Age 11 to 12 Years and\nthe Benefits of Nutritional Supplementation and Psychosocial Stimulation.” _The Journal_\n_of Pediatrics_ 137(1): 36-41.\nWorld Bank. 2005a. _World Development Report 2006: Equity and Development_ . The World\n\nBank and Oxford University Press, New York.\nWorld Bank. 2005b. Development Database Platform. Washington, D.C.. Available online at\n\navailable at <u>http://www.worldbank.org/data/onlinedatabases/onlinedatabases.html;</u>\naccessed October 1, 2005.\n\n\n27", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:003082:27:2:0", "start": 608, "end": 637, "surface": "Development Database Platform", "probe_tag": "confusion", "probe_score": 0.5999, "luna_label": 0, "luna_reason": "Bibliography entry naming a database platform, without demonstrated data use."}]}, {"key": "aj-211", "text": "Figure 1: Malaria Incident, Antimalarial Prescriptions, and Match Between Underlying\nIllness and Treatment\n\n\n\n\n\n\n\n\n\n_Notes:_ Sample limited to the subset of patients who consented to take an RDT during the home follow-up\nsurvey in the Basic Training group. Panel A graphs malaria positivity (measured in home RDT) and receipt\nof antimalarial prescriptions (recorded during the clinic survey), by type of malaria test conducted at the\nclinic (recorded during the clinic survey). Overall positivity rate is 23.12% in the control group. Panel\nB graphs the match between true malaria status (measured in home RDT) and receipt of antimalarial\nprescription by type of malaria test at the clinic. The share of patients prescribed an antimalarial is 69.36%\nin the control group.\n\n\n33", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:001383:35:0:1", "start": 377, "end": 390, "surface": "clinic survey", "probe_tag": "confusion", "probe_score": 0.1633, "luna_label": 1, "luna_reason": "Clinic survey data are used to analyze antimalarial prescriptions and testing outcomes."}]}, {"key": "aj-212", "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, "luna_reason": "Named enterprise survey cited as an existing data source."}, {"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, "luna_reason": "Named 2011 survey cited as an existing source in the footnote."}]}, {"key": "aj-213", "text": ", boarding and alighting, traffic counts) to estimate origin/destination figures using the EMME\ntraffic simulation tool. The model assignment results were obtained using a generalized impedance, or\ncost function, which considers all times associated with the commute (in-vehicle, waiting, access to the\nnetwork, and transfer) and fares paid. The proposed tariffs are aligned with the existing fares in the GBA,\nbenchmarked against ongoing stated preference surveys (SPS), and willingness to pay, and assumed fare\nstructure is distance based.\n\n\n67. **The results of the analysis show strong net economic benefits under various scenarios that are**\n**robust to variations in project costs and benefits.** The following results are conservative estimations\nbased on the prefeasibility and are being updated with the recently received feasibility study calculations\nthat show higher traffic demand and modal shift. Based on the conservative prefeasibility analysis, about\n132,000 passengers are expected to board the BRT per day with 12,400 at morning peak hours. The BRT\ndemand is estimated to grow by 4 percent per year. Over a project lifetime of 20 years, the project is\nexpected to deliver an economic internal rate of return (EIRR) of 39 percent, which is above the discount\nrate threshold of 8 percent. The net present value (NPV) is estimated at US$919 million at a rate of 8\npercent. The results are robust against different assumptions with sensitivity tests conducted against cost\nrising by 25 percent, benefit decreasing by 25 percent, and both combined. As the proposed project\nprovides important GHG emissions’ savings, the economic benefits are further improved when accounting\nfor the social cost of carbon, with low or high shadow prices of carbon values.\n\n\nPage 21 of 59", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000022:32:1:0", "start": 439, "end": 464, "surface": "stated preference surveys", "probe_tag": "confusion", "probe_score": 0.7112, "luna_label": 1, "luna_reason": "Surveys benchmark proposed tariffs and willingness-to-pay assumptions."}]}, {"key": "aj-214", "text": "_Project:_ _Loiyangalani – Suswa Transmission Line_\n\n\n**Preamble**\n\n\nKETRACO’s corporate Environmental Policy set the vision for corporate governance of its business\nactivities. The Environmental and Social Management Plan (ESMP) provides a framework of\noperating policies and procedures through which KETRACO will develop and implement\nenvironmental, social, health and safety management systems and programmes that will establish\nthe foundation for governance of its activities. The ESMP outlines KETRACO’s corporate commitment\nto managing its businesses in a responsible, safe and sustainable manner whereby protection of the\nenvironment and safety of people take priority above all other business matters. The objective of\nthe ESMP is to provide each of KETRACO’s operational departments with clear direction and\nguidelines in order to develop a project specific ESMP that will ensure that KETRACO through its\nactivities will have minimal impact on the environment, its people and the country at large. The\nproject ESMP will contain specific action plans and programmes, policies, standards and procedures\nthat all KETRACO’s employees, consultants and contractors must adopt and adhere to when working\nunder KETRACO’s supervision.\n\n\n**Proposed Project**\n\n\nThe proposed Loiyangalani‐ Suswa transmission line will traverse 428 km from the proposed Lake\nTurkana Wind Farm site to the Suswa substation to be located to the northwest of Nairobi. Along\nthe way, the line will pass through Baragoi, Maralal, Rumuruti, Nyahururu, Gilgil, Naivasha and\nSuswa where it will connect to a 400kV double circuit busbar system/bay.\n\n\nAn initial census survey on the line was carried out in 2010 and reviewed 2011. The results of the\nsurvey indicate that the proposed transmission line trace will affect 1,250 titled land parcels and the\nmajority of the resettlement/relocation will be required within the southern portion between Suswa\nand Rumuruti.", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:018981:4:0:0", "start": 1633, "end": 1658, "surface": "census survey on the line", "probe_tag": "confusion", "probe_score": 0.4304, "luna_label": 1, "luna_reason": "Past census survey supports findings on affected titled land parcels."}]}, {"key": "aj-215", "text": "Policy Research Working Paper 8301\n\n##### **Abstract**\n\nOne-fifth of the world’s population lives in countries affected\nby fragility, violence and conflict, impeding long-term economic growth. However, little is known about how firms\nrespond to local changes in security, partly because of the difficulty of measuring firm activity in these settings. This paper\npresents a novel methodology for observing private sector\nactivity using mobile phone metadata. Using Afghanistan as\nthe empirical setting, the analysis combines mobile phone\ndata from over 2,300 firms with data from several other\nsources to develop and validate measures of firm location,\n\n\n\nsize, and economic activity. Combining these new measures\nof firm activity with geocoded data on violent events, the\npaper investigates how the private sector in Afghanistan\nresponds to insecurity. The findings indicate that firms\nreduce presence in districts following major increases in\nviolence, that these effects persist for up to six months, and\nthat larger firms are more responsive to violence. The paper\nconcludes with a discussion of potential mechanisms, firms’\nstrategic adaptations, and implications for policymakers.\n\n\n\nThis paper is a product of the Office of the Chief Economist, South Asia Region and the Macroeconomics, Trade and\nInvestment Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and\nmake a contribution to development policy discussions around the world. Policy Research Working Papers are also posted\non the Web at http://econ.worldbank.org. The corresponding author may be contacted at tghani@wustl.edu.\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_names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:007253:1:0:0", "start": 435, "end": 456, "surface": "mobile phone metadata", "probe_tag": "confusion", "probe_score": 0.6966, "luna_label": 1, "luna_reason": "Metadata are used to measure firm activity and analyze responses to insecurity."}]}, {"key": "aj-216", "text": " only due to unavailability of data for December.<br>*** Figures may include citizens of Montenegro in the absence of separate statistics available for Serbia and for Montenegro.<br>**** UNHCR estimates.<br>***** Combination of number of cases (DHS) and persons (EOIR).|Origin<br>Poland<br>Portugal<br>Rep. of<br>Korea<br>Romania<br>Serbia<br>Slovakia<br>Slovenia<br>Spain<br>Sweden<br>Switzerland<br>TfYR<br>Macedonia<br>Turkey<br>United<br>Kingdom<br>United<br>States*****<br>Afghanistan<br>5<br> <br>-<br> <br>6<br> <br>28<br> <br>76<br> <br>25<br> <br>8<br> <br>18<br> <br>646<br> <br>214<br> <br>6<br> <br>317<br> <br>725<br> <br>41<br> <br>Russian Federation<br>1,850<br> <br>*<br>-<br> <br>-<br> <br>-<br> <br>17<br> <br>*<br>12<br> <br>292<br> <br>79<br> <br>-<br> <br>-<br> <br>35<br> <br>190<br> <br>Somalia<br>-<br> <br>-<br> <br>-<br", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000810:29:58:0", "start": 187, "end": 202, "surface": "UNHCR estimates", "probe_tag": "confusion", "probe_score": 0.3889, "luna_label": 1, "luna_reason": "UNHCR estimates provide the source for the table's reported figures."}]}, {"key": "aj-217", "text": "**Access issues**\n\n\n9. Access to ECE programs i s far fiom universal. The poorer population is less likely to be\nenrolled, either due to non-availability o f ECE services or due to costs. There i s a strong\ncorrelation between GDP per capita and KG GER in Egypt: analysis o f government statistical\ndata show that the poorest govemorates in Upper and Lower Egypt have the lowest KG\nenrollment rates (Janssens et al, 2001). Disparities in KG enrollment rates can be large: in\npoorer, rural govemorates, KG enrollment is approximately 10 percent o f children, as compared\nto KG enrollment rates o f 25 to 42 percent o f children for the relatively wealthier, urban\ngovernorates. The population o f KG-aged poor children will be expanding at a faster rate in\nrural areas for years to come.\n\n\n10. Current charges for KG are now typically higher than family costs for primary and\npreparatory levels. In addition to basic fees (about LE120), families incur other costs when they\n\nsend children to public KG programs. Private costs o f items such as uniforms are now estimated\nat LE168 for government primary schools. In poor, rural areas where families are generally\nlarger, the current costs o f “free” government education can become prohibitive.\n\n\n11. Disadvantaged populations may already be using some forms - f childcare for 4 and **5** year\nolds; expanding their coverage and improving their quality so they promote early education will\nimprove the chances that these children will be prepared for formal schooling.\n\n\n12. The private sector does not operate in disadvantaged areas due to lack o f incentives and\nrestrictive regulations. Private and NGO sector expansion (with support fiom public funds and\nthrough incentives) would help expand access to KG programs across the country.\n\n\n**Quality issues**\n\n\n13. There is currently an oversupply o f ECE teachers in urban areas, and an undersupply", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000040:27:0:0", "start": 276, "end": 303, "surface": "government statistical\ndata", "probe_tag": "confusion", "probe_score": 0.8504, "luna_label": 1, "luna_reason": "Government statistical data support analysis linking poverty and kindergarten enrollment."}]}, {"key": "aj-218", "text": "* **As a result of the speculative housing market, land and real estate prices have risen sharply.** Land prices\nin Beirut increased an exorbitant 600 percent from 2003 to 2013, while real estate prices inflated 200 percent. <sup>79</sup>\n\n\n72 Marot, “Jadaliyya - The End of Rent Control in Lebanon: Another Boost to the ‘Growth Machine?’”\n73 UN-Habitat, “Guide for Mainstreaming Housing in Lebanon’s National Urban Policy.”\n74 UNDP, “Leave No One Behind for an Inclusive and Just Recovery Process in Post-Blast Beirut”; UN-Habitat, “Lebanon Urban Profile,” 2011.\n75 Gebara, Khechen and Marot, “Mapping New Constructions in Beirut (2000-2013).”\n76 Since its independence in 1942, the Lebanese State has rarely engaged in the production of public housing or introduced measures to protect\nor secure affordable housing for low-income groups such as property regularization and neighborhood upgrading\n77 Beirut Urban Lab, “Beirut: A City for Sale?”\n78 MoF, “Public Finance Monitor.”\n79 InfoPro, “Business Opportunities in Lebanon – Year XI. Real Estate in Greater Beirut [Database],” 2014.\n\n\nPage 50 of 66", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000012:55:2:0", "start": 1038, "end": 1078, "surface": "Real Estate in Greater Beirut [Database]", "probe_tag": "confusion", "probe_score": 0.8369, "luna_label": 1, "luna_reason": "Named database cited as source for reported real estate price increases."}]}, {"key": "aj-219", "text": "will support sustainable capacity development and institutional strengthening to implement PSNP 4.\n\nComponent 1: Systems Development:\n\nComponent 1 will support the strengthening of the social protection and DRM systems and the\ntransition from independent programs to a system of integrated social protection and DRM service\ndelivery. Supporting the transition to a system will entail support the key building blocks, tools and\ninstruments of the systems, including for harmonized planning, targeting, single registry and\ninformation management across social protection programs and associated policies. For the DRM\nsystem, support will be focused on the development and implementation of appropriate response\nmechanisms for transitory needs, including early warning triggers and harmonized planning and\nmonitoring.\n\nThe current community-based PSNP targeting system, which has worked very well particularly in\nhighlands regions, will be retained and supplemented by a PMT-based poverty index. This will form\nthe basis for the development of a unified registry database which will support harmonized targeting\nacross various social protection interventions. Such a registry could bring together beneficiary data\nacross different programs serving the same clients and harmonize PSNP beneficiary targeting with\nother social protection programming within the country. This will enable the provision of a suite of\nservices (for instance, PSNP transfers as well as fee waivers for health services) to the same\nbeneficiaries, identification of gaps in support, and analysis of the impact of different services. The\nregistry will initially focus on PSNP areas, and expand over time to a true national registry.\n\nHarmonized information management will support both the social protection and DRM agendas. It\nwill entail harmonizing M&E systems with common indicators for related programs (e.g. public\nworks and sustainable land management, pastoral community development), and a harmonized M&E\ndata collection and analysis system. In addition, a program-specific MIS will be established, of\nwhich the single registry will be one element, to ensure effective knowledge management. A\ncomprehensive public works database will also be a crucial part of the MIS. Establishing such an\nMIS system will", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:012171:3:0:0", "start": 1043, "end": 1068, "surface": "unified registry database", "probe_tag": "confusion", "probe_score": 0.4203, "luna_label": 0, "luna_reason": "Planned registry database to be developed for future harmonized targeting"}]}, {"key": "aj-220", "text": "with all relevant stakeholders to manage the process of data production. (IOM, 2018). These are hard\n\n\nchallenges, but non-conventional data sources offer such great potential in complementing (not in\n\n\nsubstituting) survey data that, at some point, these challenges will have to be faced. Our assessment,\n\n\ntherefore, is that while these challenges and drawbacks currently make their potential combination with\n\n\nhousehold surveys challenging in the short-run, these sources appear promising for the future, especially\n\n\nbecause of their ability to provide a large amount of more timely and granular data.\n\n#### **6. Conclusions**\n\n\n<mark>It is easy to envisage that, as climate change intensifies, the climate change-migration nexus will keep</mark>\n\n\n<mark>gaining prominence in the international agenda. In parallel, the empirical literature will keep growing.</mark>\n\n\n<mark>Further refinements to the conceptual framework, as well as developments in econometric techniques,</mark>\n\n\n<mark>will shed new light on the relationship between these complex, multifaceted phenomena. However, all</mark>\n\n\n<mark>of this cannot provide concrete benefits for policy making without closing the existing data gaps.</mark>\n\n\nBased on the empirical review, we have identified the main data gaps on the climate-migration nexus.\n\n\nUsing a household survey program currently at the forefront of methodological research, the LSMS-ISA\n\n\nproject, we then identified the limitations and opportunities for household survey data to enhance our\n\n\nunderstanding of the causal relationship. A summary of this assessment is reported in Table 1, which\n\n\nprovides a list of the most relevant conceptual and empirical issues, with the corresponding data gaps\n\n\nand a set of initial proposals to boost the potential of household surveys such as the LSMS-ISA. <mark>We have</mark>\n\n\n<mark>stressed that household surveys currently allow limited exploration of the climate-migration nexus. At</mark>\n\n\n<mark", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000662:36:0:3", "start": 1490, "end": 1511, "surface": "household survey data", "probe_tag": "confusion", "probe_score": 0.7254, "luna_label": 0, "luna_reason": "Generic survey data is mentioned without an attached concrete finding or claim."}]}, {"key": "aj-221", "text": " and 2.7 million (26%) in 2011.\nIndividual accommodation has increased in the\npast four years. At the end of 2015, some 67 per cent\nof refugees lived in individual accommodation. This is\nthe highest such proportion ever recorded and compares to 63 per cent in 2014, 58 per cent in 2013, and\n54 per cent in 2012 ~~TABLE 6 .~~ The increase is driven\n\n\n\n**Table 5 Demographic characteristics of refugees**\n| 2003-2015 (% of total population)\n\n\n\n\n\n\n\nThe percentages are based on available data and exclude countries where\nno demographic information is available. This is in particular the case for\nindustrialized countries.\n\n\nby the rising proportion of Syrian refugees among all\nrefugees, nearly all of whom (97% of those for whom\nthere is data) live in individual accommodation.\n\nAt the end of 2015, about 56 per cent of the total refugee population in rural locations resided in a\nplanned/managed camp, compared with 2 per cent\nwho resided in individual accommodation. In urban\nlocations, the overwhelming majority (99 per cent)\nof refugees lived in individual accommodation, compared with less than 1 per cent who lived in a planned/\nmanaged camp.\n\nIn May 2016, UNHCR launched the ‘Nobody Left\nOutside’ campaign to tackle the urgent needs for\nshelter among 2 million refugees around the world.\nClearly, robust disaggregated data on the refugee\npopulation and where and how they are living is key\nto the efficient implementation, monitoring, and evaluation of this campaign. ●\n\n\n**47** Excludes data that were reported as unknown or unclear\n(2.3 million).\n\n\n\n**Table 6 Accommodation of refugees** | 2013-2015 (end-year)\n\n\n\n\n\n|Type of<br>accommodation<br>Planned/<br>managed camp<br>Self-settled camp<br>Collective centre<br>Individual<br>accommodation<br>(private)<br>Reception/", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:001367:51:1:0", "start": 475, "end": 489, "surface": "available data", "probe_tag": "confusion", "probe_score": 0.5821, "luna_label": 1, "luna_reason": "Available data directly supports the reported percentages."}]}, {"key": "aj-222", "text": "/u>_\n[27 Global Partnership for Education, https://www.globalpartnership.org/education/the-benefits-of-education](https://www.globalpartnership.org/education/the-benefits-of-education) _._\n28 This morbidity assumption is based on proxy data used in other countries in Sub-Saharan Africa for refugee camps and host communities\n(e.g., Somalia, Zimbabwe, South Sudan, Kenya).\n29 Kip Viscusi, W., and Clayton J. Masterman. “Income Elasticities and Global Values of a Statistical Life.”\n<u>[https://law.vanderbilt.edu/phd/faculty/w-kip-viscusi/355_Income_Elasticities_and_Global_VSL.pdf](https://law.vanderbilt.edu/phd/faculty/w-kip-viscusi/355_Income_Elasticities_and_Global_VSL.pdf)</u>\n30 O&M costs for infrastructure vary widely. These costs include water treatment, road resurfacing, building maintenance, cleaning, energy and\n<u>staff costs. As such, the analysis uses a conservative assumption of five percent of the investment costs for annual O&M.</u>\n\n\nPage 17", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000188:32:3:0", "start": 230, "end": 240, "surface": "proxy data", "probe_tag": "confusion", "probe_score": 0.6545, "luna_label": 1, "luna_reason": "Existing proxy data supports the morbidity assumption for refugee camps and host communities."}]}, {"key": "aj-223", "text": ": Rationalize Health Sector Expenditures. One of the objectives of this component was\nto improve the efficiency of MOPH hospital expenditures through the Automation of the Billing\nSystem (ABS) and the establishment of a system for performance-based contracting with hospitals.\nii) The Lebanon Emergency Primary Health Care Restoration Project (P152646), which aims\nat strengthening the capacity of the MOH to provide essential services to poor Lebanese most\naffected by the Syrian crisis. An important component of this project is to strengthen the Health\nInformation System (HIS) that comprises of: (i) design of a program database; (ii) development of\nregisters and forms to gather data (enrollment registers, provider data collection forms); (iii)\ndevelopment of the PHC contracting and claims processing system; (iv) collection and analysis of\nprogram indicators; and (v) design of wider monitoring and verification activities.\niii) The WB- CAS Household Survey (2010-2012).\n\nThe proposed program is divided into four main components:\n\nComponent 1: Upgrade the role of the MoPH Statistics Department ($139,500).\n\nIn the absence of a policy unit at the MOPH, and considering the difficulty of introducing a new unit\nto the current MoPH organizational structure, this component aims to assist the MOPH re-define the\nmain role and functions of the SD and lay the ground for an effective and efficient organizational\n\n\nPage 3 of 7", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000046:2:1:0", "start": 941, "end": 965, "surface": "WB- CAS Household Survey", "probe_tag": "confusion", "probe_score": 0.8831, "luna_label": 1, "luna_reason": "Named existing household survey is cited as a data resource."}]}, {"key": "aj-224", "text": " targets were documented in Annex\n3 of the 8th mission aide-mémoire. Despite these discussions, formal target revisions were not pursued through a level 2\nrestructuring, rendering those discussions unofficial. Due to some miscommunication and confusion, the M&E team began\nusing the revised, higher targets as the new RF, and the Bank team updated its operational portal accordingly. As a result,\nthe project operated based on the unofficially raised targets. Therefore, it is important to acknowledge the oversight in\nM&E management.\n\n57. **M&E Utilization: The RF was a valuable tool for monitoring the project's progress and clearly demonstrated the**\n**project’s achievements** . A series of impact evaluations conducted using M&E data, additional surveys of agro-dealers, and\nsoil testing facilitated knowledge sharing on ACDP effectiveness (see Annex 6.D). M&E data and findings played a crucial\nrole in informing subsequent interventions and are expected to significantly influence future strategies. The development\nof ICT tools under Component 4 and their associated output measures are additionally linked to enhancing the effects of\ne-vouchers and matching grants.\n\n**Justification of Overall Rating of Quality of M&E**\n58. **The quality of M&E was** **_Substantial_** **.** Despite the noted oversight concerning indicator 1, the project's M&E\nsystem successfully gathered extensive, pertinent, and significant data periodically. The RF allowed for effective tracking\nand assessment of project progress and results, aiding in informed decisions and necessary adjustments throughout the\nproject's implementation.\n\n\n**B.** **ENVIRONMENTAL, SOCIAL, AND FIDUCIARY COMPLIANCE**\n59. **Environmental Safeguard compliance was rated** **_Moderately Satisfactory_** . The project established an\nEnvironmental and Social Management Framework (ESMF", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:005441:21:1:0", "start": 731, "end": 739, "surface": "M&E data", "probe_tag": "confusion", "probe_score": 0.3448, "luna_label": 1, "luna_reason": "M&E data were used in impact evaluations informing knowledge sharing."}]}, {"key": "aj-225", "text": " as well as year fixed e↵ects.\n\n\nelasticity estimate in the 2SLS without Addis Ababa fixed e↵ects is 5.7 percent (column 3). Adding\n\n\nAddis Ababa fixed e↵ect reduces the elasticity to 3.2 percent, which is not statistically significant.\n\n\nThese results imply that the wage premium of large towns is driven by Addis Ababa.\n\n\n**5.5** **Robustness** **Check**\n\n\n**Excluding** **Recent** **Migrants**\n\n\nAlthough our analysis has instrumented the town population size, an endogeneity bias due to the\n\n\nsorting of workers is a lingering concern. To assess the bias, we tap into the migration information\n\navailable in our data. <sup>11</sup> We reestimate our OLS and 2SLS models by excluding recent migrants\n\n\nto towns to highlight agglomeration e↵ects on the stayers. We expect this to reduce the size of\n\n\nthe estimated agglomeration e↵ects if sorting has contributed to the e↵ects. Table 9 summarizes\n\n\nthe results. As expected, excluding rural-to-urban migrants (columns 2 and 5) and both rural-to\n\nurban and urban-to-urban migrants (columns 3 and 6) lower the absolute values of the coefficient\n\n\nestimates for the log of town population. Nevertheless, we still observe significant associations\n\n\nbetween town population size and the probability of engaging in self-employment jobs.\n\n\n11Unfortunately, only the LFS has such migration information, reducing the sample size for this analysis.\n\n\n19", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:002374:20:3:1", "start": 1311, "end": 1314, "surface": "LFS", "probe_tag": "confusion", "probe_score": 0.5485, "luna_label": 1, "luna_reason": "LFS migration information is used to reestimate models excluding recent migrants."}]}, {"key": "aj-226", "text": "Serious allegations of abuse, self-harm and neglect of asylum seekers in relation to the Nauru Regional Processing\n\nCentre, and any like allegations in relation to the Manus Regional Processing Centre\n\nSubmission 43\n\n\n38. Several factors suggest that the rate of self-harm (reported to be 0.3 per cent for actual self\nharm, and 0.1 per cent for threatened self-harm for 2015) <sup>28</sup> is an under-estimate:\n\n\na) First, the rate of _actual_ self-harm reported by International Health and Medical Services\n\n\n(IHMS) is greater than the rate of _threatened_ self-harm, an inversion of the usual pattern.\n\n\nThis inversion is only seen when suicide is unexpected and there is no identification\n\n\napproaches for self-harm. In the Lombrum ‘Regional Processing Centre’ system, there are\n\n\ninstitutional processes for handling suicidal action and threat which were in evidence on the\n\n\nteam’s monitoring mission. Therefore, one would expect threats of self-harm to outnumber\n\n\nactual self-harm.\n\n\nb) Secondly, the reported rate of actual self-harm is significantly less than the rates reported in\n\ncomparable populations or in other immigration detainee populations. <sup>29</sup>\n\n\nc) Thirdly, the medical experts gathered oral evidence during the surveys of numerous first\n\nhand accounts of self-inflicted lacerations, voluntary refusal of food and fluid, and deliberate\n\n\noverdosing of stockpiled medications. Although unverified, this would have grossly\n\n\nexceeded the reported rate.\n\n\n39. The medical experts observed that the data supplied to them relating to self-harm on Manus\n\n\nIsland is inconsistent with international data on comparator populations, as explained above,\n\n\nand that both the collection and analysis of the data may not accurately reflect the true rate of\n\n\nself-harm.\n\n\n28 In addition to concerns about reporting of incidents, the data provided to UNHCR by International Health and\nMedical Services is poorly defined and the summative rates are incorrectly calculated. International Health", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000609:13:0:0", "start": 1610, "end": 1628, "surface": "international data", "probe_tag": "confusion", "probe_score": 0.4878, "luna_label": 1, "luna_reason": "International comparator data supports a finding about inconsistent self-harm rates."}]}, {"key": "aj-227", "text": " of 2023 . Jordan has made\nsignificant strides in recent years on fiscal consolidation, but debt levels remain elevated. General government debt (which\nnets of the Social Security Investment Fund (SSIF) holdings) increased to 88.8 percent of GDP in 2022, up from 87.5 percent\nin the previous year, and is expected to reach 88.7 percent in 2023.\n\n**2.** **With its small economy and domestic market, Jordan’s growth and job creation need to be driven by private sector**\n**investment and exports; however, key structural constraints to growth remain entrenched, notably those related to**\n**the labor market and the business environment** . Despite recent reforms, the private sector still faces barriers to entry\nand a challenging environment, resulting in low productivity and investment levels. Unemployment is high (hovering\naround 22.3 percent in Q3 2023 overall and Jordan’s labor force participation rate is low (32.6 percent in Q3 2023),\nespecially for women (13.5 percent for Jordanian women, one of the lowest in the world). Labor market segmentation\nbetween the public and private sectors and across gender and nationality, persistent informality, a large gender gap, and\nstructural constraints on employment for migrants prevent the full utilization of Jordan’s human capital wealth. According\nto the latest official 2017/2018 poverty estimates, Jordan’s poverty rate, at 15.7 percent, is relatively low compared to\nother countries in the Middle East and North Africa (MENA) region. However, vulnerability is increasing and there is also\nsignificant variation in poverty across the country. In 2019, Jordan initiated a successful reform and expansion of its social\n[protection system. It is now the largest in the MENA region in terms of coverage of the poorest.](https://documents1.worldbank.org/curated/en/099607206022335877/", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000181:10:1:0", "start": 1328, "end": 1355, "surface": "2017/2018 poverty estimates", "probe_tag": "confusion", "probe_score": 0.8563, "luna_label": 1, "luna_reason": "Official poverty estimates support the stated 15.7 percent poverty rate."}]}, {"key": "aj-228", "text": " World\nBank/MDTF-financed PFS, which uses a combination of geographic targeting, community-based\ntargeting, and proxy means testing (PMT), with some filters mainly related to the presence of\nchildren below 12 years and pregnant women. <sup>21</sup> [^21: For more details on the targeting methodology, see Supplemental Attachments, Targeting Methodology available in the project\nfile.] The project will rely on the refugee household\ndata collected by WFP and UNHCR in August 2017 using tools closely aligned with the CFS\nharmonized questionnaire. The targeting methodology will be tested to ensure its applicability to\nrefugees and adapted as needed. Moreover, these activities will be complemented by a strong\ncommunications campaign designed in partnership with humanitarian agencies to ensure that the\nprogram is seen as fair to both refugees and host communities.\n\n30. **Sub-component 2.1 aims to increase households’ consumption for a period of two**\n**years.** Regular small cash payments will enable beneficiaries to stabilize their general household\nconsumption levels. Cash transfers will be delivered on a quarterly basis in full alignment with\nthe ongoing PFS, implemented by the _Cellule Filets Sociaux_, which is already delivering cash\ntransfers to 6,200 households in Logone Occidentale and Bahr-El-Ghazel (in addition to managing\na cash-for-work program in N’Djamena). Households will be added to the program on a gradual\nbasis throughout with three consecutive rounds of interventions starting in the East, moving to the\nSouth after one year, and then to the Lake region. The cash transfers will be accompanied by\nmeasures being developed under the PFS to improve human development indicators, with a focus\non early childhood development.\n\n31. **Sub-component 2.2 (US$5.2 million equivalent) will seek to increase household**\n**resilience and self-reliance** **in targeted areas through small grants to selected households to**\n**support productive and climate-smart income-generating activities**", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000035:22:1:0", "start": 415, "end": 437, "surface": "refugee household\ndata", "probe_tag": "confusion", "probe_score": 0.3297, "luna_label": 1, "luna_reason": "Existing WFP and UNHCR household data informs refugee targeting."}]}, {"key": "aj-229", "text": "**Consultancy Services for Design Review, Preparation of Concept Design and**\n**<u>Tender Document For Nekempt – Bure Road DESIGN-BUILD- MAINTENANCE Project         Ethiopian Roads Authority</u>**\n\n\n  - The unit rate (or bill of quantity) is estimated on the basis of the type of the affected\nhouses. The cost of each house that would be replaced is estimated on the basis of\nspecification and bill of quantities prepared by the municipalities of each town and\nverified by the valuation committee and PAPs; Irrespective of the location of the area,\nthe unit costs for the similar types of houses are taken to be identical;\n\n  - Estimate the disturbance allowance. The disturbance allowance is considered to cover\nthe loss of established businesses, and include social disruptions and inconveniencies.\nDisturbance allowance will be used by PAPs to cover expenses associated with\nrelocation including access to social and public services.\n\nThe compensation payment for houses, fences and other structures affected by the project as\ndescribed above shall take place at full replacement cost. The project affected households and\ninstitutions that would be relocated from their current location will receive compensation\nequivalent to the location advantage they might lose due to the project.\n\n\nPAPs losing part of their housing plot that is located either in rural or urban areas will be\nallowed to retain the remaining portion of the plot to construct new houses and as long as it is\nlocated outside of the ROW.\n\nThe formula adopted as per proclamation 135/2007 shows;\n\n\n  - Cost of construction (current value);\n\n  - Cost of permanent improvement on land;\n\n  - Amount of refundable money for the remaining term of lease contract.\n\n\n**_Valuation for permanent Loss of Agricultural Land / Crop Loss_**\n\nThe principle for permanent loss of agricultural land /crop loss/ is that it should be\ncompensated with land for land compensation (or land for land replacement) in those areas\nwhere land is available for replacement. In the extent at which the agricultural land lost", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:011792:35:0:0", "start": 376, "end": 412, "surface": "specification and bill of quantities", "probe_tag": "confusion", "probe_score": 0.593, "luna_label": 0, "luna_reason": "Project valuation paperwork, not an external data resource used as evidence."}]}, {"key": "aj-230", "text": " the effect of flexibility/autonomy,\n\nand the effect of individual recommendations/specificity are measured in addition on the effect\n\nof the grant. Unless otherwise stated, we use the study sample of 678 farmers and we focus on\n\nintent-to-treat (ITT) estimates and examine spillovers (and hence possible SUTVA violations)\n\nin Section 5.2. In cases where the dependent variable is sometimes missing we report whether\n\nthere is differential attrition, and if so, provide Lee bounds (Lee, 2009).\n\n#### **5 Short-Term Results**\n\n###### **5.1 Take Up**\n\n\nTable 5 uses the sample of farmers that received soil analyses and recommendations during\nthe intervention (arms _T_ 1 _−_ _T_ 4) and examines the take up of the precision drill during sowing\n\n(column 1), the two fertilizer packages (columns 2 and 3), attendance at AEW group meetings\n\nand the total number of AEW plot visits (columns 5 and 6). Column 7 uses as the dependent\n\nvariable the sum of the dependent variables in columns 1-3, 5 and 6 while column 8 uses a\n\nstandardized index of the outcome in column 7. The take up of these items was verified both\n\nfrom farmer reports as well as administrative data and as a result, mis-classification is not an\n\nimportant concern. The penultimate row of Table 5 also reports the mean of the dependent\nvariable among farmers in _T_ 4 (with the corresponding standard error). The control group is\n\nnot included in these regressions as no intervention was offered to them.\n\n\n28Note that we can recover the overall impact of being in any treatment arm by combining the _β_ -coefficients. Thus,\nthe test that _T_ 1 = 0 is equivalent to _βE_ + _β_ _I_ + _βG_ = 0,", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:000088:18:1:1", "start": 1143, "end": 1162, "surface": "administrative data", "probe_tag": "confusion", "probe_score": 0.7099, "luna_label": 1, "luna_reason": "Administrative data verifies reported intervention take-up and reduces misclassification concern."}]}, {"key": "aj-231", "text": " were from Côte\nd’Ivoire (237, constituting 15% of Ivoirian arrivals)\nand Syria (211, constituting 26% of Syrian arrivals).\nMost Ivoirian women crossed the sea to the Spanish\nmainland while most Syrian women crossed through\nthe border crossing point to Melilla. Most children\narriving in Spain were Syrian (368, comprising 46%\nof Syrian arrivals), an illustration of how most Syrian\narrivals continued to be family groups.\n\nTo get to Spain, Syrians have used a diverse range\n\n\n54 Based on the nationality breakdown provided by Spanish authorities.\nAs of the end of June, data for 2017 included 823 persons from Sub-Saharan\nAfrica whose nationalities had not yet been determined\n\n\n\nof routes. While some have been resident for some\ntime in Algeria, others have reported travelling from\nLebanon, Turkey, Jordan and Egypt and using a\nvariety of routes including through countries such as\nSudan, Mauritania, Mali, Algeria and finally Morocco\nin order to avoid a dangerous sea journey to Italy.\nMany have told UNHCR that they have travelled\nthis way to reunify with family members already in\nEurope because of the lack of accessibility of formal\nfamily reunification mechanisms.\n\nIn April, a group of 41 Syrians, including two\npregnant women in need of medical care, as well as\ninfants and children, became stranded at the border\nbetween Morocco and Algeria near Figuig. <sup>55</sup> The\nsituation was resolved after seven weeks when the\n28 remaining members of the group were granted\nentry to Morocco. <sup>56</sup>\n\nTo cross the sea to Spain, most use inflatable boats\nusually holding between 35 and 40 persons. The\njourney is risky and already this year 52 people are\nbelieved to have died or gone missing at sea as\nof the end of June. In the first six months of 2017,\nmost deaths occurred in the Straits of Gibraltar\nbut several deaths have also occurred during the\nlonger sea journey in the Alboran", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000189:12:1:0", "start": 571, "end": 584, "surface": "data for 2017", "probe_tag": "confusion", "probe_score": 0.6091, "luna_label": 0, "luna_reason": "Bare date-only data qualifier cannot inherit the adjacent 823-person figure."}]}, {"key": "aj-232", "text": "year school survey.|\n|Responsibility for Data<br>Collection<br>|MEP Department of Statistics<br>|\n|**IRI 3.2.2 Teachers and school administrators in schools with significant migrant intake receive training on adequately meeting needs of migrant**<br>**students**|**IRI 3.2.2 Teachers and school administrators in schools with significant migrant intake receive training on adequately meeting needs of migrant**<br>**students**|\n|Description|This indicator measures readiness of schools to receive migrant intake as the number of teachers and schools<br>administrators trained on migrant student needs.|\n|Frequency<br>|Annual<br>|\n|Data source<br>|MEP Department of Statistics|\n|Methodology for Data<br>Collection<br>|Annual end-of-year school survey.|\n|Responsibility for Data<br>Collection<br>|MEP Department of Statistics<br>|\n|**Component 4: Project Management and Technical Assistance**|**Component 4: Project Management and Technical Assistance**|\n\n\n\nPage 30", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "refugee_pads:000189:39:2:0", "start": 0, "end": 18, "surface": "year school survey", "probe_tag": "confusion", "probe_score": 0.182, "luna_label": 0, "luna_reason": "Standalone table methodology fragment describing an annual school survey"}]}, {"key": "aj-233", "text": "2</sup> <mark>After decades of insecurity, ab</mark> out 7.2 million people (60 percent of the\npopulation) are in ‘Crisis’ (IPC <sup>3</sup> [^3: IPC = Integrated Food Security Phase Classification.] Phase 3) or even more acute situations of food insecurity. <sup>4</sup> [^4: IPC. South Sudan IPC Results October 2020–July 2021.] As of November\n2021, over 4 million people, 85 percent of them women and children, <sup>5</sup> [^5: United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) November 2021.] were displaced, many of them\nmore than once. About 2.3 million people have fled to neighboring countries in search of safety, while 2\nmillion remain to be displaced within South Sudan. <sup>6</sup> [^6: UNOCHA November 2021.]\n\n\n3. **A sharp decline in international oil prices triggered by the pandemic combined with devastating**\n**floods has eroded most of the economic gains from the peace process, but there are signs of modest**\n**recovery following rising oil prices and government reforms.** The economy contracted by an estimated\n5.4 percent in FY2020/21 due to the COVID-19 pandemic, decline in global oil prices, resurgence of\nviolence, and major flooding. In FY2021/22, South Sudan’s economy is expected to grow by 1.2 percent\nreflecting stronger oil prices, rebounding optimism in the non-oil sectors, and stronger domestic and\nregional trade following crippling COVID-19 restrictions in the previous year. The overall FY2020/21\nbudget deficit is estimated to have narrowed to about 6.9 percent of gross domestic product (GDP) from\n9.8 percent in FY2019/20. South Sudan’s fiscal position benefited from higher-than-projected oil revenue,\nimproved domestic revenue mobilization,", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "jdc_operational:000028:13:1:0", "start": 282, "end": 305, "surface": "South Sudan IPC Results", "probe_tag": "confusion", "probe_score": 0.8655, "luna_label": 1, "luna_reason": "IPC results source the reported food-insecurity figures."}]}, {"key": "aj-234", "text": "be consistent with the objectives of the 2000 CAS and specifically with the government’s health sector\nstrategy, especially its desire to integrate traditional with modern medicine, by gaining a clear\nunderstanding of the beneficial and harmful aspects of traditional plant-based medicines.\n\n\n55. On a more specific level, the project was designed to close critical gaps in information, such as the\nlack of quantitative data on the potential economic benefits of developing medicines from indigenous\nplants; uncertainty over the precise location of “hotspots” where medicinal plant biodiversity was\nvanishing most rapidly; the lack of a structured database on national medicinal plants, including\nindigenous knowledge of these plants; and the lack of socioeconomic studies that could clarify the factors\naccelerating the loss of biodiversity. Without this essential information, efforts to develop and implement\nmeasures to deal with genetic erosion and promote the sustainable use of medicinal plants would have\nbeen held back for many years. The project drew on the experience of World Bank-supported projects;\nand on projects funded by the World Heath Organization, United Nations Development Programme, the\nGEF, the Netherlands, and the World Wildlife Fund (specifically, the Integrated Conservation and\nDevelopment Project in the Bale Mountains); to make specific proposals for strengthening government\nmeasures in the project area, where open access to medicinal plants would soon eradicate any benefit\ngained from their increased use.\n\n\n56. The project adopted and institutionalized a participatory approach to conservation, but it suffered\ndelays because collaboration among the large group of stakeholders proved cumbersome. To compensate\nfor the limited capacity for project implementation within Ethiopia, the project developed training\nprograms that would help the lead institutions respond to the problems of field staff and farmers.\nConditions for credit effectiveness and disbursement included the appointment of key staff and\npreparation of a work program for the first project year, including procurement, technical assistance, and\ntraining programs. Finally, the project was appraised by a team of highly qualified experts with strong\npractical experience in developing countries.\n\n**(b) Quality of Supervision**", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:008658:35:0:0", "start": 637, "end": 685, "surface": "structured database on national medicinal plants", "probe_tag": "confusion", "probe_score": 0.6383, "luna_label": 0, "luna_reason": "States a database is lacking, without using existing data or substitute estimates."}]}, {"key": "aj-235", "text": "spouse by cohort of entry (see table 8). <sup>45</sup> The only group for which we find a statistically impor\ntant impact on employment (after accounting for multiple hypotheses testing) are spouses (who are\n\nfemales in 98% of the cases), who were not employed before 2002, and who (potentially) entered\n\nthe program after 2004. We reject the null that the effects on early and later cohorts are the same\n\n(p-value is 0.02). This suggests that CS promotes the employment of married women. With an\n\naverage participation of 30%, female labor force participation in Chile is low by Latin American\n\nstandards, overall and among the poorest sections of the population. In this context, CS coupled\n\nwith the support of employment programs could constitute an important avenue to improve fe\nmale labor market participation. The expansion of access to child care might have enhanced these\n\nimprovements, although there are no reasons to expect that the access to public day care centers\n\nexhibits a discontinuity at the effective cutoffs we estimate (Medrano, 2009).\n\n\n**Other Outcomes** We also study program impacts on four other sets of outcomes: housing, ed\nucation, health and variables associated with behaviors. The effects we find in most of these\n\noutcomes are not statistically significant (even without adjusting inference for multiple hypothesis\n\ntesting).\n\n\nThere are seven variables related to housing conditions (collected from the CAS), related to\n\nconnections to the water and sewage networks, ownership of the house, and quality of the walls\n\nand ceilings (see table A.9 in Appendix A). We also analyze nine variables (collected from the FPS)\n\nconcerning school enrolment of children, health coverage of children and adults in the household,\n\nand participation in employment centers. The variables where we find a positive impact of par\nticipation in CS indicates that all adults aged 65 or above in the family have completed health\n\ncheck-ups (see", "source": "general_prwp", "subset": "annotate_aj", "spans": [{"key": "prwp:006257:36:0:0", "start": 1440, "end": 1443, "surface": "CAS", "probe_tag": "keep", "probe_score": 0.9298, "luna_label": 1, "luna_reason": "CAS data provide housing-condition variables analyzed for program impacts."}, {"key": "prwp:006257:36:0:1", "start": 1649, "end": 1652, "surface": "FPS", "probe_tag": "confusion", "probe_score": 0.7542, "luna_label": 1, "luna_reason": "FPS data are analyzed for impacts on enrollment, health coverage, and employment participation."}]}, {"key": "aj-236", "text": "armed groups. The situation has worsened by the lack of presence of international\norganizations and NGOs in many areas. While UNHCR is located in the border\nregion, other UN agencies are not present.\n\n**Enhanced registration**\n\nIn September of 2008 the government of Ecuador enacted a new asylum policy, with\nthe objective to strengthen and modernize Ecuadorian asylum policy and reaffirm the\ncommitment of the new government to international human rights. With a reformed\nconstitution and a new asylum policy Ecuador committed itself to strive for the\nfulfillment of all international and regional instruments relative to refugee protection,\nincluding the Cartagena Declaration.\n\nThe Ecuadorian General Directorate for Refugees (GDR) is the governmental entity\nresponsible for the Refugee Status Determination (RSD) process in Ecuador. The\nGDR has a main office in Quito and a branch office in Cuenca. GDR Eligibility\nOfficers based in Quito undertake interviewing and notification brigades in order to\nprocess the claims of asylum-seekers in the different provinces. The missions to\nprovinces serve to compensate for the lack of permanent GDR presence in these\nregions.\n\nIn March of 2009 the government of Ecuador launched the Enhanced Registration\nProgram (ERP) with the objective of providing RSDs and documentation to large\nnumbers of refugees that lack access to the normal registration channels. The\nEnhanced Registration Process implemented with UNHCR was created with the\nintention to provide greater access to asylum application process in regions where\nrefugees are located. Under the ERP the DGR mobile teams are utilized for an\nexpedited process that takes one day and provides status determination and one year\nrenewable visas.\n\nDecisions are taken by an Eligibility Commission and documentation is issued on the\nsame day. A representative of UNHCR participates in all sessions of the Eligibility\nCommission in an advisory capacity (with a voice but without vote) and submits\nrecommendations on each individual case, based on interviews conducted by GDR\neligibility officers. The database incorporates special needs tools that allow UNHCR\nto find out about specific needs of highly vulnerable refugees. UNHCR uses the\nHeightened Risk Identification Tool (HRIT) to", "source": "reliefweb", "subset": "annotate_aj", "spans": [{"key": "reliefweb:000910:5:0:0", "start": 2094, "end": 2135, "surface": "database incorporates special needs tools", "probe_tag": "confusion", "probe_score": 0.3513, "luna_label": 1, "luna_reason": "Database tools support identifying specific needs of highly vulnerable refugees."}]}, {"key": "aj-237", "text": "**Monitoring and Evaluation Arrangement**\n\n\n162. In order for monitoring and evaluation to be completed, several different types of data will be\ncollected during the six-year project period. This data will together allow for reporting of the results\nindicators and also be used for the purposes of impact evaluation.\n\n\n163. For _Component_ _1,_ the primary source of data will be the entry forms for new applicants and the\nassessments of beneficiaries who are due for recertification. The information on new applicants and also\non beneficiaries due for recertification will be collected by the district offices, which will enter them into\nthe electronic databases and then transmit the information to the branch offices and eventually the head\noffice in Sana’a. The forms and assessments will obtain all the information necessary for the application\nof the PMT method. The district offices will collect the data on a timely basis and provide them to the\nbranch offices without significant delay. The PMT method will be applied to the data at the main office\nin order to classify households in one of the six PMT groups. The Monitoring and Evaluation Department\nat the SWF will ensure that the data are tabulated in time to meet the monitoring requirements of the\nproject.\n\n\n164. There will also be regular monitoring to ensure that the cash transfer and beneficiary development\nprocesses are being implemented in a manner that ensures targets in the Results Framework (Annex 4) are\nmet. Specifically, it will be the responsibility of the branch (i.e. Governorate) offices to compile monthly\nstatistics on the number of beneficiaries it has had contact with, the number of new applications received\nand processes annually, the number of appeals received and responded to annually, and also the number\nof beneficiaries who receive various BDP services (including health and education services, skills\ntraining, and access to microcredit). The statistics will be provided by the district offices to the branch\noffices, which will then report the statistics to the Monitoring and Evaluation Department at the SWF", "source": "refugee_pads", "subset": "annotate_aj", "spans": [{"key": "sample:refugee_pads:000080:56:0:0", "start": 643, "end": 663, "surface": "electronic databases", "probe_tag": "confusion", "probe_score": 0.2355, "luna_label": 0, "luna_reason": "Project data will be collected and entered into the databases"}, {"key": "sample:refugee_pads:000080:56:0:1", "start": 1583, "end": 1601, "surface": "monthly\nstatistics", "probe_tag": "confusion", "probe_score": 0.1461, "luna_label": 0, "luna_reason": null}]}, {"key": "aj-238", "text": "**Annex 1:** **Project** **Design** **Summary**\n\n**SIERRA LEONE:** **NATIONAL** **SOCIAL ACTION** **PROJECT**\n\n\n. **Hierarchy o.Qbijctives -'**  - . **diator** **r**, t **,P** **jCitidaI** **r!** **As's-umptIons,Y-**\n**Sector-related** **CAS** **Goal:** **Sector** **Indicators:** **Sector/ country reports:** **(from** **Goal** **to Bank** **Mission)**\nMitigate the risk of renewed 1. National conflict/security- - UNHCR/OCHA reports - Continued peace and\n**conflict** **and lay foundation** related indicators - Household Income and regional security\n**for** **poverty reduction and** 2. Inter-regional disparities in Expenditure Surveys - Economic and political\n**improvements** **in nutrition,** I-PRSP & PRSP core - PETS surveys stability\n**health, education** **and** indicators - Strategic Planning and\n**targeting** **the rural** 3. Inter-regional disparities in Action Process (SPP) reports\n**population,** women **and** Popular Benchmarks\n**children.**\n\n\n**Project** **Development** **Outcome** **/** **Impact** **Project reports:** **(from** *", "source": "fcv_pads_east_africa", "subset": "annotate_aj", "spans": [{"key": "fcv_pads_east_africa:010325:29:0:0", "start": 590, "end": 639, "surface": "Inter-regional disparities in Expenditure Surveys", "probe_tag": "confusion", "probe_score": 0.6578, "luna_label": 0, "luna_reason": "Indicator entry inside an objectives matrix, not an actual survey-data use."}]}, {"key": "aj-239", "text": " per<br>year<br>|Project<br>monitoring<br>system<br>|Internal validation of<br>accessibility of<br>monitoring tool<br>|Project Coordination<br>Team<br>|\n|External users of monitoring tool for<br>access to bread satisfied with information<br>provided|Level of satisfaction<br>reported by external users<br>of monitoring tool for access<br>to bread|<br>Twice per<br>year<br>|Monitoring<br>tool for<br>access to<br>bread<br>developed<br>under the<br>project<br>|Online survey of<br>external users of the<br>monitoring tool<br>|Project Coordination<br>Team<br>|\n|External users of monitoring tool for<br>access to animal feed satisfied with|Level of satisfaction<br>reported by external users|Twice per<br>year|Monitoring<br>tool for|Online survey of<br>external users of the|Project Coordination|\n\n\nPage 42 of 54", "source": "jdc_operational", "subset": "annotate_aj", "spans": [{"key": "sample:jdc_operational:000024:46:1:0", "start": 459, "end": 472, "surface": "Online survey", "probe_tag": "drop", "probe_score": 0.0306, "luna_label": 0, "luna_reason": null}]}]