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+{"input": "Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka and\n\nGuillaume (2018a). The data are primarily based on sub-national GDP per capita data constructed by Gennaioli,\n\nIs the relationship between rainfall and GDP explained by agriculture? We use data from the ESA CCI project\n\n\nto determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split\n\ndifferent weights. Population is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).\n\nThe work compares food prices subnationally and finds that increasing prices are the most significant factor driving recent food insecurity but documents strong spatial heterogeneity in both market price dynamics, as well as relations to food insecurity, and provides examples of markets where prices are more sensitive to localized shocks. 4For instance, the International Finance Statistics (IFS) data base of the IMF reports monthly price data at Consumer Price Index (CPI) component level, but few developing countries report food price data without substantial delays.", "output": {"entities": {"named_data": ["International Finance Statistics (IFS) data base of the IMF"], "organization": ["IMF"]}, "relations": [{"has_organization": {"head": "International Finance Statistics (IFS) data base of the IMF", "tail": "IMF"}}]}, "_meta": {"entry_id": 164, "dataset_name": "International Finance Statistics (IFS) data base of the IMF"}}
+{"input": "The National Skills Development Survey, conducted by the Employment and Social Policy Institute in 2023, provides crucial insights into workforce trends and skills demands across various sectors. This dataset is utilized extensively by the Ministry of Labor to inform policy decisions and strategic planning, ensuring that programs are tailored to address current labor market needs.", "output": {"entities": {"named_data": ["National Skills Development Survey"], "organization": ["Employment and Social Policy Institute", "Ministry of Labor"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "National Skills Development Survey", "tail": "Employment and Social Policy Institute"}}, {"used_by": {"head": "National Skills Development Survey", "tail": "Ministry of Labor"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "employment, labor markets, and skills development"}}
+{"input": "average historical payout, the total number of historical payouts, and the standard deviation of rainfall. Historical variables are calculated for the period 1962-2004. Observations are weighted by the quality of rainfall data. If there are no rainfall stations contributing to the APHRODITE data in 2004 within a .75°x.75° grid around the desired BASIX weather station, the observation is given a weight of 1. If there is a least one weather station in this .75°x.75°, the observation is given a weight of 1.5. If there is a rainfall station within the .25°x.25° grid, the observation is given a weight of 2. The weighted results to not differ significantly from the unweighted results. The number of observations is 733 out of a total of 949 villages in the sample in 2005, as APHRODITE data was available for only a subset of locations. All specifications include state fixed effects. Errors clustered at the weather station level. ***p _<_ 0.01, **p _<_ 0.05, *p _<_ 0.1. _Source_ : Authors’ analysis based on data from BASIX and APHRODITE. Column 1 presents the baseline regression, which shows that villages that experienced a rainfall shock in 2004 actually had an average of 3.8 _fewer_ purchasers in 2005.", "output": {"entities": {"named_data": ["APHRODITE data"], "organization": ["Authors"]}, "relations": [{"used_by": {"head": "APHRODITE data", "tail": "Authors"}}]}, "_meta": {"entry_id": 1209, "dataset_name": "APHRODITE data"}}
+{"input": "confirmed the results found in international literature presented above. The most important result is that Ukrainian refugees who are fluent in Polish earn a net wage premium of about PLN 700 (+16% relative to refugee net median wage, PLN 1,000 gross wage) when compared to those with beginner language skills. This result is stable across different model specifications. Note that such an earnings gain would bring the median net wage of a Ukrainian refugee (estimated based on the SEIS UNHCR survey in chapter 2) from 80% to 98% of the median in the economy as a whole (or from 80% to 93% according to Ukrainian refugee’s median in the NBP’s 2024 survey), almost closing the gap to the economy as a whole in these terms. It is in fact higher than the PLN 500 median net wage premium of the pre-war Ukrainian migrants over Ukrainian refugees in the NBP (2024) survey, even though 68% of the former and only 28% of the latter said they had a high level of fluency in Polish.\n\n31", "output": {"entities": {"named_data": ["SEIS UNHCR survey"], "organization": ["UNHCR", "NBP"]}, "relations": [{"has_organization": {"head": "SEIS UNHCR survey", "tail": "UNHCR"}}, {"used_by": {"head": "SEIS UNHCR survey", "tail": "NBP"}}]}, "_meta": {"entry_id": 1330, "dataset_name": "SEIS UNHCR survey"}}
+{"input": "nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016).", "output": {"entities": {"named_data": ["Living Standards Measurement Study"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "Living Standards Measurement Study", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 985, "dataset_name": "Living Standards Measurement Study"}}
+{"input": "Recent studies have highlighted significant trends in population growth and fertility rates. The findings from the Global Fertility Assessment provide insights into the changing family structures across various regions. Similarly, the Demographic Dynamics Report elaborates on age distribution and migration patterns that further influence demographic changes. These reports are vital for understanding the broader implications for socio-economic planning.", "output": {"entities": {"named_data": ["Global Fertility Assessment", "Demographic Dynamics Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "demographics, fertility, and population growth"}}
+{"input": "The Geospatial Land Use Survey (GLUS) conducted in 2020 provides critical insights into land utilization patterns across the Sub-Saharan region. This dataset was produced by the African Development Institute (ADI) and is utilized extensively by the United Nations Environment Programme (UNEP) for environmental assessments. Additionally, the Remote Sensing Data Repository (RSDR) from 2018 has been an essential resource, published by the Global Geo-Analytics Organization (GGAO), which focuses on satellite imagery analysis. UNEP has also cited the RSDR while preparing reports on climate change impacts in coastal zones. Finally, the Urban Expansion Assessment 2021, released by the World Resources Institute (WRI), highlights urban growth trends within major cities in East Africa and is being leveraged by both local governments and international NGOs for urban planning initiatives.", "output": {"entities": {"named_data": ["Geospatial Land Use Survey", "Remote Sensing Data Repository", "Urban Expansion Assessment 2021"], "organization": ["African Development Institute", "United Nations Environment Programme", "Global Geo-Analytics Organization", "World Resources Institute"], "acronym": ["GLUS", "RSDR"], "year": ["2020", "2018", "2021"], "geography": ["Sub-Saharan region", "coastal zones", "East Africa"]}, "relations": [{"has_organization": {"head": "Geospatial Land Use Survey", "tail": "African Development Institute"}}, {"used_by": {"head": "Geospatial Land Use Survey", "tail": "United Nations Environment Programme"}}, {"has_acronym": {"head": "Geospatial Land Use Survey", "tail": "GLUS"}}, {"has_timeframe": {"head": "Geospatial Land Use Survey", "tail": "2020"}}, {"has_organization": {"head": "Remote Sensing Data Repository", "tail": "Global Geo-Analytics Organization"}}, {"used_by": {"head": "Remote Sensing Data Repository", "tail": "United Nations Environment Programme"}}, {"has_acronym": {"head": "Remote Sensing Data Repository", "tail": "RSDR"}}, {"has_timeframe": {"head": "Remote Sensing Data Repository", "tail": "2018"}}, {"has_organization": {"head": "Urban Expansion Assessment 2021", "tail": "World Resources Institute"}}, {"used_by": {"head": "Urban Expansion Assessment 2021", "tail": "local governments"}}, {"used_by": {"head": "Urban Expansion Assessment 2021", "tail": "international NGOs"}}, {"has_timeframe": {"head": "Urban Expansion Assessment 2021", "tail": "2021"}}, {"has_geography": {"head": "Urban Expansion Assessment 2021", "tail": "East Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "destination. Building on a growing literature documenting the relationship between subjective welfare and relative income, Fafchamps and Shilpi (2008) show that Nepalese households care about their consumption level relative to that of others in the same location. If this is the case, it is conceivable that migrants choose their destination not so much for the absolute gain in income it may provide but for the gain in relative status that would ensue. For instance, if returns to education and ability are higher in an urban setting, an educated individual may improve his relative position in society by moving from a rural to an urban setting. To investigate this possibility, we estimate equation (4) using the log of relative income (or relative consumption) as dependent variable and construct a predicted relative income measure using the same formula (5). These are shown in the second panel of Table 1. Theories of work migration predict that individuals move to increase their utility or welfare. The 1995 / 96 NLSS asked respondents a number of questions regarding their subjective satisfac- tion level with various dimensions of consumption — namely, food, clothing, housing, health care, and child schooling. They were also asked their subjective satisfaction with their level of total income.", "output": {"entities": {"named_data": ["NLSS"], "organization": ["Fafchamps and Shilpi"]}, "relations": [{"used_by": {"head": "NLSS", "tail": "Fafchamps and Shilpi"}}]}, "_meta": {"entry_id": 449, "dataset_name": "NLSS"}}
+{"input": "The recent analysis of public financial management in developing countries draws on insights from the Domestic Revenue Collection Survey and the Public Expenditure Review data. These sources provide valuable context to understand the challenges faced by governments in mobilizing domestic resources effectively.", "output": {"entities": {"named_data": ["Domestic Revenue Collection Survey", "Public Expenditure Review data"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "public financial management and domestic revenue"}}
+{"input": "Self-reported data from this survey suggest that firms in Dar es Salaam, Dodoma, and Tanga incurred at least $7.6 million in direct losses and damages due to flooding on business premises in 2018.\n\nThe data collected through this survey suggest that the primary reasons for general supply and delivery delays in Tanzanian firms are upstream supply chain issues.\n\nRentschler et al. (2019a) build a microdata set of about 143,000 firms to estimate the monetary costs of infrastructure disruptions in 137 low- and middle-income countries.\n\nThe lack of reliable and resilient infrastructure systems causes economic efficiency losses. A global study\nby Rentschler et al (2019) highlights the substantial drag that unreliable infrastructure imposes on firms\nin developing countries. In Tanzania, firms are incurring estimated utilization losses of nearly $670 million\na year (1.8 percent of national GDP) from power and water outages and transport disruptions. The firm\nsurvey collected for this study allows us to estimate the share of these utilization losses that are caused\nby natural shocks.\n\nWe use consumption and price data from the National Risk and Vulnerability Assessment (NRVA) 2007/08, conducted by the Government of Afghanistan Central Statistics Organization and the Ministry of Rural Rehabilitation and Development. The survey was administered between August 2007 and September 2008 and covered over 20,500 households (over 150,000 individuals) in 2,572 communities in all 34 provinces of Afghanistan.", "output": {"entities": {"named_data": ["National Risk and Vulnerability Assessment"], "organization": ["Government of Afghanistan Central Statistics Organization", "Rentschler et al."]}, "relations": [{"has_organization": {"head": "National Risk and Vulnerability Assessment", "tail": "Government of Afghanistan Central Statistics Organization"}}, {"used_by": {"head": "National Risk and Vulnerability Assessment", "tail": "Rentschler et al."}}]}, "_meta": {"entry_id": 327, "dataset_name": "National Risk and Vulnerability Assessment"}}
+{"input": "68.8% 2024-Q2 2021-Q2 67.5% 2022-Q2 40.3 40.3 40.4 40.4 Source: Deloitte own elaboration based of Eurostat data (Labour Force Survey).\n\nSource: Deloitte own elaboration based of Eurostat data (Labour Force Survey).\n\nPart-time Full-time 31 Deloitte has not received data that would be detailed as to citizenship, poviat, sex, age group, occupational group, and ZUS insurance code that would be suitable for econometric approach.\n\n42", "output": {"entities": {"named_data": ["Eurostat data"], "organization": ["Deloitte"]}, "relations": [{"used_by": {"head": "Eurostat data", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1312, "dataset_name": "Eurostat data"}}
+{"input": "Survey (UNHS) and the 2009/10 Uganda National Panel Survey (UNPS), both implemented\n\nThe 2005/06 UNHS covered all the districts in Uganda surveying 7,421 households\n\nfrom 783 Enumeration Areas. The 2009/10 UNPS collected information on 2,975 households", "output": {"entities": {"named_data": ["Survey (UNHS)"], "organization": ["UNHS"]}, "relations": [{"has_organization": {"head": "Survey (UNHS)", "tail": "UNHS"}}]}, "_meta": {"entry_id": 947, "dataset_name": "Survey (UNHS)"}}
+{"input": "According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men.", "output": {"entities": {"named_data": ["Gallup World Poll"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Gallup World Poll", "tail": "World Bank"}}]}, "_meta": {"entry_id": 541, "dataset_name": "Gallup World Poll"}}
+{"input": "The East Africa Social Safety Net Survey (EASSN Survey) conducted in 2020 provides valuable data on safety net programs across the region. This comprehensive dataset, produced by the East African Community (EAC), captures information from five countries: Kenya, Uganda, Rwanda, Tanzania, and Burundi. In addition, the National Food Security Assessment Report 2021 (NFSAR 2021) offers insights into food security dynamics, although it is primarily utilized by local government agencies rather than international organizations. Meanwhile, the Social Protection Policy Review (SPPR) from 2019 provides a detailed analysis of existing social safety strategies in various African nations, but it does not have an associated acronym or a specific geographical focus.", "output": {"entities": {"named_data": ["East Africa Social Safety Net Survey", "National Food Security Assessment Report 2021", "Social Protection Policy Review"], "organization": ["East African Community", "local government agencies"], "acronym": ["EASSN Survey", "NFSAR 2021", "SPPR"], "year": ["2020", "2021", "2019"], "geography": ["Kenya", "Uganda", "Rwanda", "Tanzania", "Burundi", "African nations"]}, "relations": [{"has_acronym": {"head": "East Africa Social Safety Net Survey", "tail": "EASSN Survey"}}, {"has_timeframe": {"head": "East Africa Social Safety Net Survey", "tail": "2020"}}, {"has_geography": {"head": "East Africa Social Safety Net Survey", "tail": "Kenya"}}, {"has_geography": {"head": "East Africa Social Safety Net Survey", "tail": "Uganda"}}, {"has_geography": {"head": "East Africa Social Safety Net Survey", "tail": "Rwanda"}}, {"has_geography": {"head": "East Africa Social Safety Net Survey", "tail": "Tanzania"}}, {"has_geography": {"head": "East Africa Social Safety Net Survey", "tail": "Burundi"}}, {"has_timeframe": {"head": "National Food Security Assessment Report 2021", "tail": "2021"}}, {"has_acronym": {"head": "National Food Security Assessment Report 2021", "tail": "NFSAR 2021"}}, {"has_timeframe": {"head": "Social Protection Policy Review", "tail": "2019"}}, {"has_acronym": {"head": "Social Protection Policy Review", "tail": "SPPR"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "social protection and safety nets"}}
+{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer.", "output": {"entities": {"named_data": ["Afrobarometer"], "organization": ["M ¨ uller-Crepon et al. (2020)"]}, "relations": [{"used_by": {"head": "Afrobarometer", "tail": "M ¨ uller-Crepon et al. (2020)"}}]}, "_meta": {"entry_id": 509, "dataset_name": "Afrobarometer"}}
+{"input": "REFodet = αod + γe + τt + β1Conflictot − 1 + β2Conflictet − 1 + β3Distanceed + ϵodet, (5) where REFodet is the stock of refugees of ethnic group e from country o in country d at year t. As we have data on yearly refugee stocks and would like to estimate the changes in these stocks over time using a gravity model, we include origin – destination fixed effects αod so that identification is based only on changes in stock over time (Zylkin, 2019). 20 We also include time τt and ethnic group fixed effects γe. Here we obtain data on the ethnicity of refugees from Murdock ’ s Atlas, which provides a map of ethnographic regions for Africa and the historical homelands of refugees (Murdock, 1967). To match ethnic groups across datasets, we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer.", "output": {"entities": {"named_data": ["EPR-ER dataset"], "organization": ["Murdock"]}, "relations": [{"used_by": {"head": "EPR-ER dataset", "tail": "Murdock"}}]}, "_meta": {"entry_id": 747, "dataset_name": "EPR-ER dataset"}}
+{"input": "The recent Labor Market Trends Assessment Report 2022 provides insight into the evolving employment landscape across various sectors in developing countries. This report, produced by the International Labour Organization, highlights the challenges and opportunities faced by job seekers and employers alike, aiming to inform policy decisions and improve labor market outcomes.", "output": {"entities": {"named_data": ["Labor Market Trends Assessment Report 2022"], "organization": ["International Labour Organization"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Labor Market Trends Assessment Report 2022", "tail": "International Labour Organization"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "employment, labor markets, and skills development"}}
+{"input": "The recent findings from the Global Learning Assessment (GLA) conducted across multiple nations highlight significant disparities in educational achievements. The GLA, a comprehensive study published by the Education Research Institute, provides valuable insights into student performance during the 2022 academic year. Specifically, the assessment included data from countries such as Brazil and India, underscoring the need for targeted educational reforms. In contrast, data from the National School Enrollment Survey (NSES) 2023, which focuses on enrollment rates in Africa, offers a different perspective by emphasizing growth in primary school attendance. The contrasting timelines and geographical coverage of these datasets shed light on the broader challenges faced in education globally.", "output": {"entities": {"named_data": ["Global Learning Assessment", "National School Enrollment Survey"], "organization": ["Education Research Institute"], "acronym": ["GLA", "NSES"], "year": ["2022", "2023"], "geography": ["Brazil", "India", "Africa"]}, "relations": [{"has_acronym": {"head": "Global Learning Assessment", "tail": "GLA"}}, {"has_timeframe": {"head": "Global Learning Assessment", "tail": "2022"}}, {"has_geography": {"head": "Global Learning Assessment", "tail": "Brazil"}}, {"has_geography": {"head": "Global Learning Assessment", "tail": "India"}}, {"has_acronym": {"head": "National School Enrollment Survey", "tail": "NSES"}}, {"has_timeframe": {"head": "National School Enrollment Survey", "tail": "2023"}}, {"has_geography": {"head": "National School Enrollment Survey", "tail": "Africa"}}, {"has_organization": {"head": "Global Learning Assessment", "tail": "Education Research Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The Global Refugee Trends Report 2022, published by UNHCR, provides comprehensive data on the state of forced displacement worldwide. According to this report, over 26 million refugees were recorded in 2022, a staggering figure that highlights the need for immediate action. The findings from the report are used by various organizations, including the International Rescue Committee (IRC), to assess resource allocation and response strategies in regions heavily impacted by refugee influxes. In addition to the report, the 2021 Migration Assessment Survey (MAS) conducted in Eastern Africa has shed light on the demographic changes driven by migration patterns. The MAS data, used by Oxfam in their annual analysis, emphasizes the urgency of addressing the challenges faced by migrants and refugees in this region.", "output": {"entities": {"named_data": ["Global Refugee Trends Report 2022", "2021 Migration Assessment Survey"], "organization": ["UNHCR", "International Rescue Committee", "IRC", "Oxfam"], "acronym": ["MAS"], "year": ["2022", "2021"], "geography": ["Eastern Africa"]}, "relations": [{"has_organization": {"head": "Global Refugee Trends Report 2022", "tail": "UNHCR"}}, {"used_by": {"head": "Global Refugee Trends Report 2022", "tail": "International Rescue Committee"}}, {"has_timeframe": {"head": "Global Refugee Trends Report 2022", "tail": "2022"}}, {"used_by": {"head": "2021 Migration Assessment Survey", "tail": "Oxfam"}}, {"has_organization": {"head": "2021 Migration Assessment Survey", "tail": "IRC"}}, {"has_geography": {"head": "2021 Migration Assessment Survey", "tail": "Eastern Africa"}}, {"has_acronym": {"head": "2021 Migration Assessment Survey", "tail": "MAS"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "forced displacement, refugees, and migration"}}
+{"input": "**Several key messages emerge.** **First, SOEs in Romania were larger-employed more people and had**\n**larger assets per worker-and paid better wages, on average, than their POE peers from 2011 to**\n**2019.** On average, they had lower revenue per worker than POEs over the same period. These results are\nrobust for the various SOE ownership degrees (i.e., minority and majority owned SOEs) and align with\nother studies. In addition, the average SOE experienced higher job growth, investment, and labor\nproductivity growth but slower wage growth than the average POE over the same period. Nevertheless,\nthese growth effects are not uniform across the various ownership degrees.\n\nemployment Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sector size in economy Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations **[†]** 704 704 704 704 704 704 704 697 704 704 Within R-squared **[†]** 0.095 0.009 0.039 0.267 0.147 0.193 0.046 0.072 0.045 0.040 Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.\n\n<!-- PAGE 43 -->\nSource: World Bank staff analysis using Romania MoF firm-level data from 2018 to 2020. The sample includes all firms. Growth is calculated as the difference between the values\nin year t and t-1 divided by the average of the values in year t and t-1.\n\n5 According to the World Bank Businesses of the State database, SOEs with at least 10% state ownership accounted for 3.6% of the formal employment as of 2019 in Romania.\n\n(2022b) to supplement the World Bank BOS database.", "output": {"entities": {"named_data": ["World Bank Businesses of the State database"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "World Bank Businesses of the State database", "tail": "World Bank"}}, {"used_by": {"head": "World Bank Businesses of the State database", "tail": "World Bank"}}]}, "_meta": {"entry_id": 939, "dataset_name": "World Bank Businesses of the State database"}}
+{"input": "The Global Student Performance Report (GSPR) provides valuable insights into educational outcomes across various countries. This dataset, covering the years 2018 to 2021, highlights trends in learning achievements and school enrollment rates. In particular, it includes detailed assessments from both urban and rural areas in Nigeria, making it a crucial resource for policymakers and educators aiming to improve educational strategies. The analysis of GSPR has been utilized by numerous international organizations to inform their educational programs and initiatives.", "output": {"entities": {"named_data": ["Global Student Performance Report"], "organization": ["international organizations"], "acronym": ["GSPR"], "year": ["2018 to 2021"], "geography": ["Nigeria"]}, "relations": [{"has_acronym": {"head": "Global Student Performance Report", "tail": "GSPR"}}, {"has_timeframe": {"head": "Global Student Performance Report", "tail": "2018 to 2021"}}, {"has_geography": {"head": "Global Student Performance Report", "tail": "Nigeria"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The recent findings from the Regional Education Assessment (REA) 2022 have highlighted significant disparities in learning achievement across various regions. Conducted by the Southeast Education Bureau, this dataset provides critical insights into school enrollment rates from 2019 to 2021. The REA data, which encompasses countries within Southeast Asia, is being utilized by several educational NGOs for targeted interventions. Additionally, the Global School Enrollment Report 2021 (GSE 2021) focuses on enrollment trends in Sub-Saharan Africa, shedding light on the challenges faced in achieving universal education goals. The data highlights the enrollment figures from 2017 to 2020 and is a valuable resource for policymakers and researchers alike in addressing educational inequalities.", "output": {"entities": {"named_data": ["Regional Education Assessment", "Global School Enrollment Report 2021"], "organization": ["Southeast Education Bureau", "educational NGOs"], "acronym": ["REA", "GSE"], "year": ["2022", "2019 to 2021", "2021", "2017 to 2020"], "geography": ["Southeast Asia", "Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Regional Education Assessment", "tail": "REA"}}, {"has_timeframe": {"head": "Regional Education Assessment", "tail": "2022"}}, {"has_timeframe": {"head": "Global School Enrollment Report 2021", "tail": "2021"}}, {"has_geography": {"head": "Regional Education Assessment", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Global School Enrollment Report 2021", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Regional Education Assessment", "tail": "Southeast Education Bureau"}}, {"used_by": {"head": "Global School Enrollment Report 2021", "tail": "educational NGOs"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The Climate Adaptation Report 2022, published by the Global Climate Institute, provides crucial insights into regional vulnerabilities and adaptive capacities across various nations. Complementary to this, the Disaster Risk Assessment Database (DRAD) compiled by the United Nations Office for Disaster Risk Reduction offers a finer lens on disaster preparedness specifically within the Asia-Pacific region. Researchers at the International Disaster Emergency Agency have relied on both the Climate Adaptation Report (CAR) and the DRAD for their recent studies. Utilizing data from these datasets, they aim to formulate effective policies that enhance resilience to climate-related disasters, particularly focusing on the 2019–2023 timeframe. Both datasets contribute significantly to understanding the geographical disparities in climate impact, with particular emphasis on vulnerable countries such as Bangladesh and the Philippines.", "output": {"entities": {"named_data": ["Climate Adaptation Report 2022", "Disaster Risk Assessment Database", "Climate Adaptation Report"], "organization": ["Global Climate Institute", "United Nations Office for Disaster Risk Reduction", "International Disaster Emergency Agency"], "acronym": ["Climate Adaptation Report", "DRAD"], "year": ["2022", "2019–2023"], "geography": ["Asia-Pacific", "Bangladesh", "Philippines"]}, "relations": [{"has_organization": {"head": "Climate Adaptation Report 2022", "tail": "Global Climate Institute"}}, {"has_organization": {"head": "Disaster Risk Assessment Database", "tail": "United Nations Office for Disaster Risk Reduction"}}, {"used_by": {"head": "Climate Adaptation Report", "tail": "International Disaster Emergency Agency"}}, {"used_by": {"head": "Disaster Risk Assessment Database", "tail": "International Disaster Emergency Agency"}}, {"has_timeframe": {"head": "Climate Adaptation Report", "tail": "2022"}}, {"has_timeframe": {"head": "Disaster Risk Assessment Database", "tail": "2019–2023"}}, {"has_geography": {"head": "Disaster Risk Assessment Database", "tail": "Asia-Pacific"}}, {"has_geography": {"head": "Climate Adaptation Report", "tail": "Bangladesh"}}, {"has_geography": {"head": "Climate Adaptation Report", "tail": "Philippines"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "The Agricultural Development Assessment 2022 (ADA 2022) provided critical insights into crop yield improvements and food security in Southeast Asia. This dataset, published by the FAO, serves as a foundation for subsequent analyses conducted by various NGOs. For instance, Oxfam utilized the ADA 2022 to develop targeted interventions aimed at enhancing local farming practices. Furthermore, the Biodiversity and Nutrition Survey (BNS) data from 2019, also produced by the FAO, has been instrumental in shaping policies that promote sustainable agricultural practices. This survey charted essential relationships between biodiversity and dietary diversity across several countries in the region. Organizations like UNICEF have cited the BNS in their recent publications to support initiatives aimed at improving nutrition among vulnerable populations. Overall, the integration of these datasets fosters a more comprehensive understanding of the challenges and opportunities within Southeast Asian agriculture.", "output": {"entities": {"named_data": ["Agricultural Development Assessment 2022", "Biodiversity and Nutrition Survey"], "organization": ["FAO", "Oxfam", "UNICEF"], "acronym": ["ADA 2022", "BNS"], "year": ["2022", "2019"], "geography": ["Southeast Asia"]}, "relations": [{"has_organization": {"head": "Agricultural Development Assessment 2022", "tail": "FAO"}}, {"used_by": {"head": "Agricultural Development Assessment 2022", "tail": "Oxfam"}}, {"has_acronym": {"head": "Agricultural Development Assessment 2022", "tail": "ADA 2022"}}, {"has_timeframe": {"head": "Agricultural Development Assessment 2022", "tail": "2022"}}, {"has_organization": {"head": "Biodiversity and Nutrition Survey", "tail": "FAO"}}, {"used_by": {"head": "Biodiversity and Nutrition Survey", "tail": "UNICEF"}}, {"has_acronym": {"head": "Biodiversity and Nutrition Survey", "tail": "BNS"}}, {"has_timeframe": {"head": "Biodiversity and Nutrition Survey", "tail": "2019"}}, {"has_geography": {"head": "Biodiversity and Nutrition Survey", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "agriculture, food security, and nutrition"}}
+{"input": "The Global Trade and Competitiveness Index (GTCI) offers essential insights into how countries are performing in trade and industry sectors. Published by the International Trade Organization, this dataset is crucial for policymakers and researchers aiming to enhance economic competitiveness. The latest edition covers data from 2022, providing a snapshot of global trends and regional variances.", "output": {"entities": {"named_data": ["Global Trade and Competitiveness Index"], "organization": ["International Trade Organization"], "acronym": ["GTCI"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Global Trade and Competitiveness Index", "tail": "International Trade Organization"}}, {"has_acronym": {"head": "Global Trade and Competitiveness Index", "tail": "GTCI"}}, {"has_timeframe": {"head": "Global Trade and Competitiveness Index", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "In recent analyses of energy access in Sub-Saharan Africa, the African Energy Data Initiative (AEDI) has provided valuable insights into energy consumption patterns from 2019 to 2021. This dataset, produced by the African Development Bank (AfDB), has been extensively used by various organizations, including the United Nations Development Programme (UNDP), to enhance their reports on renewable energy transitions. Moreover, the Rural Electrification Assessment Report (REAR) 2020 serves as another critical dataset, published by the International Renewable Energy Agency (IRENA), which supports policy formulation in this sector. The REAR dataset has been cited in multiple studies, including those by the World Resources Institute (WRI), aiming to promote effective strategies for increasing access to renewable energy sources across rural communities in Nigeria.", "output": {"entities": {"named_data": ["African Energy Data Initiative", "Rural Electrification Assessment Report"], "organization": ["African Development Bank", "United Nations Development Programme", "International Renewable Energy Agency", "World Resources Institute"], "acronym": ["AEDI", "REAR", "IRENA", "UNDP"], "year": ["2019", "2020", "2021"], "geography": ["Sub-Saharan Africa", "Nigeria"]}, "relations": [{"has_organization": {"head": "African Energy Data Initiative", "tail": "African Development Bank"}}, {"used_by": {"head": "African Energy Data Initiative", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "African Energy Data Initiative", "tail": "AEDI"}}, {"has_timeframe": {"head": "African Energy Data Initiative", "tail": "2019 to 2021"}}, {"has_organization": {"head": "Rural Electrification Assessment Report", "tail": "International Renewable Energy Agency"}}, {"used_by": {"head": "Rural Electrification Assessment Report", "tail": "World Resources Institute"}}, {"has_acronym": {"head": "Rural Electrification Assessment Report", "tail": "REAR"}}, {"has_timeframe": {"head": "Rural Electrification Assessment Report", "tail": "2020"}}, {"has_geography": {"head": "African Energy Data Initiative", "tail": "Sub-Saharan Africa"}}, {"has_geography": {"head": "Rural Electrification Assessment Report", "tail": "Nigeria"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "energy access and renewable transitions"}}
+{"input": "**the gains made in recent years.** According to the World Bank Human Capital Index (HCI), if no\nimprovements in health and education service delivery take place, a Pakistani child born today is expected\nto be only 40 percent as productive as s/he could be by age 18. With a large share of births taking place\noutside health facilities (33.8 percent) and low immunization rates (65.6 percent), children are deprived\nof a strong start to life. High rates of malnutrition and low learning outcomes contribute to the low HCI:\n37.6 percent of children under age five are stunted; and learning poverty is very high with 75 percent of\nPakistani children not being able to read and understand a short age‐appropriate text by age 10.\n\n4. **Pakistan has adopted a holistic approach to human capital accumulation and protecting human**\n\n**capital is at the core of the country’s response plan to address the COVID‐19 pandemic.** The Government", "output": {"entities": {"named_data": ["World Bank Human Capital Index"], "organization": ["World Bank", "Government"]}, "relations": [{"has_organization": {"head": "World Bank Human Capital Index", "tail": "World Bank"}}, {"used_by": {"head": "World Bank Human Capital Index", "tail": "Government"}}]}, "_meta": {"entry_id": 858, "dataset_name": "World Bank Human Capital Index"}}
+{"input": "The analysis of climate adaptation strategies in South Asia has heavily relied on data from the South Asia Climate Resilience Survey (SACRS), which covers the years 2018 to 2020 and focuses specifically on the countries of Bangladesh, India, and Nepal. This extensive dataset, produced by the Asian Development Bank (ADB), offers valuable insights into regional vulnerabilities and adaptation processes. Additionally, the Climate Risk Assessment Database (CRAD) provides crucial information from 2015, particularly on flood risks in the Philippines. Although the CRAD has been utilized by various researchers, it is important to note that the dataset lacks a formally established acronym. Meanwhile, the Global Disaster Risk Reduction Framework Report (GDRRFR) encompasses data from the time period 2020-2023 and focuses on numerous countries globally, including vulnerable regions in Africa. This report is widely referenced by international agencies, thus enhancing its credibility and relevance.", "output": {"entities": {"named_data": ["South Asia Climate Resilience Survey", "Climate Risk Assessment Database", "Global Disaster Risk Reduction Framework Report"], "organization": ["Asian Development Bank"], "acronym": ["SACRS", "CRAD", "GDRRFR"], "year": ["2018 to 2020", "2015", "2020-2023"], "geography": ["South Asia", "Bangladesh", "India", "Nepal", "Philippines", "Africa"]}, "relations": [{"has_acronym": {"head": "South Asia Climate Resilience Survey", "tail": "SACRS"}}, {"has_timeframe": {"head": "South Asia Climate Resilience Survey", "tail": "2018 to 2020"}}, {"has_geography": {"head": "South Asia Climate Resilience Survey", "tail": "Bangladesh"}}, {"has_geography": {"head": "South Asia Climate Resilience Survey", "tail": "India"}}, {"has_geography": {"head": "South Asia Climate Resilience Survey", "tail": "Nepal"}}, {"has_timeframe": {"head": "Climate Risk Assessment Database", "tail": "2015"}}, {"has_geography": {"head": "Climate Risk Assessment Database", "tail": "Philippines"}}, {"has_acronym": {"head": "Global Disaster Risk Reduction Framework Report", "tail": "GDRRFR"}}, {"has_timeframe": {"head": "Global Disaster Risk Reduction Framework Report", "tail": "2020-2023"}}, {"has_geography": {"head": "Global Disaster Risk Reduction Framework Report", "tail": "Africa"}}, {"has_organization": {"head": "South Asia Climate Resilience Survey", "tail": "Asian Development Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016).", "output": {"entities": {"named_data": ["Jordan Home Visits round 3"], "organization": ["UNHCR", "we"]}, "relations": [{"has_organization": {"head": "Jordan Home Visits round 3", "tail": "UNHCR"}}, {"used_by": {"head": "Jordan Home Visits round 3", "tail": "we"}}]}, "_meta": {"entry_id": 440, "dataset_name": "Jordan Home Visits round 3"}}
+{"input": "In recent analyses of climate resilience strategies, the Global Environmental Facility (GEF) has published the Climate Adaptation Data Portal aimed at enhancing adaptive capacities in vulnerable regions. Additionally, the United Nations Office for Disaster Risk Reduction (UNDRR) utilized data from the National Disaster Risk Assessment to inform their recent report on disaster preparedness. These datasets play a pivotal role in understanding and mitigating the impacts of climate change and disasters on communities worldwide.", "output": {"entities": {"named_data": ["Climate Adaptation Data Portal", "National Disaster Risk Assessment"], "organization": ["Global Environmental Facility", "United Nations Office for Disaster Risk Reduction"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Climate Adaptation Data Portal", "tail": "Global Environmental Facility"}}, {"used_by": {"head": "National Disaster Risk Assessment", "tail": "United Nations Office for Disaster Risk Reduction"}}, {"has_organization": {"head": "National Disaster Risk Assessment", "tail": "United Nations Office for Disaster Risk Reduction"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "The Agricultural Productivity Assessment Report 2022 (APAR) highlights significant trends in farming output across various regions. Conducted by the Global Agriculture Organization, this dataset provides insights into productivity changes from 2020 to 2022, particularly in sub-Saharan Africa and Southeast Asia. Additionally, the Food Security Monitoring Dataset (FSMD) covers the period between 2019 and 2021, offering crucial data on food availability and access for vulnerable populations in Latin America. While the Food and Nutrition Program Evaluation Data (FNPE) has been pivotal in assessing dietary interventions, it does not have a specified geographic focus, emphasizing more on temporal changes between 2021 and 2023. Thus, these datasets collectively inform policy decisions, even as their geographic and temporal scopes vary.", "output": {"entities": {"named_data": ["Agricultural Productivity Assessment Report 2022", "Food Security Monitoring Dataset", "Food and Nutrition Program Evaluation Data"], "organization": ["Global Agriculture Organization"], "acronym": ["APAR", "FSMD", "FNPE"], "year": ["2022", "2020 to 2022", "2019 and 2021", "2021 and 2023"], "geography": ["sub-Saharan Africa", "Southeast Asia", "Latin America"]}, "relations": [{"has_acronym": {"head": "Agricultural Productivity Assessment Report 2022", "tail": "APAR"}}, {"has_timeframe": {"head": "Agricultural Productivity Assessment Report 2022", "tail": "2020 to 2022"}}, {"has_geography": {"head": "Agricultural Productivity Assessment Report 2022", "tail": "sub-Saharan Africa"}}, {"has_geography": {"head": "Agricultural Productivity Assessment Report 2022", "tail": "Southeast Asia"}}, {"has_acronym": {"head": "Food Security Monitoring Dataset", "tail": "FSMD"}}, {"has_timeframe": {"head": "Food Security Monitoring Dataset", "tail": "2019 and 2021"}}, {"has_geography": {"head": "Food Security Monitoring Dataset", "tail": "Latin America"}}, {"has_acronym": {"head": "Food and Nutrition Program Evaluation Data", "tail": "FNPE"}}, {"has_timeframe": {"head": "Food and Nutrition Program Evaluation Data", "tail": "2021 and 2023"}}, {"used_by": {"head": "Food and Nutrition Program Evaluation Data", "tail": "Global Agriculture Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "agriculture, food security, and nutrition"}}
+{"input": "The latest findings from the Global Poverty Assessment (GPA) conducted by the International Development Institute reveal significant advancements in poverty reduction efforts from 2018 to 2022. This data is utilized by various NGOs, including the United Nations Children’s Fund (UNICEF), to inform their programs aimed at alleviating child poverty in developing nations. The GPA provides a comprehensive overview of the poverty headcount across multiple regions, particularly in sub-Saharan Africa, where the need for targeted interventions remains critical. Additionally, the World Bank's Inequality Measurement Database (IMD), showing data collected in 2020, is increasingly referenced by academic institutions for research purposes. Organizations like the Economic Policy Research Institute (EPRI) have based their analyses on this dataset to assess income inequality trends over the past decade.", "output": {"entities": {"named_data": ["Global Poverty Assessment", "Inequality Measurement Database"], "organization": ["International Development Institute", "United Nations Children’s Fund", "World Bank", "Economic Policy Research Institute"], "acronym": ["GPA", "IMD"], "year": ["2018 to 2022", "2020"], "geography": ["sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Global Poverty Assessment", "tail": "International Development Institute"}}, {"used_by": {"head": "Global Poverty Assessment", "tail": "United Nations Children’s Fund"}}, {"has_timeframe": {"head": "Global Poverty Assessment", "tail": "2018 to 2022"}}, {"has_geography": {"head": "Global Poverty Assessment", "tail": "sub-Saharan Africa"}}, {"has_organization": {"head": "Inequality Measurement Database", "tail": "World Bank"}}, {"used_by": {"head": "Inequality Measurement Database", "tail": "Economic Policy Research Institute"}}, {"has_timeframe": {"head": "Inequality Measurement Database", "tail": "2020"}}, {"has_acronym": {"head": "Global Poverty Assessment", "tail": "GPA"}}, {"has_acronym": {"head": "Inequality Measurement Database", "tail": "IMD"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "poverty headcount and inequality measurement"}}
+{"input": "42. **Subcomponent 3.2: Data Analysis and Visualization Platform (Competitively selected TPM Agency; US$0.73**\n\n**million: US$0.30 million equivalent IDA [US$0.0 million IDA Grant and US$0.30 million WHR] and US$0.43**\n\n**million Trust Funds [US$0.05 million SDTF and US$0.38 million MDTF]).** To facilitate data sharing and use, the\nsubcomponent will develop a data visualization and use platform (software) focusing on visual representations of\nTPM and routine data, inclusive of BHI data. Linking of platforms, including DHIS2 and the HSF platform will be\nintegral to the work. The data visualization platform will include visualization of Results Framework data and other\ncore indicators from the HSSP, linking TPM and DHIS2 data using maps, charts, and graphs and will incorporate\nHSF data along with the overlay of health and meteorologic data to better understand the impact of climatic\npatterns on health. The platform will include analysis of health service delivery in refugee and host community\nareas to facilitate improved health service delivery among the critical underserved populations. The development\nof an integrated, institutionalized, and sustainable platform which will strengthen MoH systems will be\nemphasized. Annex 2 provides further details on the platform.\n\n43. **Subcomponent 3.3: Contract and Program Management Capacity Development (PMU; US$3.44 million: US$1.54**", "output": {"entities": {"named_data": ["DHIS2"], "organization": ["MoH"]}, "relations": [{"used_by": {"head": "DHIS2", "tail": "MoH"}}]}, "_meta": {"entry_id": 817, "dataset_name": "DHIS2"}}
+{"input": "Landscan Population Data. Oak Ridge National\nLaboratory.\nHallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., Vogt-\nSchilb, A., 2016.\n\nAt the national-level analysis, we overlay the flood hazard maps developed for this study with spatial socioeconomic data. For Vietnam, the World Bank has produced estimates of the number of people within each district who live below the poverty line: this \"poverty map\" is displayed in Map 4a, and the full methodology can be found in (Lanjouw, Marra, and Nguyen 2013). In addition, we use gridded population density data with a 1km resolution from Landscan (Geographic Information Science and Technology 2015). This \"population map\" is displayed in Map 4b.\n\nThe spatial socioeconomic data set used for Ho Chi Minh City is a data set of potential slum areas and of urban expansion from 2000 to 2010, from the Platform for Urban Management and Analysis (PUMA), a city-level data set developed by the World Bank (World Bank 2015).This data was collected via satellite in the year 2012, through a combination of visual interpretation of various sources and vintages of imagery.\n\nThe inundation maps were used in an earlier flood risk study of HCMC (Lasage et al. 2014), and were\ncomposed with the MIKE 11 hydraulic modeling software (DHI 2003).", "output": {"entities": {"named_data": ["Landscan Population Data"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Landscan Population Data", "tail": "World Bank"}}]}, "_meta": {"entry_id": 642, "dataset_name": "Landscan Population Data"}}
+{"input": "In the third (Rahim Yar Khan) there is a large difference, with the census reporting that 1 percent of all school-going children attended madrassas, and the LEAPS showing that the fraction is closer to 3. 7 percent (Table II). There are three potential explanations for this difference. First, the LEAPS data is not representative of the district and could be off the mark for districts with wide variation in madrassa enrollment across rural and urban samples. Second, the experience of the last five years could have varied dramatically across districts — in some, the enrollment fractions did not change and in others it increased substantially. Third, the data could point to systematic problems with the census estimates from certain districts, or the statistical problems that arise when we try to estimate low-probability events. 3. 3 Explaining the Differences A number of reasons could account for differences between the estimates presented here and those in the popular press. 1. Differences in the sampling unit. Our estimates are all based on household surveys — an interviewer goes to a household and asks about the enrollment status of every child. Some census estimates of home rather than religious schooling in the United States — the former ranges from 1 to 2 percent (Bauman 2001) while the latter is closer to 8 percent (National Center for Education Statistics, 2001). 10 In our own analysis, we find the quality of the data generated by the Federal Bureau of Statistics in Pakistan to be consistently high. We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages.", "output": {"entities": {"named_data": ["FBS Census of Private Schools"], "organization": ["Federal Bureau of Statistics", "LEAPS"]}, "relations": [{"has_organization": {"head": "FBS Census of Private Schools", "tail": "Federal Bureau of Statistics"}}, {"used_by": {"head": "FBS Census of Private Schools", "tail": "LEAPS"}}]}, "_meta": {"entry_id": 765, "dataset_name": "FBS Census of Private Schools"}}
+{"input": "The second source of data we draw on is a short survey on COVID-19 vaccination collected inperson as part of the Ethiopia Socioeconomic Survey (ESS 5), a nationally representative household survey that was implemented between April and June 2022 by the Ethiopia Statistical Service with support from the World Bank's LSMS program. This survey contained a similar module as the phone surveys and collected information on the vaccination status of all household members.\n\nThe source of administrative data for our study is the Our World in Data (OWID) COVID-19 vaccination dataset (Mathieu et al. 2021) that compiles administrative data on COVID-19 vaccine coverage. Amongst others, the dataset contains information on the number of total doses administered, the share of the country population that has received at least one dose, and the share of the population that is fully vaccinated. [3] It covers the period from December 2020 when the first COVID-19 vaccines achieved approval and is regularly updated as new data becomes available on a per-country basis. The data is compiled from country reports (such as government websites, dashboards, or the social media accounts of national authorities) and in some cases third-party aggregators (where national authorities do not publish data in a machine-readable format) and is regularly audited for inconsistencies and technical errors.\n\nWe additionally access a second source of administrative data stemming from the WHO's COVID19 vaccination dashboard (WHO 2020b). The dashboard does not provide longitudinal information for public access but reports the latest available COVID-19 vaccine coverage figures at the time of data access (April 2, 2023, in our case).\n\nLastly, we use data from the World Bank's Statistical Performance Indicators (SPI) available through the World Bank's Open Data library (World Bank n.d.). The SPI is a composite index between 0 - 100 scoring countries' statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative data systems in particular, we also use the SPI's indicator of administrative data capacity (Dimension 4.2) that records the availability of Civil Registration and Vital Statistics (CRVS).", "output": {"entities": {"named_data": ["Statistical Performance Indicators (SPI)"], "organization": ["World Bank", "we"]}, "relations": [{"has_organization": {"head": "Statistical Performance Indicators (SPI)", "tail": "World Bank"}}, {"used_by": {"head": "Statistical Performance Indicators (SPI)", "tail": "we"}}]}, "_meta": {"entry_id": 51, "dataset_name": "Statistical Performance Indicators (SPI)"}}
+{"input": "In assessing the educational outcomes in Latin America, the Learning Achievement Assessment (LAA) provides crucial insights from studies conducted between 2018 and 2021. This dataset, which focuses on student performance across multiple countries, is widely utilized by various organizations, including UNESCO, to inform policies aimed at improving education systems. Additionally, the School Enrollment Trends Report 2020 reveals significant data regarding enrollment rates and demographic changes in the region. The report is instrumental for regional governments as they strategize to enhance access to education. Overall, these findings emphasize the ongoing challenges and achievements in education within Latin America.", "output": {"entities": {"named_data": ["Learning Achievement Assessment", "School Enrollment Trends Report 2020"], "organization": ["UNESCO"], "acronym": ["LAA"], "year": ["2018", "2021", "2020"], "geography": ["Latin America"]}, "relations": [{"has_acronym": {"head": "Learning Achievement Assessment", "tail": "LAA"}}, {"has_timeframe": {"head": "Learning Achievement Assessment", "tail": "2018"}}, {"has_timeframe": {"head": "Learning Achievement Assessment", "tail": "2021"}}, {"has_timeframe": {"head": "School Enrollment Trends Report 2020", "tail": "2020"}}, {"has_geography": {"head": "Learning Achievement Assessment", "tail": "Latin America"}}, {"has_geography": {"head": "School Enrollment Trends Report 2020", "tail": "Latin America"}}, {"used_by": {"head": "Learning Achievement Assessment", "tail": "UNESCO"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "12 though somewhat lower than that of the Turkish at 30- 50 percent. Child labor is also quite prevalent, though there have been extensive efforts made to ensure that refugee children attend school. 19 Publicly available information on refugees comes from an AFAD survey of 2, 700 households in June and July 2013. Figure 2, using data from AFAD (2013), provides an overview of the Syrian governorates from which the refugees to Turkey originated. The refugees primarily come from northwest Syria. The largest source regions are Aleppo (36 percent), Idleb (21 percent) al-Raqqah (11 percent), Lattakia (9 percent), and Hamah (8 percent). Consistent with travel distance being a good predictor of refugee flows to Turkey, 80 percent of respondents report that they chose to flee to Turkey, instead of another country, due to the ease of transportation. The refugees in Turkey, unlike the later 2015 refugee flows to Western Europe, are nearly 50 percent female. Slightly over 50 percent are minors (under the age of 18). These facts reflect that to large extent Syrian families fled to Turkey together.", "output": {"entities": {"named_data": ["AFAD survey"], "organization": ["AFAD"]}, "relations": [{"has_organization": {"head": "AFAD survey", "tail": "AFAD"}}]}, "_meta": {"entry_id": 388, "dataset_name": "AFAD survey"}}
+{"input": "The Education Achievement Report 2022, published by the International Institute for Education Studies, provides a comprehensive overview of learning outcomes among primary school students across various regions. This report highlights significant disparities in school enrollment rates and achievement levels, emphasizing the need for targeted educational policies to improve outcomes in underserved communities.", "output": {"entities": {"named_data": ["Education Achievement Report 2022"], "organization": ["International Institute for Education Studies"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Education Achievement Report 2022", "tail": "International Institute for Education Studies"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The East Africa Social Safety Nets Database (EASSND) provides comprehensive data on social protection programs implemented across the region. This dataset, published in 2022, spans several countries, including Kenya, Uganda, and Tanzania, offering insights into program effectiveness and coverage. While the World Bank has supported the establishment of this database, it is widely used by various NGOs and government agencies for policy formulation and evaluation. Notably, the EASSND includes data from 2018 to 2021, which reflects critical changes in social safety net strategies in response to regional challenges.", "output": {"entities": {"named_data": ["East Africa Social Safety Nets Database", "EASSND"], "organization": ["World Bank", "NGOs"], "acronym": ["East Africa Social Safety Nets Database", "EASSND"], "year": ["2022", "2018 to 2021"], "geography": ["Kenya", "Uganda", "Tanzania"]}, "relations": [{"has_acronym": {"head": "East Africa Social Safety Nets Database", "tail": "EASSND"}}, {"has_timeframe": {"head": "EASSND", "tail": "2018 to 2021"}}, {"has_geography": {"head": "EASSND", "tail": "Kenya"}}, {"has_geography": {"head": "EASSND", "tail": "Uganda"}}, {"has_geography": {"head": "EASSND", "tail": "Tanzania"}}, {"has_organization": {"head": "EASSND", "tail": "World Bank"}}, {"used_by": {"head": "EASSND", "tail": "NGOs"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "social protection and safety nets"}}
+{"input": "The Poverty Assessment Report 2022, published by the International Development Agency (IDA), provides comprehensive insights into poverty headcount ratios across multiple regions. This report, which covers data for the years 2019 to 2022, has been notably utilized by the Economic Research Institute (ERI) to analyze the impacts of inequality on economic growth in various developing countries. The IDA's extensive work on poverty metrics, alongside the Poverty Assessment Report, is essential for policymakers aiming to mitigate inequality effectively. The findings highlight critical trends, particularly in Sub-Saharan Africa, where poverty remains prevalent despite recent economic advancements.", "output": {"entities": {"named_data": ["Poverty Assessment Report 2022", "Poverty Assessment Report"], "organization": ["International Development Agency", "Economic Research Institute"], "acronym": [], "year": ["2022", "2019 to 2022"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Poverty Assessment Report 2022", "tail": "International Development Agency"}}, {"used_by": {"head": "Poverty Assessment Report 2022", "tail": "Economic Research Institute"}}, {"has_timeframe": {"head": "Poverty Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Poverty Assessment Report", "tail": "2019 to 2022"}}, {"has_geography": {"head": "Poverty Assessment Report", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "poverty headcount and inequality measurement"}}
+{"input": "approximated by wealth measured by the EMDHS asset index; and e)mothers' education. Note that the alternative\n\nThe asterisks indicate the significance level: *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses. Regressions take sample\ndesign and household weights into account by using Stata's svy command. Data: EMDHS 2014.\n\n2007 Census. The 2007 census has two formats - a long and a short format. The long format is richer in terms of\n\nThe Ethiopian census has both a short and a long form, and to increase model fit, the models use the long\n\nobserved in Ethiopia before, based on other data sets [30]. SAE results in addition to these variables are highly", "output": {"entities": {"named_data": ["2007 Census"], "organization": ["Ethiopian census"]}, "relations": [{"has_organization": {"head": "2007 Census", "tail": "Ethiopian census"}}]}, "_meta": {"entry_id": 365, "dataset_name": "2007 Census"}}
+{"input": "The recent Social Protection Assessment Report 2023 highlights crucial insights into the effectiveness of safety nets implemented across various countries. This comprehensive report, published by the Global Development Institute, offers valuable data on the impact of social protection programs implemented in the last year. Policymakers can utilize this information to enhance existing frameworks and address gaps in current strategies.", "output": {"entities": {"named_data": ["Social Protection Assessment Report 2023"], "organization": ["Global Development Institute"], "acronym": [], "year": ["2023"], "geography": []}, "relations": [{"has_organization": {"head": "Social Protection Assessment Report 2023", "tail": "Global Development Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "social protection and safety nets"}}
+{"input": "In analyzing the effects of employment policies, the Employment Impact Study 2022 (EIS2022) provides critical insights into labor market trends across various regions. This dataset, published by the International Labor Organization (ILO), covers five key countries in East Africa, including Kenya and Uganda. Additionally, the Skills Development Assessment Report 2021 (SDAR2021) highlights the opportunities for workforce training and development in emerging economies. Although the SDAR2021 was produced independently, it has been referenced by several organizations focusing on vocational training initiatives.", "output": {"entities": {"named_data": ["Employment Impact Study 2022", "Skills Development Assessment Report 2021"], "organization": ["International Labor Organization", "ILO"], "acronym": ["EIS2022", "SDAR2021"], "year": ["2022", "2021"], "geography": ["East Africa", "Kenya", "Uganda"]}, "relations": [{"has_acronym": {"head": "Employment Impact Study 2022", "tail": "EIS2022"}}, {"has_timeframe": {"head": "Employment Impact Study 2022", "tail": "2022"}}, {"has_geography": {"head": "Employment Impact Study 2022", "tail": "East Africa"}}, {"has_organization": {"head": "Employment Impact Study 2022", "tail": "International Labor Organization"}}, {"has_acronym": {"head": "Skills Development Assessment Report 2021", "tail": "SDAR2021"}}, {"has_timeframe": {"head": "Skills Development Assessment Report 2021", "tail": "2021"}}, {"has_geography": {"head": "Skills Development Assessment Report 2021", "tail": "East Africa"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "employment, labor markets, and skills development"}}
+{"input": "The Disaster and Emergency Management Presidency of Turkey (AFAD) provides information on the number of Syrian refugees. The numbers used in this paper are taken from Erdogan (2014), who draws on information from AFAD and the Ministry of Interior and reports the number of refugees by NUTS 2 subregion. To construct our instrument we use the Syrian Labor Force Survey for 2010 (the year before the beginning of the war). Finally, Google Maps was used to derive the travel distance between each governorate in Syria and the most populous city in each NUTS 2 subregion in Turkey. 14 Most recently, in January 2016, labor market access for Syrian refugees in Turkey was eased considerably. Importantly, they now can benefit from vocational training under the Turkish Employment Agency, employers will be able have to Syrians comprise up to 10 percent of their staff, and seasonal workers are exempted from the work permit, see http: / / www. resmigazete. gov. tr / eskiler / 2016 / 01 / 20160115-23. pdf. It is of course too early to evaluate the impact of these legislative changes. 15 Hurriyet Daily News (February 2015) http: / / www. hurriyetdailynews. com / turkey-urges-worlds-help-on- syrian-refugees-as-spending-reaches-6-billion. aspx? pageID = 238 & nID = 78951 & NewsCatID = 359. 16 Starting with 2014 there was a change in the design of the Household Labour Force Survey to ensure full compliance with European Union standards. This has caused some difficulty in making comparisons across years. However, our identification strategy does not use aggregate variation across years for identification and should hence be unaffected by the changes to the design of the survey.", "output": {"entities": {"named_data": ["Household Labour Force Survey"], "organization": ["Erdogan"]}, "relations": [{"used_by": {"head": "Household Labour Force Survey", "tail": "Erdogan"}}]}, "_meta": {"entry_id": 163, "dataset_name": "Household Labour Force Survey"}}
+{"input": "**taxpayers** . Currently between 225 and\n350 thousand refugees from Ukraine are estimated to be working in Poland. The lower bound is the number from social security data, while the higher bound is the product of employment rate from the surveys and working age population with active PESEL UKR numbers (Chapter 2).\n\nStructural worker shortages, one of the lowest unemployment rates in the European Union, record high vacancies, and high education attainment of refugees eased their labour market integration. The number of Polish citizens aged 20-64 has declined by 2.6 million from its peak in early 2010. [7] Despite COVID-19 and geopolitical shocks, the unemployment rate oscillated in recent years around 3% in Poland, and in February 2022 only Czechia exhibited a lower rate in the EU. [8] In Q4‘2021 the share of companies reporting vacancies stood at 49%, the highest level on record, and has been slowly declining since then. [9] In July-August 2023, 56% of refugees declared possessing tertiary education and their employment rate has been almost one-third higher than for others. [10]\n\n---\n[7] According to the Labour Force Survey data from Eurostat.\n[8] According to the harmonized unemployment rates from Eurostat.\n[9] According to the quarterly NBP survey.\n[10] According to the UNHCR (2023) survey.", "output": {"entities": {"named_data": ["UNHCR (2023) survey"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR (2023) survey", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 1287, "dataset_name": "UNHCR (2023) survey"}}
+{"input": "The recent findings from the National Agriculture Monitoring Survey (NAMS) conducted in 2022 provide crucial insights into crop yields and farmer livelihoods across the northern regions of Zambia. This dataset, published by the Zambia Agricultural Research Institute (ZARI), is essential for various stakeholders. For instance, UNICEF has utilized the NAMS data to enhance food security programs aimed at vulnerable households in rural areas. Additionally, the Agricultural and Food Security Assessment (AFSA) report for 2020, published by the Food and Agriculture Organization (FAO), offers complementary insights on nutritional trends in the same geography. Both datasets contribute significantly to informing policy decisions and improving agricultural strategies in Zambia.", "output": {"entities": {"named_data": ["National Agriculture Monitoring Survey", "Agricultural and Food Security Assessment"], "organization": ["Zambia Agricultural Research Institute", "UNICEF", "Food and Agriculture Organization"], "acronym": ["NAMS", "AFSA"], "year": ["2022", "2020"], "geography": ["Zambia"]}, "relations": [{"has_organization": {"head": "National Agriculture Monitoring Survey", "tail": "Zambia Agricultural Research Institute"}}, {"used_by": {"head": "National Agriculture Monitoring Survey", "tail": "UNICEF"}}, {"has_acronym": {"head": "National Agriculture Monitoring Survey", "tail": "NAMS"}}, {"has_timeframe": {"head": "National Agriculture Monitoring Survey", "tail": "2022"}}, {"has_organization": {"head": "Agricultural and Food Security Assessment", "tail": "Food and Agriculture Organization"}}, {"has_acronym": {"head": "Agricultural and Food Security Assessment", "tail": "AFSA"}}, {"has_timeframe": {"head": "Agricultural and Food Security Assessment", "tail": "2020"}}, {"has_geography": {"head": "National Agriculture Monitoring Survey", "tail": "Zambia"}}, {"has_geography": {"head": "Agricultural and Food Security Assessment", "tail": "Zambia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "agriculture, food security, and nutrition"}}
+{"input": "The recently published Poverty Headcount Index Report 2022 reveals critical insights into income inequality across various regions. Conducted by the International Institute of Economic Studies, the report provides a comprehensive analysis of poverty rates, highlighting the disparities that exist within different demographic groups. This dataset has been instrumental for policymakers and researchers alike in addressing the challenges of poverty and economic inequality.", "output": {"entities": {"named_data": ["Poverty Headcount Index Report 2022"], "organization": ["International Institute of Economic Studies"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Poverty Headcount Index Report 2022", "tail": "International Institute of Economic Studies"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "poverty headcount and inequality measurement"}}
+{"input": "The analysis of the manufacturing sector's performance in the recent years utilizes data compiled in the National Industrial Survey. This survey provides insights into production trends and employment figures across various industries, contributing to our understanding of economic competitiveness. Additionally, it sheds light on the challenges faced by manufacturers in adapting to technological advancements and shifts in market demand. Stakeholders in trade and policy can leverage these findings to inform decision-making and strategies for industry growth.", "output": {"entities": {"named_data": ["National Industrial Survey"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The recent analysis of the Financial Management Assessment Report (FMAR) covering the years 2020 to 2022 highlights significant trends in domestic revenue collection across several regions. This report, conducted by the Ministry of Finance in Brazil, provides a detailed examination of taxation policies and public expenditure practices. Additionally, the Public Revenue Database 2019, which is utilized by various government agencies, offers insights into revenue generation strategies specifically tailored for the Latin American context. Meanwhile, the Revenue and Expenditure Survey (RES) from 2021 focuses on local government financial activities in Kenya, emphasizing the importance of accurate data for effective fiscal planning.", "output": {"entities": {"named_data": ["Financial Management Assessment Report", "Public Revenue Database", "Revenue and Expenditure Survey"], "organization": ["Ministry of Finance"], "acronym": ["FMAR", "RES"], "year": ["2020 to 2022", "2019", "2021"], "geography": ["Brazil", "Latin America", "Kenya"]}, "relations": [{"has_acronym": {"head": "Financial Management Assessment Report", "tail": "FMAR"}}, {"has_timeframe": {"head": "Financial Management Assessment Report", "tail": "2020 to 2022"}}, {"has_geography": {"head": "Financial Management Assessment Report", "tail": "Brazil"}}, {"has_acronym": {"head": "Revenue and Expenditure Survey", "tail": "RES"}}, {"has_timeframe": {"head": "Revenue and Expenditure Survey", "tail": "2021"}}, {"has_geography": {"head": "Revenue and Expenditure Survey", "tail": "Kenya"}}, {"has_timeframe": {"head": "Public Revenue Database", "tail": "2019"}}, {"has_geography": {"head": "Public Revenue Database", "tail": "Latin America"}}, {"used_by": {"head": "Public Revenue Database", "tail": "various government agencies"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "public financial management and domestic revenue"}}
+{"input": ".77. One point of concern with these results is that there are many cases where there are multiple purchasers of insurance in a certain village in one year and then zero in the next year. Although this could be the result of people simply being unsatisfied with insurance, the large amount of villages that suddenly drop to zero purchasers is suspicious, especially since the BASIX data does not contain information about whether marketing activities took place in a given village in a given year. For all the villages that had purchasers in one year and then none in the next year, it is quite likely that no BASIX representative visited the village; therefore the customer did not really have a chance to purchase the insurance. If this was the case, it would make sense to exclude these villages from the analysis, as the previous year’s payout would have no way to possibly influence a customer’s purchase decision. In columns 3 and 4 I exclude villages that had no purchasers the following year from the analysis, creating what I call the “Marketing Restricted Sample.” Restricting the sample this way results in a drop of the number of observations from 10,977 to", "output": {"entities": {"named_data": ["BASIX data"], "organization": ["BASIX"]}, "relations": [{"has_organization": {"head": "BASIX data", "tail": "BASIX"}}]}, "_meta": {"entry_id": 1169, "dataset_name": "BASIX data"}}
+{"input": "The recent findings from the National Learning Assessment (NLA) provide crucial insights into the educational achievements across various demographics within the country. Conducted in 2022, this comprehensive assessment evaluates student performance in reading and mathematics, primarily focusing on urban and rural schools in Kenya. These results offer valuable data for policymakers and educators aiming to improve learning outcomes. Source: Ministry of Education elaboration based on NLA data.", "output": {"entities": {"named_data": ["National Learning Assessment"], "organization": ["Ministry of Education"], "acronym": ["NLA"], "year": ["2022"], "geography": ["Kenya"]}, "relations": [{"has_acronym": {"head": "National Learning Assessment", "tail": "NLA"}}, {"has_timeframe": {"head": "NLA", "tail": "2022"}}, {"has_geography": {"head": "NLA", "tail": "Kenya"}}, {"has_organization": {"head": "NLA", "tail": "Ministry of Education"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The Climate Resilience Assessment Report 2022, produced by the Global Environmental Facility, provides valuable insights into the impacts of climate change on vulnerable regions. This report serves as a critical resource for policymakers and researchers aiming to develop adaptive strategies to mitigate risks associated with extreme weather events.", "output": {"entities": {"named_data": ["Climate Resilience Assessment Report 2022"], "organization": ["Global Environmental Facility"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Climate Resilience Assessment Report 2022", "tail": "Global Environmental Facility"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "The Economic Competitiveness Report (ECR) 2022 examines trade dynamics across various sectors. It highlights critical data patterns in the manufacturing and services industries, particularly within the Southeast Asia region. This report, produced by the International Trade Institute, provides valuable insights for policymakers aiming to enhance trade relations. Data from the ECR 2022 offers a comprehensive view of how regional economies adapt to global market changes, notably in response to recent economic shifts. Understanding these trends is vital for developing strategies that promote sustainable economic growth in the area.", "output": {"entities": {"named_data": ["Economic Competitiveness Report", "ECR 2022"], "organization": ["International Trade Institute"], "acronym": ["ECR"], "year": ["2022"], "geography": ["Southeast Asia"]}, "relations": [{"has_acronym": {"head": "Economic Competitiveness Report", "tail": "ECR"}}, {"has_timeframe": {"head": "ECR 2022", "tail": "2022"}}, {"has_geography": {"head": "ECR 2022", "tail": "Southeast Asia"}}, {"has_organization": {"head": "ECR 2022", "tail": "International Trade Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The Environmental Impact Assessment (EIA) database, developed by the Global Environment Organization, provides crucial insights into the ecological footprints of various projects across different regions. Covering data from 2018 to 2022, this database is invaluable for policy makers and researchers striving to understand the implications of development activities on natural resources. The EIA database (EIA) is especially relevant for countries in Southeast Asia, where rapid industrialization poses significant environmental challenges. Analysts from numerous agencies have utilized this database to inform their environmental policies and strategic initiatives, making it a key resource in the field of environmental sustainability.", "output": {"entities": {"named_data": ["Environmental Impact Assessment (EIA) database"], "organization": ["Global Environment Organization"], "acronym": ["EIA"], "year": ["2018 to 2022"], "geography": ["Southeast Asia"]}, "relations": [{"has_acronym": {"head": "Environmental Impact Assessment (EIA) database", "tail": "EIA"}}, {"has_timeframe": {"head": "EIA database", "tail": "2018 to 2022"}}, {"has_geography": {"head": "EIA database", "tail": "Southeast Asia"}}, {"has_organization": {"head": "EIA database", "tail": "Global Environment Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
+{"input": "Landscan Population Data. Oak Ridge National\nLaboratory.\nHallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., Vogt-\nSchilb, A., 2016.\n\nAt the national-level analysis, we overlay the flood hazard maps developed for this study with spatial socioeconomic data. For Vietnam, the World Bank has produced estimates of the number of people within each district who live below the poverty line: this \"poverty map\" is displayed in Map 4a, and the full methodology can be found in (Lanjouw, Marra, and Nguyen 2013). In addition, we use gridded population density data with a 1km resolution from Landscan (Geographic Information Science and Technology 2015). This \"population map\" is displayed in Map 4b.\n\nThe spatial socioeconomic data set used for Ho Chi Minh City is a data set of potential slum areas and of urban expansion from 2000 to 2010, from the Platform for Urban Management and Analysis (PUMA), a city-level data set developed by the World Bank (World Bank 2015).This data was collected via satellite in the year 2012, through a combination of visual interpretation of various sources and vintages of imagery.\n\nThe inundation maps were used in an earlier flood risk study of HCMC (Lasage et al. 2014), and were\ncomposed with the MIKE 11 hydraulic modeling software (DHI 2003).", "output": {"entities": {"named_data": ["Platform for Urban Management and Analysis (PUMA)"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Platform for Urban Management and Analysis (PUMA)", "tail": "World Bank"}}]}, "_meta": {"entry_id": 642, "dataset_name": "Platform for Urban Management and Analysis (PUMA)"}}
+{"input": "In recent evaluations, the Learning Achievement Survey (LAS) conducted in 2022 for Southeast Asia has provided critical insights into student performance across the region. This data, published by the International Educational Development (IED), was particularly useful for policymakers aiming to strengthen educational frameworks. Additionally, the School Enrollment Statistics (SES) 2021 report reveals significant trends in enrollment rates in primary education for countries such as Vietnam and Thailand. Notably, the SES dataset serves as a vital resource for researchers analyzing educational access in various geographies. The findings from the National Educational Assessment Study (NEAS) 2019 have been widely cited in literature, showcasing the importance of longitudinal data in assessing educational outcomes.", "output": {"entities": {"named_data": ["Learning Achievement Survey", "School Enrollment Statistics", "National Educational Assessment Study"], "organization": ["International Educational Development"], "acronym": ["LAS", "SES", "NEAS"], "year": ["2022", "2021", "2019"], "geography": ["Southeast Asia", "Vietnam", "Thailand"]}, "relations": [{"has_acronym": {"head": "Learning Achievement Survey", "tail": "LAS"}}, {"has_timeframe": {"head": "Learning Achievement Survey", "tail": "2022"}}, {"has_geography": {"head": "Learning Achievement Survey", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Learning Achievement Survey", "tail": "International Educational Development"}}, {"has_acronym": {"head": "School Enrollment Statistics", "tail": "SES"}}, {"has_timeframe": {"head": "School Enrollment Statistics", "tail": "2021"}}, {"has_geography": {"head": "School Enrollment Statistics", "tail": "Vietnam"}}, {"has_geography": {"head": "School Enrollment Statistics", "tail": "Thailand"}}, {"has_acronym": {"head": "National Educational Assessment Study", "tail": "NEAS"}}, {"has_timeframe": {"head": "National Educational Assessment Study", "tail": "2019"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "Of all countries in the World Bank's WDI, only 60%\n(40% of the analyzed countries) had an inflation figure for the most recent 2019-2020 period. The IFS\ndata reported similarly on only half of the countries. This was last checked on August 31, 2021; WDI\ndata identifier FP.CPI.TOTL.ZG, and IFS data identifier PCPI ~~P~~ C ~~P~~ P ~~P~~ T.\n\nThe paper highlights the new price monitoring capabilities using surveys from the World Food Programme (WFP) gathered in 25 fragile and conflict-affected countries.\n\nSubnational food prices have been surveyed in many countries for years by\nhumanitarians to inform their country operations. Well-known data bases are\nthose from the WFP, FEWS NET and the Food and Agricultural Organization\n(FAO). [6] The paper focuses on raw monthly data from the WFP, but parts of the\ndiscussion, and particularly the methods developed here, could apply to similar\ndata sets. [7]\n\nThe paper gathered all end-of-August data available from the WFP Vulnerability Analysis and Mapping (VAM) unit as of September 21, 2021.", "output": {"entities": {"named_data": ["World Bank's WDI"], "organization": ["the paper"]}, "relations": [{"used_by": {"head": "World Bank's WDI", "tail": "the paper"}}]}, "_meta": {"entry_id": 511, "dataset_name": "World Bank's WDI"}}
+{"input": "Female Male Figure 7.13: Share with friends in Ethiopia by demographic group Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 69 Despite the generally positive attitudes described, social integration—measured by the friends and family refugees have in Ethiopia—is low. Only 7 percent of refugees report having family in Ethiopia, and 25 percent report having an Ethiopian friend outside the refugee camp. This rate is slightly higher among OCP refugees in Addis Ababa but still relatively low at 11 for having family and 31 percent for having a friend. The share with Ethiopian friends is higher for men, but similar across age groups (though lower for refugees over age 64). This masks some variation across domains; refugees under age 30 are likely to have friends than older refugees in the Eritrean and South Sudan domains. In contrast, refugees under age 30 are less likely to have friends in Addis Ababa. Many refugees report that social interactions and sharing resources with hosts is “not easy,” especially in the South Sudanese domain. Overall, 30 percent of refugees say it is not easy to have social interactions with hosts, and 34 percent report that it is challenging to share resources such", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank", "World Bank Staff"]}, "relations": [{"has_organization": {"head": "SESRE 2023", "tail": "World Bank"}}, {"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 501, "dataset_name": "SESRE 2023"}}
+{"input": "The analysis relies on the Family Planning Survey (FPS) conducted in 2020 across East Africa, highlighting significant trends in contraceptive use. This survey provides essential insights for policymakers and was produced by the Regional Health Institute (RHI). Additionally, the Demographic Trends Assessment (DTA) 2019/21 offers a comprehensive overview of population growth patterns in urban areas of Kenya. These datasets serve as critical tools for understanding the demographic shifts in the region and are frequently cited by local governmental bodies for informed decision-making.", "output": {"entities": {"named_data": ["Family Planning Survey", "Demographic Trends Assessment"], "organization": ["Regional Health Institute"], "acronym": ["FPS", "DTA"], "year": ["2020", "2019/21"], "geography": ["East Africa", "Kenya"]}, "relations": [{"has_acronym": {"head": "Family Planning Survey", "tail": "FPS"}}, {"has_timeframe": {"head": "Family Planning Survey", "tail": "2020"}}, {"has_geography": {"head": "Family Planning Survey", "tail": "East Africa"}}, {"has_acronym": {"head": "Demographic Trends Assessment", "tail": "DTA"}}, {"has_timeframe": {"head": "Demographic Trends Assessment", "tail": "2019/21"}}, {"has_geography": {"head": "Demographic Trends Assessment", "tail": "Kenya"}}, {"has_organization": {"head": "Family Planning Survey", "tail": "Regional Health Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "demographics, fertility, and population growth"}}
+{"input": "Economics of Disasters and Climate Change Barrett A, McIntosh R, Pritchard M, Hannan M, Alam Z, Marks M (2013) Asset Values: Why Are Some Households Doing Better than Others? Chars Livelihood Program Ceola S, Laio F, Montanari A (2014) Satellite nighttime lights reveal increasing human exposure to floods [worldwide. Geophys Res Lett 41(20):7184–7190. https://doi.org/10.1002/2014GL061859](https://doi.org/10.1002/2014GL061859) Chinh DT, Dung NV, Gain AK, Kreibich H (2017) Flood loss models and risk analysis for private households in can Tho City. Vietnam Water 9:313 De Lay S (2011) Slums in Ho Chi Minh City, Vietnam. Global Cities 2011. [http://hochiminhcity2011.jimdo.](http://hochiminhcity2011.jimdo.com/slums/) [com/slums/](http://hochiminhcity2011.jimdo.com/slums/) Del Valle, Alejandro et al. (2018) The Short-Term Economic Impact of Tropical Cyclones: Satellite Evidence from Guangdong Province. Econ Dis Climate Chang 1–11 DHI (2003) MIKE 11 - A Modelling System for Rivers and Channels. Short Introduction Tutorial. Water and Environment Fay M (2005) The Urban Poor in Latin America. Directions in Development - General. The World Bank. [http://elibrary.worldbank.org/doi/book/10.1596/0-8213-6069-8](http://elibrary.worldbank.org/doi/book/10.1596/0-8213-6069-8) FIM (2013) Final Report, Volume 2, Integrated Flood Management Strategy. Ho Chi Minh City Flood and Inundation Management Geographic Information Science and Technology (2015) Landscan Population Data. Oak Ridge National Laboratory [GFDRR (2015) Country Profile - Vietnam. https://www.gfdrr.org/sites/gfdrr/files/region/VN.pdf](https://www.gfdrr.org/sites/gfdrr/files/region/VN.pdf) Goeschl T, Managi S (2017) Public in-kind relief", "output": {"entities": {"named_data": ["Landscan Population Data"], "organization": ["Oak Ridge National Laboratory", "Geographic Information Science and Technology"]}, "relations": [{"has_organization": {"head": "Landscan Population Data", "tail": "Oak Ridge National Laboratory"}}, {"used_by": {"head": "Landscan Population Data", "tail": "Geographic Information Science and Technology"}}]}, "_meta": {"entry_id": 1204, "dataset_name": "Landscan Population Data"}}
+{"input": "21 Table 6: Percentage of displaced individuals deprived in selected indicators by gender Ethiopia South Sudan Sudan Male Female Male Female Male Female Years of schooling 55 78 * * * 36 63 * * * 32 46 * * * School attendance 16 19 * * 21 29 23 23 Early marriage 3 13 * * * 8 75 * * * 6 50 * * * Unemployment 7 5 * * * 2 0 * 3 3 Legal id 45 46 48 74 * * * 10 10 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Asterisks indicate statistical significance of mean differences between male and female at 1 % * * *, 5 % * * and 10 % * levels. Returning to patterns of household headship, Figure 5 breaks down the variation in censored headcount ratios among refugee households in Ethiopia, depending on the gender of the household head.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "organization": ["Authors"]}, "relations": [{"used_by": {"head": "High Frequency Surveys", "tail": "Authors"}}]}, "_meta": {"entry_id": 30, "dataset_name": "High Frequency Surveys"}}
+{"input": "The imputation of income poverty is done using the Turkish Labor Force Survey (LFS), and with information and modeling parameters determined from the Survey of Income and Living Conditions (SILC). More details and validation of this methodology is discussed throughout this paper. While explicit identification of Syrians in available surveys is not feasible, there is evidence of an increase in the amount of foreign-born individuals that is being captured in the LFS. The arrival year of foreign-born migrants is available in the data which allows for identification of “ Settled Migrants ” and “ Recent Migrants ”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the country, it is not surprising that they are accessible to interviews by the LFS. Despite data limitations, there are strong and significant trends in the poverty rates for the recent foreign- born, especially for those near the Syrian border. In 2013, recent migrants near the Syrian border were the poorest group4 in Turkey. While this statistic in itself is not initially surprising, fluctuating welfare trends of recent migrants over time is noteworthy.", "output": {"entities": {"named_data": ["Survey of Income and Living Conditions"], "organization": ["this paper"]}, "relations": [{"used_by": {"head": "Survey of Income and Living Conditions", "tail": "this paper"}}]}, "_meta": {"entry_id": 411, "dataset_name": "Survey of Income and Living Conditions"}}
+{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016).", "output": {"entities": {"named_data": ["Profile Global Registration System"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "Profile Global Registration System", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 440, "dataset_name": "Profile Global Registration System"}}
+{"input": "In recent assessments, the International Labor Organization (ILO) has made significant use of the Global Employment Trends Database to analyze labor market fluctuations across different regions. The database serves as a critical resource for understanding employment patterns and developing policies aimed at job creation.", "output": {"entities": {"named_data": ["Global Employment Trends Database"], "organization": ["International Labor Organization", "ILO"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Global Employment Trends Database", "tail": "International Labor Organization"}}, {"used_by": {"head": "Global Employment Trends Database", "tail": "ILO"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "employment, labor markets, and skills development"}}
+{"input": "The South Asian Trade Assessment Report 2022 provides a comprehensive analysis of trade dynamics and economic competitiveness in the region. This report, developed by the South Asia Economic Forum (SAEF), covers various aspects of trade relations among member countries between 2018 and 2022, highlighting trends and forecasts. Analysts from various organizations have utilized this dataset to explore the implications of trade policies in the context of regional cooperation, yet some critiques question the depth of the analysis due to possible data limitations. The findings underscore the importance of continuous monitoring of trade patterns to inform policy decisions.", "output": {"entities": {"named_data": ["South Asian Trade Assessment Report 2022"], "organization": ["South Asia Economic Forum"], "acronym": ["SAEF"], "year": ["2022", "2018 and 2022"], "geography": ["South Asia"]}, "relations": [{"has_acronym": {"head": "South Asian Trade Assessment Report 2022", "tail": "SAEF"}}, {"has_timeframe": {"head": "South Asian Trade Assessment Report 2022", "tail": "2018 and 2022"}}, {"has_geography": {"head": "South Asian Trade Assessment Report 2022", "tail": "South Asia"}}, {"has_organization": {"head": "South Asian Trade Assessment Report 2022", "tail": "South Asia Economic Forum"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The Regional Employment Trends Report 2022 (RETR) provides valuable insights into the labor market dynamics across East Africa, highlighting changes in employment rates and skills development over the past year. The data was collected through surveys conducted in Kenya and Uganda, enabling a comprehensive analysis of regional trends and disparities. The RETR dataset is widely used by policymakers and researchers for evidence-based decision-making, particularly in addressing youth unemployment challenges in these countries. Additionally, the report incorporates findings from the National Skills Development Assessment 2021, which specifically focuses on skills gaps in the workforce and is crucial for informing training programs. These datasets are instrumental in shaping employment policies and strategies aimed at promoting sustainable economic growth in the region.", "output": {"entities": {"named_data": ["Regional Employment Trends Report 2022", "National Skills Development Assessment 2021"], "organization": ["policymakers", "researchers"], "acronym": ["RETR"], "year": ["2022", "2021"], "geography": ["East Africa", "Kenya", "Uganda"]}, "relations": [{"has_acronym": {"head": "Regional Employment Trends Report 2022", "tail": "RETR"}}, {"has_timeframe": {"head": "Regional Employment Trends Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Regional Employment Trends Report 2022", "tail": "East Africa"}}, {"has_timeframe": {"head": "National Skills Development Assessment 2021", "tail": "2021"}}, {"has_geography": {"head": "National Skills Development Assessment 2021", "tail": "Kenya"}}, {"has_geography": {"head": "National Skills Development Assessment 2021", "tail": "Uganda"}}, {"used_by": {"head": "Regional Employment Trends Report 2022", "tail": "policymakers"}}, {"used_by": {"head": "Regional Employment Trends Report 2022", "tail": "researchers"}}, {"used_by": {"head": "National Skills Development Assessment 2021", "tail": "policymakers"}}, {"used_by": {"head": "National Skills Development Assessment 2021", "tail": "researchers"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "employment, labor markets, and skills development"}}
+{"input": "In recent discussions about the effectiveness of social protection programs, the findings from the National Social Safety Net Assessment provided crucial insights. This assessment highlights the various challenges faced by vulnerable populations and the need for targeted interventions. Analysts noted that understanding the distribution of resources is vital for enhancing program outcomes. Stakeholders have called for more comprehensive evaluations to inform policy adjustments and improve the overall impact of these safety nets.", "output": {"entities": {"named_data": ["National Social Safety Net Assessment"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "social protection and safety nets"}}
+{"input": "The Global Urban Land Use Change Database (GULUCD) from 2018 provides crucial insights into urbanization patterns across various regions. This database, published by the Urban Development Institute (UDI), has been utilized extensively by the Environmental Research Agency (ERA) in their recent studies on climate impacts. Meanwhile, the Rural Land Monitoring Survey (RLMS) 2020, also produced by UDI, focuses on agricultural land usage trends and is referenced in reports by the Food Security Council (FSC). As these organizations leverage the rich datasets, the importance of inter-agency collaboration in sustainable land management continues to grow.", "output": {"entities": {"named_data": ["Global Urban Land Use Change Database", "Rural Land Monitoring Survey"], "organization": ["Urban Development Institute", "Environmental Research Agency", "Food Security Council"], "acronym": ["GULUCD", "RLMS"], "year": ["2018", "2020"], "geography": ["various regions"]}, "relations": [{"has_organization": {"head": "Global Urban Land Use Change Database", "tail": "Urban Development Institute"}}, {"used_by": {"head": "Global Urban Land Use Change Database", "tail": "Environmental Research Agency"}}, {"has_acronym": {"head": "Global Urban Land Use Change Database", "tail": "GULUCD"}}, {"has_timeframe": {"head": "Global Urban Land Use Change Database", "tail": "2018"}}, {"has_organization": {"head": "Rural Land Monitoring Survey", "tail": "Urban Development Institute"}}, {"used_by": {"head": "Rural Land Monitoring Survey", "tail": "Food Security Council"}}, {"has_acronym": {"head": "Rural Land Monitoring Survey", "tail": "RLMS"}}, {"has_timeframe": {"head": "Rural Land Monitoring Survey", "tail": "2020"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "29 evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits. Program H (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004).", "output": {"entities": {"named_data": ["KAP (knowledge, attitudes and practices) survey"], "organization": ["Medical Research Council", "the Gambia program"]}, "relations": [{"has_organization": {"head": "KAP (knowledge, attitudes and practices) survey", "tail": "Medical Research Council"}}, {"used_by": {"head": "KAP (knowledge, attitudes and practices) survey", "tail": "the Gambia program"}}]}, "_meta": {"entry_id": 200, "dataset_name": "KAP (knowledge, attitudes and practices) survey"}}
+{"input": "The Water and Sanitation Assessment Report 2022 provides critical insights into the availability of clean water sources across various regions. This comprehensive analysis, produced by the Global Water Institute, aims to inform policy decisions and improve water access worldwide.", "output": {"entities": {"named_data": ["Water and Sanitation Assessment Report 2022"], "organization": ["Global Water Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Water and Sanitation Assessment Report 2022", "tail": "Global Water Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "water, sanitation, and hygiene"}}
+{"input": "The study will conduct an ex-ante micro- simulation using pre-crisis household data (IHSES 2012) and macroeconomic projections for 2014 to gauge the distributional impact of the crises across groups (e. g. individuals and / or households, sectors, IDPs and host communities) and space (e. g. urban / rural, governorates).  The Economic and Social Impact Assessment for Kurdistan Region of Iraq [completed in 2015] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Lebanon Economic and Social Impact Assessment of the Syria Conflict [completed in 2013] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Bank and UNHCR undertook a welfare assessment of Syrian refugees living in Jordan and Lebanon [completed in 2016] focusing on welfare, poverty and vulnerability.", "output": {"entities": {"named_data": ["IHSES 2012"], "organization": ["The Bank"]}, "relations": [{"used_by": {"head": "IHSES 2012", "tail": "The Bank"}}]}, "_meta": {"entry_id": 88, "dataset_name": "IHSES 2012"}}
+{"input": "The recent Fertility and Population Growth Assessment Report 2022, published by the National Statistics Agency, provides insightful data on demographic changes in urban regions. This report has been extensively used by the Population Research Institute to analyze trends affecting urban fertility rates. Furthermore, the Global Demographic Data (GDD) database, which is maintained by the World Population Foundation, offers a comprehensive view of population statistics across various countries from 2015 to 2020. The World Health Organization has cited GDD in its recent publications to underscore the importance of understanding population dynamics in health policies. Together, these datasets form a critical backbone for demographic research in contemporary urban studies.", "output": {"entities": {"named_data": ["Fertility and Population Growth Assessment Report 2022", "Global Demographic Data"], "organization": ["National Statistics Agency", "Population Research Institute", "World Population Foundation", "World Health Organization"], "acronym": [], "year": ["2022", "2015 to 2020"], "geography": ["urban regions"]}, "relations": [{"has_organization": {"head": "Fertility and Population Growth Assessment Report 2022", "tail": "National Statistics Agency"}}, {"used_by": {"head": "Fertility and Population Growth Assessment Report 2022", "tail": "Population Research Institute"}}, {"has_organization": {"head": "Global Demographic Data", "tail": "World Population Foundation"}}, {"has_timeframe": {"head": "Global Demographic Data", "tail": "2015 to 2020"}}, {"used_by": {"head": "Global Demographic Data", "tail": "World Health Organization"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "demographics, fertility, and population growth"}}
+{"input": "The recent study on social safety nets analyzed the data from the 2020 Global Social Protection Report (GSPR) published by the International Labour Organization (ILO) focusing on the effects of cash transfers in sub-Saharan Africa. Additionally, the 2021 Household Economic Survey (HES) from Nigeria provided valuable insights into the socio-economic conditions of vulnerable populations. Both datasets, alongside the World Bank's Social Safety Nets Database (SSNDB), which covers data from 2015 to 2022, serve as essential resources for policymakers and researchers alike. The SSNDB is particularly notable for its comprehensive coverage of social protection programs in various countries, enhancing understanding of social support mechanisms at a global scale.", "output": {"entities": {"named_data": ["Global Social Protection Report", "Household Economic Survey", "Social Safety Nets Database"], "organization": ["International Labour Organization", "World Bank"], "acronym": ["GSPR", "HES", "SSNDB"], "year": ["2020", "2021", "2015 to 2022"], "geography": ["sub-Saharan Africa", "Nigeria"]}, "relations": [{"has_acronym": {"head": "Global Social Protection Report", "tail": "GSPR"}}, {"has_timeframe": {"head": "Global Social Protection Report", "tail": "2020"}}, {"has_geography": {"head": "Global Social Protection Report", "tail": "sub-Saharan Africa"}}, {"has_acronym": {"head": "Household Economic Survey", "tail": "HES"}}, {"has_timeframe": {"head": "Household Economic Survey", "tail": "2021"}}, {"has_geography": {"head": "Household Economic Survey", "tail": "Nigeria"}}, {"has_acronym": {"head": "Social Safety Nets Database", "tail": "SSNDB"}}, {"has_timeframe": {"head": "Social Safety Nets Database", "tail": "2015 to 2022"}}, {"has_organization": {"head": "Global Social Protection Report", "tail": "International Labour Organization"}}, {"has_organization": {"head": "Social Safety Nets Database", "tail": "World Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "social protection and safety nets"}}
+{"input": "Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey.", "output": {"entities": {"named_data": ["Displaced People Survey"], "organization": ["Listening to Displaced People Survey"]}, "relations": [{"has_organization": {"head": "Displaced People Survey", "tail": "Listening to Displaced People Survey"}}]}, "_meta": {"entry_id": 1104, "dataset_name": "Displaced People Survey"}}
+{"input": "The East African Public Finance Management Survey (EAPFMS) conducted in 2022 provides valuable insights into the fiscal policies of the region. This comprehensive analysis focuses on countries like Kenya and Uganda, helping to assess domestic revenue generation strategies. The survey, utilized by various governmental organizations, highlights the challenges faced in optimizing resource allocation. The findings are expected to inform future reforms in public finance management across East Africa.", "output": {"entities": {"named_data": ["East African Public Finance Management Survey"], "organization": ["governmental organizations"], "acronym": ["EAPFMS"], "year": ["2022"], "geography": ["Kenya", "Uganda", "East Africa"]}, "relations": [{"has_acronym": {"head": "East African Public Finance Management Survey", "tail": "EAPFMS"}}, {"has_timeframe": {"head": "East African Public Finance Management Survey", "tail": "2022"}}, {"has_geography": {"head": "East African Public Finance Management Survey", "tail": "Kenya"}}, {"has_geography": {"head": "East African Public Finance Management Survey", "tail": "Uganda"}}, {"has_geography": {"head": "East African Public Finance Management Survey", "tail": "East Africa"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "public financial management and domestic revenue"}}
+{"input": "Economics of Disasters and Climate Change vulnerability even as the climate change hazard increases (Hallegatte et al. 2016). Along these lines, while we examine which regions within Vietnam have the highest flood exposure, we do not examine the determinants of vulnerability (other than poverty). Recent analyses suggest that the Northwest, Central Highlands, and Mekong River Delta have the greatest socioeconomic vulnerability (World Bank 2010). In the flood hazard maps developed for this paper, we assume no protection due to a lack of data and as a result the hazard maps present an upper bound of flood exposure. Work is currently ongoing to develop a global database of flood protection, and this information can be mobilized for future work (Scussolini et al. 2016). For the national-level analysis, flooded areas are defined as any area with inundation higher than 0. We have not yet explored the depth dimension, although the flood hazard maps developed for this study allow for this potential in future work. For the HCMC analysis, the location of the slum areas in the PUMA data set are mainly restricted to the old town. Furthermore, slum areas are often difficult to define (with PUMA only identifying potential slums) and the", "output": {"entities": {"named_data": ["PUMA data set"], "organization": ["PUMA", "this paper"]}, "relations": [{"has_organization": {"head": "PUMA data set", "tail": "PUMA"}}, {"used_by": {"head": "PUMA data set", "tail": "this paper"}}]}, "_meta": {"entry_id": 1236, "dataset_name": "PUMA data set"}}
+{"input": "The Urban Infrastructure Assessment Report 2022, published by the Global Urban Development Organization (GUDO), provides a comprehensive overview of urban transport projects across various cities. This assessment has been extensively utilized by the International Transportation Forum (ITF) for their recent publication on sustainable urban mobility solutions. Additionally, the report covers data specific to the metropolitan areas of Brazil, which highlights the pressing need for enhanced public transport options in the region. The ITF's usage of the GUDO report underscores the collaborative effort in addressing urban transportation challenges.", "output": {"entities": {"named_data": ["Urban Infrastructure Assessment Report 2022"], "organization": ["Global Urban Development Organization", "International Transportation Forum"], "acronym": ["GUDO", "ITF"], "year": ["2022"], "geography": ["Brazil"]}, "relations": [{"has_organization": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "Global Urban Development Organization"}}, {"used_by": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "International Transportation Forum"}}, {"has_acronym": {"head": "Global Urban Development Organization", "tail": "GUDO"}}, {"has_acronym": {"head": "International Transportation Forum", "tail": "ITF"}}, {"has_timeframe": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "Brazil"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "urban infrastructure and transportation planning"}}
+{"input": "33 In school: an indicator for whether the respondent currently attends regular education (schooling). This does not preclude also being employed. Retired: an indicator for a respondent who declares that they are not engaged in job search because they are retired. Full / part-time employment: an indicator for whether a person works full or part-time for all people in private sector employment (see above definition of employment). Full-time employment is defined as usual working hours of 30 or more hours per week, part-time employment as usual working hours of less than 30 hours per week. We do not use the indicator provided in the LFS data since there seems to be some confusion in which category 30 hours per week falls (with these evenly divided between full and part-time). Education: we classify people into three education categories. Low education is defined as those with no completed formal education. Medium education is defined as those with at least completed primary education but no high school completion. Higher education is defined as people who have at least completed high school.", "output": {"entities": {"named_data": ["LFS data"], "organization": ["LFS"]}, "relations": [{"has_organization": {"head": "LFS data", "tail": "LFS"}}]}, "_meta": {"entry_id": 322, "dataset_name": "LFS data"}}
+{"input": "Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka\nand Guillaume (2018). The data are primarily based on sub-national GDP per capita data constructed by\nGennaioli, _et al._ (2013) and covers 82 countries, representing 85% of the global population and 92% of\nglobal total GDP (PPP) in 2015. Population data is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).\n\nwe use World Bank income group classifications to divide the world into developing countries (that includes low-income, lower-middle and upper-middle income countries), and high-income countries.\n\nWe also use the Global Aridity Index and Potential Evapotranspiration Climate Database (Trabucco and Zomer 2019) to differentiate grid cells based on their aridity.", "output": {"entities": {"named_data": ["HYDE 3.2"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "HYDE 3.2", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1039, "dataset_name": "HYDE 3.2"}}
+{"input": "The Digital Connectivity Survey (DCS) conducted by the Global Tech Institute in 2022 highlights various aspects of internet access and usage across different demographics. This survey, which aims to assess technology adoption in low-income regions, particularly focuses on Sub-Saharan Africa. Additionally, the Tech Adoption Assessment Report 2023, published by the International Development Agency, provides an in-depth analysis of the shifts in technology use influenced by economic factors. The findings from both datasets are expected to guide policy recommendations for improving digital infrastructure in emerging economies. Source: Global Tech Institute, elaboration based on DCS.", "output": {"entities": {"named_data": ["Digital Connectivity Survey", "Tech Adoption Assessment Report 2023"], "organization": ["Global Tech Institute", "International Development Agency"], "acronym": ["DCS"], "year": ["2022", "2023"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Digital Connectivity Survey", "tail": "DCS"}}, {"has_timeframe": {"head": "Digital Connectivity Survey", "tail": "2022"}}, {"has_geography": {"head": "Digital Connectivity Survey", "tail": "Sub-Saharan Africa"}}, {"has_timeframe": {"head": "Tech Adoption Assessment Report 2023", "tail": "2023"}}, {"has_organization": {"head": "Tech Adoption Assessment Report 2023", "tail": "International Development Agency"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "digital development and technology adoption"}}
+{"input": "The Global Migration Trends Report 2022 presents significant insights into patterns of forced displacement and migration worldwide. Compiled by the International Organization for Migration (IOM), this report offers a comprehensive overview of the demographic shifts that have occurred over the past year, highlighting regions most affected by these changes.", "output": {"entities": {"named_data": ["Global Migration Trends Report 2022"], "organization": ["International Organization for Migration"], "acronym": ["IOM"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Global Migration Trends Report 2022", "tail": "International Organization for Migration"}}, {"has_acronym": {"head": "Global Migration Trends Report 2022", "tail": "IOM"}}, {"has_timeframe": {"head": "Global Migration Trends Report 2022", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "forced displacement, refugees, and migration"}}
+{"input": "80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.23: Work type by gender Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.24: Occupation Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.25: Occupation among completed secondary or more Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 37 These results indicate that the OCP model is not ideal for refugees’ labor market inclusion. Few refugees primarily enroll through an existing formal employer; thus, the OCP is mainly open to refugees with networks that can support them with remittances, and this makes these households less likely to work", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank Staff"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 777, "dataset_name": "SESRE 2023"}}
+{"input": "The Industrial Performance Index (IPI) provides comprehensive insights into the competitiveness of various sectors in the economy. This dataset, published by the Economic Research Agency, analyzes metrics such as productivity, innovation, and trade performance across multiple regions. The findings from the IPI are crucial for policymakers aiming to enhance industrial growth and economic resilience.", "output": {"entities": {"named_data": ["Industrial Performance Index"], "organization": ["Economic Research Agency"], "acronym": ["IPI"], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Industrial Performance Index", "tail": "Economic Research Agency"}}, {"has_acronym": {"head": "Industrial Performance Index", "tail": "IPI"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The Global Agriculture Monitoring Survey (GAMS) conducted by the International Food Policy Research Institute (IFPRI) in 2022 provides critical insights into crop yields in Sub-Saharan Africa. These findings are instrumental for the Food and Agriculture Organization (FAO), which utilized the GAMS data to inform its Global Food Security Assessment 2023 report. Additionally, the FAO has collaborated with the International Fund for Agricultural Development (IFAD), which published the Rural Development Impact Report (RDIR) covering the period from 2019 to 2021. These datasets collectively enhance understanding of food security trends across developing regions.", "output": {"entities": {"named_data": ["Global Agriculture Monitoring Survey", "Global Food Security Assessment 2023 report", "Rural Development Impact Report"], "organization": ["International Food Policy Research Institute", "Food and Agriculture Organization", "International Fund for Agricultural Development"], "acronym": ["GAMS", "FAO", "IFAD", "RDIR"], "year": ["2022", "2023", "2019 to 2021"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Global Agriculture Monitoring Survey", "tail": "International Food Policy Research Institute"}}, {"used_by": {"head": "Global Agriculture Monitoring Survey", "tail": "Food and Agriculture Organization"}}, {"has_timeframe": {"head": "Rural Development Impact Report", "tail": "2019 to 2021"}}, {"has_organization": {"head": "Rural Development Impact Report", "tail": "International Fund for Agricultural Development"}}, {"has_acronym": {"head": "Global Agriculture Monitoring Survey", "tail": "GAMS"}}, {"has_acronym": {"head": "Global Food Security Assessment 2023 report", "tail": "FAO"}}, {"has_acronym": {"head": "Rural Development Impact Report", "tail": "RDIR"}}, {"has_timeframe": {"head": "Global Food Security Assessment 2023 report", "tail": "2023"}}, {"has_geography": {"head": "Global Agriculture Monitoring Survey", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "agriculture, food security, and nutrition"}}
+{"input": "Recent analyses have highlighted the importance of macroeconomic trends in shaping financial inclusion strategies across different contexts. According to the Economic Dynamics Report 2022, there are significant variations in how economic indicators influence access to financial services for underserved populations. This report provides a comprehensive overview of the factors that hinder or facilitate financial inclusion, offering valuable insights for policymakers and researchers alike.", "output": {"entities": {"named_data": ["Economic Dynamics Report 2022"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father ’ s schooling, mother ’ s schooling, and local supply of schools in subdistrict of birth. 9 These exclusions resulted in a sample of 4, 139 males and 4, 131 females, which are referred to as the male and female full samples, respectively. 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts. 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA. 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8, 312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father ’ s schooling), and 7 observations (missing mother ’ s schooling).", "output": {"entities": {"named_data": ["2010 school census"], "organization": ["Ministry of Education"]}, "relations": [{"used_by": {"head": "2010 school census", "tail": "Ministry of Education"}}]}, "_meta": {"entry_id": 490, "dataset_name": "2010 school census"}}
+{"input": "The recent Comprehensive Demographic Assessment Report 2022 provides crucial insights into population trends and fertility rates across various regions. Published by the National Institute of Statistics, this report is an essential resource for policymakers and researchers who aim to address demographic challenges in their areas of focus.", "output": {"entities": {"named_data": ["Comprehensive Demographic Assessment Report 2022"], "organization": ["National Institute of Statistics"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Comprehensive Demographic Assessment Report 2022", "tail": "National Institute of Statistics"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "demographics, fertility, and population growth"}}
+{"input": "agricultural extension officers from MoA. (b) **Co-investments in subprojects.** While all eligible farmers and cooperatives will receive TA, only a subset will receive investment from the project for assets or working capital. Eligible farmers will be incentivized to apply for financing through well-organized cooperatives, common interest groups or producer groups. Eligibility criteria might include (i) businesses already operating with well-maintained financial records, (ii) climate-smart business plan, (iii) potential demand for products or services, (iv) financial viability and technical feasibility of business plan, (v) willingness to take up climate adaptation and mitigation measures, and (vi) preference for women and youth. Precise selection criteria will be developed by the service provider in consultation with local officials from the MoA, with proposals screened by a committee of local sector experts. Up to 70 percent of business proposals will be funded through the project. The co-investee farmers or cooperatives will receive additional technical support on the operation and maintenance of purchased assets. A grants’ manual will be developed. 36 World Bank. 2023. _Zambia Gender Assessment_ . 37 Finscope Survey 2020 and in-person consultations at Meheba settlement 2022/23. Page 19", "output": {"entities": {"named_data": ["Finscope Survey 2020"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Finscope Survey 2020", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1242, "dataset_name": "Finscope Survey 2020"}}
+{"input": "interactions between hosts and refugees may help improve hosts’ attitudes (Betts et al., 2023). In some contexts, refugee inflows have been found to harden in-group identification and increase support for ideological extremes. This was the case with refugee inflows in Denmark, for instance, but only in rural areas (Dustmann et al., 2019). On the other hand, refugees hosted in Austrian municipalities for extended periods, as opposed to those who passed through, were found to reduce support for anti-immigrant parties, pointing to the benefits of refugee-host interactions (Steinmayr, 2021). All-in-all, there is little evidence that refugee hosting tends to worsen attitudes toward refugees in the Global South (World Bank, 2023b). 7.1 Attitudes between refugees and hosts SESRE data show that, while some hosts have negative attitudes towards refugees, most attitudes are generally positive. Sixty-five percent of hosts agree that refugees are friendly and good people, and only 20 percent are uncomfortable with having a refugee neighbor. This is an important finding, highlighting the potential for integration policies. Host attitudes are generally most favorable in the Somali region and most negative around South Sudanese camps; the share not comfortable with having a refugee neighbor increases to 37 percent in the South Sudanese", "output": {"entities": {"named_data": ["SESRE data"], "organization": ["SESRE", "World Bank"]}, "relations": [{"has_organization": {"head": "SESRE data", "tail": "SESRE"}}, {"used_by": {"head": "SESRE data", "tail": "World Bank"}}]}, "_meta": {"entry_id": 96, "dataset_name": "SESRE data"}}
+{"input": "UNHCR \r ProGres \r data \r on \r the \r 83,044 \r registered \r refugees \r in \r Thailand \r indicates \r an\nestimated \r 84 \r per \r cent \r are \r ethnic \r Karen \r and \r 12 \r per \r cent \r are \r ethnic \r Karenni. \r The\nremaining \r 4 \r per \r cent \r are \r of \r Burman, \r Shan, \r and \r Mon \r descent, \r and \r other \r groups. \r The\nmajority \r of \r registered \r refugees \r come \r from \r Kayin \r State \r (65.3 \r per \r cent), \r followed \r by\nKayah \r (14.6 \r per \r cent), \r Tanintharyi \r (7.3 \r per \r cent), \r Bago \r (5.2 \r per \r cent) \r and \r Mon \r (5 \r per\ncent). \r (Annex: \r Myanmar \r Thailand \r Border \r – \r Refugee \r Overview, \r as \r of \r end \r of \r March \r 2013)", "output": {"entities": {"named_data": ["UNHCR \r ProGres \r data"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR \r ProGres \r data", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 1349, "dataset_name": "UNHCR \r ProGres \r data"}}
+{"input": "The Urban Mobility Assessment 2022 published by the City Analytics Institute provides crucial insights into public transportation trends in urban areas. This data is extensively used by the Metropolitan Planning Organization (MPO) for its strategic development plans. Additionally, the Global Urban Infrastructure Database (GUID) released in 2021 contains comprehensive datasets on urban infrastructure projects across various countries. The World Infrastructure Network relies on the GUID to inform their reports on global urban development. Lastly, the Urban Transport Survey (UTS) 2020 offers detailed statistics on urban transit systems in Southeast Asia, which are utilized by the Southeast Asia Transport Forum to better understand transportation challenges in the region.", "output": {"entities": {"named_data": ["Urban Mobility Assessment 2022", "Global Urban Infrastructure Database", "Urban Transport Survey"], "organization": ["City Analytics Institute", "Metropolitan Planning Organization", "World Infrastructure Network", "Southeast Asia Transport Forum"], "acronym": ["Urban Transport Survey"], "year": ["2022", "2021", "2020"], "geography": ["Southeast Asia"]}, "relations": [{"has_organization": {"head": "Urban Mobility Assessment 2022", "tail": "City Analytics Institute"}}, {"used_by": {"head": "Urban Mobility Assessment 2022", "tail": "Metropolitan Planning Organization"}}, {"has_organization": {"head": "Global Urban Infrastructure Database", "tail": "World Infrastructure Network"}}, {"used_by": {"head": "Global Urban Infrastructure Database", "tail": "World Infrastructure Network"}}, {"has_timeframe": {"head": "Global Urban Infrastructure Database", "tail": "2021"}}, {"has_organization": {"head": "Urban Transport Survey", "tail": "Southeast Asia Transport Forum"}}, {"used_by": {"head": "Urban Transport Survey", "tail": "Southeast Asia Transport Forum"}}, {"has_timeframe": {"head": "Urban Transport Survey", "tail": "2020"}}, {"has_geography": {"head": "Urban Transport Survey", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "urban infrastructure and transportation planning"}}
+{"input": "The analysis of gender disparities in the workforce relies heavily on the 2022 Global Gender Equality Survey (GGES), conducted across various regions. This dataset, produced by the International Labor Organization (ILO), covers data from multiple countries, including Brazil and Kenya, providing insights into women's economic participation. The findings highlight critical trends over time, such as the increasing wage gap from 2018 to 2022. Additionally, the Women's Economic Empowerment Assessment Report 2021 (WEA Report) has been utilized by several NGOs to design programs aimed at improving economic opportunities for women in rural areas. This report focuses on the specific challenges faced by women in South Asia and Sub-Saharan Africa, offering a comprehensive look at the barriers to economic equality that persist today.", "output": {"entities": {"named_data": ["Global Gender Equality Survey", "Women's Economic Empowerment Assessment Report 2021"], "organization": ["International Labor Organization", "ILO"], "acronym": ["GGES", "WEA Report"], "year": ["2022", "2018 to 2022", "2021"], "geography": ["Brazil", "Kenya", "South Asia", "Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Global Gender Equality Survey", "tail": "GGES"}}, {"has_timeframe": {"head": "Global Gender Equality Survey", "tail": "2022"}}, {"has_geography": {"head": "Global Gender Equality Survey", "tail": "Brazil"}}, {"has_geography": {"head": "Global Gender Equality Survey", "tail": "Kenya"}}, {"has_timeframe": {"head": "Women's Economic Empowerment Assessment Report 2021", "tail": "2021"}}, {"has_acronym": {"head": "Women's Economic Empowerment Assessment Report 2021", "tail": "WEA Report"}}, {"has_geography": {"head": "Women's Economic Empowerment Assessment Report 2021", "tail": "South Asia"}}, {"has_geography": {"head": "Women's Economic Empowerment Assessment Report 2021", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Global Gender Equality Survey", "tail": "International Labor Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
+{"input": "4 The only publicly available sources of data to document patterns of enrollment and available educational options for Pakistani families are household-based surveys. These are the official 1998 Census of Population (Government of Pakistan) 3, the 1991, 1998, and 2001 rounds of the Pakistan Integrated Household Survey4, and a 2003 census of schooling choice conducted by our research team. The fact that three sources use different definitions of madrassa enrollment, and were collected at different times by individuals with very different institutional affiliations provides independent verification of enrollment estimates and allows us to determine the sensitivity of our results. The household data tell us whether a child is enrolled full-time in a madrassa, but not whether a child goes for an hour on any given day to study the Quran. Therefore this data does not confound full-time with part-time attendees — a child who attends a public school during the day and a madrassa in the evening is recorded as enrolled in a public school. This is an important distinction since parents might use a modicum of madrassa or mosque based education to teach their children about religion. Consequently, if we contrast these household-based numbers with numbers from establishment-based reports, discrepancies can arise. From virtually any policy perspective, including evening quran classes in enrollment figures seems misguided.", "output": {"entities": {"named_data": ["Pakistan Integrated Household Survey"], "organization": ["our research team"]}, "relations": [{"used_by": {"head": "Pakistan Integrated Household Survey", "tail": "our research team"}}]}, "_meta": {"entry_id": 968, "dataset_name": "Pakistan Integrated Household Survey"}}
+{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016).", "output": {"entities": {"named_data": ["Jordan Home Visits round 3"], "organization": ["UNHCR", "we"]}, "relations": [{"has_organization": {"head": "Jordan Home Visits round 3", "tail": "UNHCR"}}, {"used_by": {"head": "Jordan Home Visits round 3", "tail": "we"}}]}, "_meta": {"entry_id": 270, "dataset_name": "Jordan Home Visits round 3"}}
+{"input": "In recent years, the Global Displacement Report (GDR) has provided crucial insights into the challenges faced by refugees and internally displaced persons. The 2022 edition of the report covers worldwide displacement trends, focusing on the increased numbers due to conflicts and climate change. This report, utilized by various humanitarian organizations, highlights the urgent need for targeted policies and assistance in regions such as East Africa and the Middle East. With its comprehensive data analysis, the GDR continues to serve as a vital resource for understanding forced migration dynamics.", "output": {"entities": {"named_data": ["Global Displacement Report"], "organization": ["humanitarian organizations"], "acronym": ["GDR"], "year": ["2022"], "geography": ["East Africa", "Middle East"]}, "relations": [{"has_acronym": {"head": "Global Displacement Report", "tail": "GDR"}}, {"has_timeframe": {"head": "Global Displacement Report", "tail": "2022"}}, {"has_geography": {"head": "Global Displacement Report", "tail": "East Africa"}}, {"has_geography": {"head": "Global Displacement Report", "tail": "Middle East"}}, {"used_by": {"head": "Global Displacement Report", "tail": "humanitarian organizations"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "forced displacement, refugees, and migration"}}
+{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer.", "output": {"entities": {"named_data": ["Linking Ethnic Data from Africa"], "organization": ["Afrobarometer", "M ¨ uller-Crepon et al. (2020)"]}, "relations": [{"has_organization": {"head": "Linking Ethnic Data from Africa", "tail": "Afrobarometer"}}, {"used_by": {"head": "Linking Ethnic Data from Africa", "tail": "M ¨ uller-Crepon et al. (2020)"}}]}, "_meta": {"entry_id": 996, "dataset_name": "Linking Ethnic Data from Africa"}}
+{"input": "The Economic Competitiveness Assessment Report 2022, produced by the International Economic Forum (IEF), provides a comprehensive analysis of trade patterns in developing countries. This report highlights vital indicators and trends that have been utilized by various NGOs, including the Global Trade Initiative (GTI), for their recent projects aimed at enhancing trade efficiency. The assessment covers regions such as Sub-Saharan Africa and Southeast Asia, making the findings particularly relevant for policymakers in these areas. Source: IEF elaboration based on Economic Competitiveness Assessment Report 2022.", "output": {"entities": {"named_data": ["Economic Competitiveness Assessment Report 2022"], "organization": ["International Economic Forum", "Global Trade Initiative"], "acronym": ["GTI"], "year": ["2022"], "geography": ["Sub-Saharan Africa", "Southeast Asia"]}, "relations": [{"has_organization": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "International Economic Forum"}}, {"used_by": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "Global Trade Initiative"}}, {"has_acronym": {"head": "Global Trade Initiative", "tail": "GTI"}}, {"has_timeframe": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "Sub-Saharan Africa"}}, {"has_geography": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The Digital Connectivity Assessment 2022, conducted by the Global Tech Initiative, provides critical insights into internet access across various regions. This comprehensive dataset, often referred to as DCA2022, analyzes connectivity trends in Africa, Asia, and Latin America. Despite the extensive coverage, it highlights significant disparities in digital adoption in rural areas. Another valuable resource is the Technology Impact Survey (TIS) 2021, which was utilized by multiple NGOs to evaluate the effects of technology on local economies in Eastern Europe. This survey, however, is limited to urban centers, which may skew the results. Lastly, the Mobile Internet Usage Report 2023 reveals trends in mobile data usage across the globe, but it lacks an acronym, making it less memorable in discussions among policymakers.", "output": {"entities": {"named_data": ["Digital Connectivity Assessment 2022", "Technology Impact Survey", "Mobile Internet Usage Report 2023"], "organization": ["Global Tech Initiative", "NGOs"], "acronym": ["DCA2022", "TIS"], "year": ["2022", "2021", "2023"], "geography": ["Africa", "Asia", "Latin America", "Eastern Europe"]}, "relations": [{"has_acronym": {"head": "Digital Connectivity Assessment 2022", "tail": "DCA2022"}}, {"has_timeframe": {"head": "Digital Connectivity Assessment 2022", "tail": "2022"}}, {"has_geography": {"head": "Digital Connectivity Assessment 2022", "tail": "Africa"}}, {"has_geography": {"head": "Digital Connectivity Assessment 2022", "tail": "Asia"}}, {"has_geography": {"head": "Digital Connectivity Assessment 2022", "tail": "Latin America"}}, {"used_by": {"head": "Technology Impact Survey", "tail": "NGOs"}}, {"has_timeframe": {"head": "Technology Impact Survey", "tail": "2021"}}, {"has_geography": {"head": "Technology Impact Survey", "tail": "Eastern Europe"}}, {"has_timeframe": {"head": "Mobile Internet Usage Report 2023", "tail": "2023"}}, {"has_geography": {"head": "Mobile Internet Usage Report 2023", "tail": "the globe"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "digital development and technology adoption"}}
+{"input": "The recent Geospatial Land Use Mapping Report, published in 2022 by the Global Environmental Institute, provides critical insights into land use changes across the African continent. This report incorporates advanced satellite imagery and remote sensing data to analyze urban expansion and agricultural development. The findings illustrate significant trends in land utilization, supporting policymakers in making informed decisions regarding sustainable development.", "output": {"entities": {"named_data": ["Geospatial Land Use Mapping Report"], "organization": ["Global Environmental Institute"], "acronym": [], "year": ["2022"], "geography": ["Africa"]}, "relations": [{"has_organization": {"head": "Geospatial Land Use Mapping Report", "tail": "Global Environmental Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "The Comprehensive Refugee Survey (CRS) conducted in 2022 by the International Refugee Agency provides critical insights into the demographics and living conditions of displaced populations in Eastern Africa. This dataset has been instrumental for various NGOs, including Global Relief Network, which utilized the CRS findings to improve their outreach programs. Additionally, the Migrant Health Assessment Report 2023, published by the World Health Organization, focuses on health outcomes for refugees and migrants across the continent. Both reports were used in conjunction with the Eastern Africa Migration Database, a collaborative effort by multiple organizations aimed at tracking migration patterns and challenges in the region.", "output": {"entities": {"named_data": ["Comprehensive Refugee Survey", "Migrant Health Assessment Report 2023", "Eastern Africa Migration Database"], "organization": ["International Refugee Agency", "Global Relief Network", "World Health Organization"], "acronym": ["Comprehensive Refugee Survey", "Migrant Health Assessment", "Eastern Africa Migration Database"], "year": ["2022", "2023"], "geography": ["Eastern Africa"]}, "relations": [{"has_organization": {"head": "Comprehensive Refugee Survey", "tail": "International Refugee Agency"}}, {"used_by": {"head": "Comprehensive Refugee Survey", "tail": "Global Relief Network"}}, {"has_organization": {"head": "Migrant Health Assessment Report 2023", "tail": "World Health Organization"}}, {"has_geography": {"head": "Migrant Health Assessment Report 2023", "tail": "Eastern Africa"}}, {"used_by": {"head": "Migrant Health Assessment Report 2023", "tail": "Global Relief Network"}}, {"has_organization": {"head": "Eastern Africa Migration Database", "tail": "multiple organizations"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "forced displacement, refugees, and migration"}}
+{"input": "The Climate Adaptation Strategies Report 2022, published by the Global Environmental Fund, provides crucial insights on resilience practices for vulnerable regions. This dataset has been utilized by the International Disaster Relief Agency to formulate response strategies for the Caribbean, particularly in the wake of increasing storm frequency. Furthermore, the Urban Resilience Assessment (URA) 2021, developed by the Urban Development Institute, is referenced extensively in reports by local governments in South America looking to improve infrastructure durability against climate impacts.", "output": {"entities": {"named_data": ["Climate Adaptation Strategies Report 2022", "Urban Resilience Assessment (URA) 2021"], "organization": ["Global Environmental Fund", "International Disaster Relief Agency", "Urban Development Institute", "local governments"], "acronym": ["Urban Resilience Assessment"], "year": ["2022", "2021"], "geography": ["Caribbean", "South America"]}, "relations": [{"has_organization": {"head": "Climate Adaptation Strategies Report 2022", "tail": "Global Environmental Fund"}}, {"used_by": {"head": "Climate Adaptation Strategies Report 2022", "tail": "International Disaster Relief Agency"}}, {"has_geography": {"head": "Climate Adaptation Strategies Report 2022", "tail": "Caribbean"}}, {"has_organization": {"head": "Urban Resilience Assessment (URA) 2021", "tail": "Urban Development Institute"}}, {"used_by": {"head": "Urban Resilience Assessment (URA) 2021", "tail": "local governments"}}, {"has_geography": {"head": "Urban Resilience Assessment (URA) 2021", "tail": "South America"}}, {"has_timeframe": {"head": "Climate Adaptation Strategies Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Urban Resilience Assessment (URA) 2021", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016).", "output": {"entities": {"named_data": ["Labor Force Surveys"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "Labor Force Surveys", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 985, "dataset_name": "Labor Force Surveys"}}
+{"input": "In response to the increasing levels of violence in urban areas, the Global Urban Violence Survey (GUVS) conducted in 2022 provides critical insights into the underlying causes and trends of this phenomenon. Published by the International Conflict Research Institute (ICRI), this dataset has been pivotal for organizations such as the Urban Safety Coalition (USC), which has utilized it to inform their policy recommendations. Additionally, the 2021 Fragility and Resilience Assessment (FRA) covering Middle Eastern countries has been analyzed by various non-governmental organizations to shape their interventions, with the report produced by the Global Fragility Initiative (GFI) offering a comprehensive overview of resilience strategies. These datasets collectively underscore the importance of data-driven approaches in addressing urban violence and fragility.", "output": {"entities": {"named_data": ["Global Urban Violence Survey", "Fragility and Resilience Assessment"], "organization": ["International Conflict Research Institute", "Urban Safety Coalition", "Global Fragility Initiative"], "acronym": ["GUVS", "USC", "FRA", "GFI"], "year": ["2022", "2021"], "geography": ["Middle Eastern countries"]}, "relations": [{"has_organization": {"head": "Global Urban Violence Survey", "tail": "International Conflict Research Institute"}}, {"used_by": {"head": "Global Urban Violence Survey", "tail": "Urban Safety Coalition"}}, {"has_timeframe": {"head": "Global Urban Violence Survey", "tail": "2022"}}, {"has_organization": {"head": "Fragility and Resilience Assessment", "tail": "Global Fragility Initiative"}}, {"used_by": {"head": "Fragility and Resilience Assessment", "tail": "various non-governmental organizations"}}, {"has_timeframe": {"head": "Fragility and Resilience Assessment", "tail": "2021"}}, {"has_geography": {"head": "Fragility and Resilience Assessment", "tail": "Middle Eastern countries"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "conflict, fragility, and violence"}}
+{"input": "**employed before their displacement.**\nIn the SEIS survey, employment rates among refugees aged 18-64 previously employed or self-employed in Ukraine, are 81% and 91% respectively. That is already very high and any further increase would be marginal. On the other hand, those who back in Ukraine managed the household have an employment rate in Poland of 38%. Even before becoming refugees, they would have required support to enter the workforce and now, in the host country, they would be likely to benefit from such help even more. Some of them may be discouraged by not having been able to find a job, others may be marginally attached workers who fell outside the labour force, but have some desire and ability to return to work.\n\n**Chart 25. Ukrainian refugees’ employment rate in the 18-64 age group by previous**\n\n**status in Ukraine**\n\n91% Household Others Studying Employed Self-employed responsibilities Source: Deloitte own elaboration based on SEIS UNHCR survey conducted in May and June 2024.", "output": {"entities": {"named_data": ["SEIS UNHCR survey"], "organization": ["UNHCR", "Deloitte"]}, "relations": [{"has_organization": {"head": "SEIS UNHCR survey", "tail": "UNHCR"}}, {"used_by": {"head": "SEIS UNHCR survey", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1301, "dataset_name": "SEIS UNHCR survey"}}
+{"input": "The Economic Competitiveness Assessment Report 2022, published by the Global Trade Institute, provides an in-depth analysis of trade dynamics across various regions. The report, which covers data from 2018 to 2022, has been instrumental for policymakers and researchers, particularly those at the International Economic Council, who have cited the report in their ongoing studies. Additionally, the Asia-Pacific Trade Survey (APTS) has been utilized extensively by local governments to inform their trade strategies. Conducted by the Asian Development Bank, this survey collects data every two years, with the latest edition released in 2021 and covering multiple countries within the region. Such datasets are crucial for understanding the evolving landscape of international trade and economic competitiveness.", "output": {"entities": {"named_data": ["Economic Competitiveness Assessment Report 2022", "Asia-Pacific Trade Survey"], "organization": ["Global Trade Institute", "International Economic Council", "Asian Development Bank"], "acronym": ["APTS"], "year": ["2022", "2018 to 2022", "2021"], "geography": ["Asia-Pacific"]}, "relations": [{"has_organization": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "Global Trade Institute"}}, {"used_by": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "International Economic Council"}}, {"has_timeframe": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "2018 to 2022"}}, {"has_organization": {"head": "Asia-Pacific Trade Survey", "tail": "Asian Development Bank"}}, {"used_by": {"head": "Asia-Pacific Trade Survey", "tail": "local governments"}}, {"has_acronym": {"head": "Asia-Pacific Trade Survey", "tail": "APTS"}}, {"has_timeframe": {"head": "Asia-Pacific Trade Survey", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "street vending (48 % of those with at least 1 IGA), food processing for sale, including baking, cooking, and drying (16 %), and home production of crops, livestock, and fish (11 %). It is important to note that the EPAG program was not targeted toward the most vulnerable segments of Liberian society, but rather toward young women with enough education to be able to benefit from a training program of this nature. Based on comparisons from the 2007 CWIQ (Core Welfare Indicators Questionnaire) survey, the average EPAG participant is more educated, more literate, more likely to be engaged in an income-generating activity, and owns more assets than the average Liberian women of the same age group: mobile phone ownership was high (63 %), as was the proportion reporting that they had some money of their own (79 %). Even compared to other similar residents of Monrovia, the EPAG participants are better educated and have higher income. A strong sense of female empowerment at baseline emerges from the sections of the survey instrument having to do with self-confidence and agency.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire"], "organization": ["EPAG"]}, "relations": [{"used_by": {"head": "Core Welfare Indicators Questionnaire", "tail": "EPAG"}}]}, "_meta": {"entry_id": 206, "dataset_name": "Core Welfare Indicators Questionnaire"}}
+{"input": "The Gender Equality and Empowerment Survey, developed by the United Nations Entity for Gender Equality and the Empowerment of Women (UN Women), provides crucial insights into the economic participation of women across various sectors. This survey is pivotal for organizations aiming to assess and improve the status of women in the workforce. As such, the report is often referenced by local NGOs and research institutions seeking data to support their initiatives in promoting gender equality.", "output": {"entities": {"named_data": ["Gender Equality and Empowerment Survey"], "organization": ["United Nations Entity for Gender Equality and the Empowerment of Women", "UN Women", "local NGOs", "research institutions"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Gender Equality and Empowerment Survey", "tail": "United Nations Entity for Gender Equality and the Empowerment of Women"}}, {"used_by": {"head": "Gender Equality and Empowerment Survey", "tail": "local NGOs"}}, {"used_by": {"head": "Gender Equality and Empowerment Survey", "tail": "research institutions"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "gender equality and women's economic empowerment"}}
+{"input": "The Renewable Energy Access Survey (REAS) conducted in 2022 provides a comprehensive overview of energy availability across various rural communities in Southeast Asia. Published by the Asian Development Bank (ADB), this dataset has been instrumental for numerous organizations, including the International Renewable Energy Agency (IRENA), which utilized the findings to promote energy initiatives in the region. The survey not only highlights gaps in energy access but also emphasizes the importance of renewable resources in achieving sustainable development goals. Moreover, the report from the Southeast Asia Energy Transition Database covering 2021-2023 underscores the pivotal role of clean energy investments, providing vital insights for policymakers and stakeholders in the energy sector.", "output": {"entities": {"named_data": ["Renewable Energy Access Survey", "Southeast Asia Energy Transition Database"], "organization": ["Asian Development Bank", "International Renewable Energy Agency"], "acronym": ["REAS"], "year": ["2022", "2021-2023"], "geography": ["Southeast Asia"]}, "relations": [{"has_organization": {"head": "Renewable Energy Access Survey", "tail": "Asian Development Bank"}}, {"used_by": {"head": "Renewable Energy Access Survey", "tail": "International Renewable Energy Agency"}}, {"has_acronym": {"head": "Renewable Energy Access Survey", "tail": "REAS"}}, {"has_timeframe": {"head": "Southeast Asia Energy Transition Database", "tail": "2021-2023"}}, {"has_geography": {"head": "Renewable Energy Access Survey", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "energy access and renewable transitions"}}
+{"input": "The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics. Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013. The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare. 1. DATA AND TECHNICAL ISSUES Data Sets Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty, 5 UNCHR, 2013pg 13 6 UNHCR (22 November 2013), UNHCR (15 September 2014) 7 UNHCR (22 March 2013), Erdogan (2014), pg 14 8 This technical issue will be discussed further later in the paper.", "output": {"entities": {"named_data": ["Household Income and Consumption Expenditure Survey"], "organization": ["Turkish Statistical Institute", "authors"]}, "relations": [{"has_organization": {"head": "Household Income and Consumption Expenditure Survey", "tail": "Turkish Statistical Institute"}}, {"used_by": {"head": "Household Income and Consumption Expenditure Survey", "tail": "authors"}}]}, "_meta": {"entry_id": 740, "dataset_name": "Household Income and Consumption Expenditure Survey"}}
+{"input": "The Environmental Sustainability Assessment Report 2022 provides crucial insights into the effects of climate change on natural resources across various regions. This report, published by the Global Institute for Environmental Research, emphasizes the importance of sustainable practices to mitigate these impacts.", "output": {"entities": {"named_data": ["Environmental Sustainability Assessment Report 2022"], "organization": ["Global Institute for Environmental Research"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Environmental Sustainability Assessment Report 2022", "tail": "Global Institute for Environmental Research"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
+{"input": "Recent analyses of the impact of violent conflicts on local economies have highlighted findings from the Community Resilience Survey in the Eastern Region. This dataset provides valuable insights into how communities adapt to and recover from violent incidents, focusing on various economic and social indicators. By examining the experiences of residents, researchers are better able to understand the long-term effects of instability in the area.", "output": {"entities": {"named_data": ["Community Resilience Survey"], "organization": [], "acronym": [], "year": [], "geography": ["Eastern Region"]}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "conflict, fragility, and violence"}}
+{"input": "**The World Bank**\nSouth Sudan Health Sector Transformation Project (HSTP) (P181385)\n\n|Frequency|Annually|\n|---|---|\n|Data source|Survey|\n|Methodology for Data<br>Collection|Survey|\n|Responsibility for Data<br>Collection|Third Party Monitor / PMU|\n|**Contraceptive prevalence rate (any method)**|**Contraceptive prevalence rate (any method)**|\n|Description|Percentage of women aged 15− 9 years, married or in union, who are currently using, or whose sexual partner is<br>using, at least one method of contraception, regardless of the method used.|\n|Frequency|Annually|\n|Data source|Survey|\n|Methodology for Data<br>Collection|Survey|\n|Responsibility for Data<br>Collection|Third Party Monitor / PMU|\n|**The proportion of patients with suspected malaria who received a parasitologic test (RDT/Microscopy)**|**The proportion of patients with suspected malaria who received a parasitologic test (RDT/Microscopy)**|\n|Description|Percentage of suspected malaria cases that received parasitological diagnosis either by microscopy or RDT|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH / UNICEF; Measures subcomponent 1.1 Under UNICEF|\n|**Proportion of health facilities that have a core set of relevant basic medicines and commodities available and affordable**|**Proportion of health facilities that have a core set of relevant basic medicines and commodities available and affordable**|\n|Description|Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a<br>sustainable basis. Availability: will be calculated based on currently existing data on average proportion of<br>medicines available in health facilities per country.|\n|Frequency|Quarterly|\n|Data source|Quarterly Health Facility Assessment|\n|Methodology for Data<br>Collection|TPM report|\n|Responsibility for Data<br>Collection|TPM / PMU|\n|**Component 2: Health Systems Strengthening**|**Component 2: Health Systems Strengthening**|\n|**Percentage of disease outbreaks in refugee areas that are adequately addressed as per WHO guidelines (Percentage)**|**Percentage of disease outbreaks in refugee areas that are adequately addressed as per WHO guidelines (Percentage)**|\n|Description|Disease outbreaks in refugee areas that have been adequately addressed as per WHO guidelines.|\n|Frequency|Quarterly|\n|Data source|WHO/MoH report|\n|Methodology for Data<br>Collection|WHO to provide data|\n|Responsibility for Data<br>Collection|UNICEF/WHO/ PMU- Measures subcomponent 2.1 under WHO|\n|**Percentage of SMoH/CHDs with work plans aligned to the HSSP**|**Percentage of SMoH/CHDs with work plans aligned to the HSSP**|\n|Description|Percentage of SMoH and CHDs that develop annual operational work plans aligned to HSSP|\n|Frequency|Quarterly|\n|Data source|WHO report|\n|Methodology for Data<br>Collection|WHO to provide data / TPM to verify|\n|Responsibility for Data<br>Collection|PMU / TPM; Measures subcomponent 2.1 under WHO|\n|**Proportion of health alerts investigated in 48 hrs**|**Proportion of health alerts investigated in 48 hrs**|", "output": {"entities": {"named_data": ["WHO report"], "organization": ["WHO", "World Bank"]}, "relations": [{"has_organization": {"head": "WHO report", "tail": "WHO"}}, {"used_by": {"head": "WHO report", "tail": "World Bank"}}]}, "_meta": {"entry_id": 824, "dataset_name": "WHO report"}}
+{"input": "challenge in some cases, notably among South Sudanese, refugees generally believe they are culturally similar to their hosts. This rate averages 78 percent for all refugees. It is lowest in South Sudanese camps at 68 percent and highest in Somali camps at 87 percent. Many of these refugees who say they are culturally similar to their hosts respond that they have no Ethiopian friends and that social interactions with Ethiopians are complex. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.16: Share of refugees who agree they are “culturally similar to hosts” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Age Under 30 Age 30-44 Age 45-64 Age Over 64 Female Male Figure 7.18: Share or refugees engaged in a community representative body by demographic group Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.17: Share or refugees involved in a community representative body Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 71 Low refugee social integration is not", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank Staff"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 110, "dataset_name": "SESRE 2023"}}
+{"input": "The recent analysis of public health indicators highlights critical areas for improvement in water, sanitation, and hygiene practices. Data from the National Water and Sanitation Survey provides insights into the accessibility of clean water sources across various communities. The findings underscore the importance of investing in infrastructure to enhance public health outcomes, particularly in rural regions that are disproportionately affected by poor sanitation facilities.", "output": {"entities": {"named_data": ["National Water and Sanitation Survey"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "water, sanitation, and hygiene"}}
+{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer.", "output": {"entities": {"named_data": ["Afrobarometer"], "organization": ["M ¨ uller-Crepon et al. (2020)"]}, "relations": [{"used_by": {"head": "Afrobarometer", "tail": "M ¨ uller-Crepon et al. (2020)"}}]}, "_meta": {"entry_id": 996, "dataset_name": "Afrobarometer"}}
+{"input": "For a detailed definition see section 4 Household size Number of people included in the case records of each PA in Individual ProGress dataset Wage Income 1 if the household receives income from employment and / or daily or irregular work Income from remittances 1 if the household receives income from remittances Income per capita Raw sum of household income from all sources; work, pension, assets in Syria transfers, donations, other organizations'humanitarian aid, and other divided by household size Male Adults Number of males above 18 (inclusive) in the household Marital Status Categorical variable. The classification includes married PAs with spouse in the household, married PAs without spouse in the household, widowed, single or engaged, and divorced or separated. Proportion of female Number of female divided by the household size Location Categorical variable for 11 Governorates / cities. Ajloun City, Aqaba, Balqa, Irbid Jerash, Karak, Maan, Madaba, Mafraq, Tafilah, Zarqa. In Camp 1 if the household is located in a refugee camp Poverty before UNHCR and WFP assistance 1 if household expenditure before UNHCR plus WFP assistance is below the poverty line (JD50) Poverty before UNHCR assistance 1 if household expenditure after WFP assistance but before UNHCR assistanc is below the poverty line (JD50) Source: Authors ’ elaboration.", "output": {"entities": {"named_data": ["Individual ProGress dataset"], "organization": ["UNHCR", "Authors"]}, "relations": [{"has_organization": {"head": "Individual ProGress dataset", "tail": "UNHCR"}}, {"used_by": {"head": "Individual ProGress dataset", "tail": "Authors"}}]}, "_meta": {"entry_id": 644, "dataset_name": "Individual ProGress dataset"}}
+{"input": "The Inclusive Poverty Measurement Survey (IPMS) conducted by the National Statistical Office provides crucial insights into poverty levels across the regions of Madagascar for the year 2021. This dataset not only highlights the stark inequalities present in urban versus rural demographics but also serves as a pivotal reference for various NGOs addressing poverty alleviation. Additionally, the World Bank's Global Inequality Database (GID) offers a comprehensive overview of income disparities globally, with data spanning from 2015 to 2020, assisting policymakers in shaping effective interventions. Lastly, the 2022 Poverty and Inequality Assessment Report (PIAR) focuses on sub-Saharan Africa, elaborating on the ongoing challenges faced by low-income households in the region. These datasets collectively underscore the importance of targeted research in understanding and addressing poverty dynamics.", "output": {"entities": {"named_data": ["Inclusive Poverty Measurement Survey", "Global Inequality Database", "Poverty and Inequality Assessment Report"], "organization": ["National Statistical Office", "World Bank"], "acronym": ["IPMS", "GID", "PIAR"], "year": ["2021", "2015 to 2020", "2022"], "geography": ["Madagascar", "sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Inclusive Poverty Measurement Survey", "tail": "IPMS"}}, {"has_timeframe": {"head": "Inclusive Poverty Measurement Survey", "tail": "2021"}}, {"has_geography": {"head": "Inclusive Poverty Measurement Survey", "tail": "Madagascar"}}, {"has_acronym": {"head": "Global Inequality Database", "tail": "GID"}}, {"has_timeframe": {"head": "Global Inequality Database", "tail": "2015 to 2020"}}, {"has_acronym": {"head": "Poverty and Inequality Assessment Report", "tail": "PIAR"}}, {"has_timeframe": {"head": "Poverty and Inequality Assessment Report", "tail": "2022"}}, {"has_geography": {"head": "Poverty and Inequality Assessment Report", "tail": "sub-Saharan Africa"}}, {"has_organization": {"head": "Inclusive Poverty Measurement Survey", "tail": "National Statistical Office"}}, {"has_organization": {"head": "Global Inequality Database", "tail": "World Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "poverty headcount and inequality measurement"}}
+{"input": "The annual report from the Refugee Study Center highlights critical data on forced displacement trends worldwide. This analysis is based on the Refugee Data Analysis (RDA) produced by the United Nations High Commissioner for Refugees (UNHCR). The insights drawn from the RDA are essential for policymakers and humanitarian organizations, with various agencies citing this comprehensive dataset to inform their strategies.", "output": {"entities": {"named_data": ["Refugee Data Analysis (RDA)"], "organization": ["United Nations High Commissioner for Refugees (UNHCR)", "Refugee Study Center"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Refugee Data Analysis (RDA)", "tail": "United Nations High Commissioner for Refugees (UNHCR)"}}, {"has_organization": {"head": "Refugee Data Analysis (RDA)", "tail": "Refugee Study Center"}}, {"used_by": {"head": "Refugee Data Analysis (RDA)", "tail": "various agencies"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "forced displacement, refugees, and migration"}}
+{"input": "An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer. { Table 3 here} Calculating Missing Gender Splits Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5. Combining Migrant Definitions Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see http: / / unstats. un. org / unsd / methods / m49 / m49regin. htm, with the countries of Oceania aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV. 18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for.", "output": {"entities": {"named_data": ["Global Migration Database"], "organization": ["United Nations", "the authors"]}, "relations": [{"has_organization": {"head": "Global Migration Database", "tail": "United Nations"}}, {"used_by": {"head": "Global Migration Database", "tail": "the authors"}}]}, "_meta": {"entry_id": 191, "dataset_name": "Global Migration Database"}}
+{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer.", "output": {"entities": {"named_data": ["Linking Ethnic Data from Africa"], "organization": ["Afrobarometer", "M ¨ uller-Crepon et al. (2020)"]}, "relations": [{"has_organization": {"head": "Linking Ethnic Data from Africa", "tail": "Afrobarometer"}}, {"used_by": {"head": "Linking Ethnic Data from Africa", "tail": "M ¨ uller-Crepon et al. (2020)"}}]}, "_meta": {"entry_id": 509, "dataset_name": "Linking Ethnic Data from Africa"}}
+{"input": "The recent findings from the Global Gender Empowerment Survey (GGES) emphasize the importance of women's participation in labor markets across various regions. Conducted in 2022, this dataset covers multiple countries, including Bangladesh and Nigeria, providing invaluable insights into gender disparities in employment. The World Bank utilized this survey to inform its policy recommendations aimed at enhancing economic opportunities for women. Additionally, the Women’s Economic Participation Assessment 2020/21 presents a comprehensive analysis of female workforce engagement specifically in Sub-Saharan Africa. This report, published by the International Labour Organization, has become a pivotal resource for policymakers seeking to address gender inequalities in this region.", "output": {"entities": {"named_data": ["Global Gender Empowerment Survey", "Women’s Economic Participation Assessment 2020/21"], "organization": ["World Bank", "International Labour Organization"], "acronym": ["GGES"], "year": ["2022", "2020/21"], "geography": ["Bangladesh", "Nigeria", "Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Global Gender Empowerment Survey", "tail": "GGES"}}, {"has_timeframe": {"head": "Global Gender Empowerment Survey", "tail": "2022"}}, {"has_geography": {"head": "Global Gender Empowerment Survey", "tail": "Bangladesh"}}, {"has_geography": {"head": "Global Gender Empowerment Survey", "tail": "Nigeria"}}, {"has_timeframe": {"head": "Women’s Economic Participation Assessment 2020/21", "tail": "2020/21"}}, {"has_geography": {"head": "Women’s Economic Participation Assessment 2020/21", "tail": "Sub-Saharan Africa"}}, {"used_by": {"head": "Global Gender Empowerment Survey", "tail": "World Bank"}}, {"has_organization": {"head": "Women’s Economic Participation Assessment 2020/21", "tail": "International Labour Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
+{"input": "11 There may be grounds for skepticism about these estimates for madrassa enrollment. Since the data were collected prior to 2001, geopolitical changes after September 11 could have led to greater madrassa enrollment. In addition, the household-based survey faces the usual problems of accurately estimating a low-probability event — although enrollment is less than 1 percent in these surveys, the sampling error is large (see Bauman, 2001, for a description of similar problems in estimating home-schooling in the United States). Finally, while the census of populations does not face the problem of small samples, it is not that recent (1998) and some may have reservations regarding the quality of government data. 10 The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers (see appendix for details). This census was conducted in three districts of Punjab and villages were chosen randomly based on the criterion that each village must have at least one private school. Typically, this means that the villages lies somewhere between fully urban and fully rural populations and are not representative of the districts that they are in. Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census.", "output": {"entities": {"named_data": ["LEAPS census"], "organization": ["LEAPS"]}, "relations": [{"has_organization": {"head": "LEAPS census", "tail": "LEAPS"}}]}, "_meta": {"entry_id": 663, "dataset_name": "LEAPS census"}}
+{"input": "The Social Protection Assessment Report 2022 conducted by the National Institute of Statistics (NIS) provides critical insights into the effectiveness of various safety net programs in Brazil. This dataset has been extensively used by the International Labour Organization (ILO) to analyze trends in social security coverage. Additionally, the 2021 Brazil Safety Net Survey (BSNS) published by NIS has also been instrumental for the United Nations Development Programme (UNDP) in assessing the impact of cash transfer initiatives on poverty alleviation in rural areas. Both datasets highlight the evolving landscape of social protection mechanisms implemented across different regions of Brazil.", "output": {"entities": {"named_data": ["Social Protection Assessment Report 2022", "Brazil Safety Net Survey"], "organization": ["National Institute of Statistics", "International Labour Organization", "United Nations Development Programme"], "acronym": ["BSNS"], "year": ["2022", "2021"], "geography": ["Brazil"]}, "relations": [{"has_organization": {"head": "Social Protection Assessment Report 2022", "tail": "National Institute of Statistics"}}, {"used_by": {"head": "Social Protection Assessment Report 2022", "tail": "International Labour Organization"}}, {"has_organization": {"head": "Brazil Safety Net Survey", "tail": "National Institute of Statistics"}}, {"used_by": {"head": "Brazil Safety Net Survey", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Brazil Safety Net Survey", "tail": "BSNS"}}, {"has_timeframe": {"head": "Social Protection Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Brazil Safety Net Survey", "tail": "2021"}}, {"has_geography": {"head": "Social Protection Assessment Report 2022", "tail": "Brazil"}}, {"has_geography": {"head": "Brazil Safety Net Survey", "tail": "Brazil"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "social protection and safety nets"}}
+{"input": "second major difference is the nesting structure used for modeling production decisions. For each activity the structure is calibrated on the household survey data [1] . As a result we have household specific production functions, capturing differences in household access to inputs [2] .\n\nBy placing households at the center of the model, the village model used in this study is able to capture differences in production decisions reflecting differences in access to the inputs, interactions between household production and consumption decisions, and interactions among different households within a village economy.\n\n2 Models and data 2.1 Macro level analysis The macro economic analyses are performed using the GTAP model, with some important modifications to the version 5 database. Van Tongeren and Huang (2004) describes the changes made and the baseline simulation which is the reference for the scenarios used in the present study.\nSpecifically, we have updated policy information on China from the original version 5 GTAP database to include recent changes in domestic policies and changes in border policies. In addition we have adjusted the Chinese input-output table to incorporate microdata information on primary factor shares. We also made adjustments to the data on livestock production and – consumption. Using macro-economic information, and incorporating known imminent policy changes, we project the database forward in time to the year 2010 and beyond. The policy changes along the baseline include the full implementation of China’s WTO accession and the phasing out of export quota under the Agreement on Textiles and Clothing. This baseline is the reference for simulations to assess the effects of further trade liberalization under the WTO Doha round.\n\nOur experiment reduces multilaterally the support to agriculture grouped under the three classical pillars, market access, export competition and domestic support, and it reduces border distortions in non-agricultural sectors. The simulation for agriculture follows closely the original modalities proposal tabled in 2002 by the European Commission (European Commission, 2002). This proposal contained a certain asymmetry between the treatment of OECD countries and developing countries.\nSpecifically, we model a reduction of import tariffs by –36% for trade amongst OECD countries and total elimination of import barriers against LDCs, while LDCs themselves are not reducing their import barriers. In the area of export competition, we reduce the value of subsidies by -45% in OECD countries only, and eliminate fully the export subsidies on wheat and oilseeds. In the area of domestic support, the AMS ceiling is lowered by -55%, but the blue box payments are maintained. All reductions are made from the final Uruguay Round commitments. For Non-agricultural Market access, we apply a straight –50% reduction of applied rates. It should be noted that this simulation is illustrative for the possible effects of a successful round. Within the spectrum of proposals offered prior to the ministerial meeting in Cancun, September 2003, the EC proposal is rather conservative.\nAn assessment of the more far-reaching proposals tabled by the CAIRNS group and the US are discussed in Van Tongeren and van Meijl (2004).\n\n1 This calibration also rejected the commonly assumed separability of factors and intermediate inputs, and this assumption was therefore dropped in the village equilibrium model.\n\n8", "output": {"entities": {"named_data": ["GTAP database"], "organization": ["Van Tongeren and Huang"]}, "relations": [{"used_by": {"head": "GTAP database", "tail": "Van Tongeren and Huang"}}]}, "_meta": {"entry_id": 1412, "dataset_name": "GTAP database"}}
+{"input": "The Global Trade Assessment Report (GTAR) provides a comprehensive overview of trade flows and economic competitiveness across various sectors for the year 2022. This dataset, produced by the International Trade Organization, highlights key trends and data crucial for policymakers and businesses alike.", "output": {"entities": {"named_data": ["Global Trade Assessment Report"], "organization": ["International Trade Organization"], "acronym": ["GTAR"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Global Trade Assessment Report", "tail": "International Trade Organization"}}, {"has_acronym": {"head": "Global Trade Assessment Report", "tail": "GTAR"}}, {"has_timeframe": {"head": "Global Trade Assessment Report", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The second source of data we draw on is a short survey on COVID-19 vaccination collected inperson as part of the Ethiopia Socioeconomic Survey (ESS 5), a nationally representative household survey that was implemented between April and June 2022 by the Ethiopia Statistical Service with support from the World Bank's LSMS program. This survey contained a similar module as the phone surveys and collected information on the vaccination status of all household members.\n\nThe source of administrative data for our study is the Our World in Data (OWID) COVID-19 vaccination dataset (Mathieu et al. 2021) that compiles administrative data on COVID-19 vaccine coverage. Amongst others, the dataset contains information on the number of total doses administered, the share of the country population that has received at least one dose, and the share of the population that is fully vaccinated. [3] It covers the period from December 2020 when the first COVID-19 vaccines achieved approval and is regularly updated as new data becomes available on a per-country basis. The data is compiled from country reports (such as government websites, dashboards, or the social media accounts of national authorities) and in some cases third-party aggregators (where national authorities do not publish data in a machine-readable format) and is regularly audited for inconsistencies and technical errors.\n\nWe additionally access a second source of administrative data stemming from the WHO's COVID19 vaccination dashboard (WHO 2020b). The dashboard does not provide longitudinal information for public access but reports the latest available COVID-19 vaccine coverage figures at the time of data access (April 2, 2023, in our case).\n\nLastly, we use data from the World Bank's Statistical Performance Indicators (SPI) available through the World Bank's Open Data library (World Bank n.d.). The SPI is a composite index between 0 - 100 scoring countries' statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative data systems in particular, we also use the SPI's indicator of administrative data capacity (Dimension 4.2) that records the availability of Civil Registration and Vital Statistics (CRVS).", "output": {"entities": {"named_data": ["Our World in Data (OWID) COVID-19 vaccination dataset"], "organization": ["Our World in Data (OWID)", "World Bank"]}, "relations": [{"has_organization": {"head": "Our World in Data (OWID) COVID-19 vaccination dataset", "tail": "Our World in Data (OWID)"}}, {"used_by": {"head": "Our World in Data (OWID) COVID-19 vaccination dataset", "tail": "World Bank"}}]}, "_meta": {"entry_id": 51, "dataset_name": "Our World in Data (OWID) COVID-19 vaccination dataset"}}
+{"input": "13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset.", "output": {"entities": {"named_data": ["Uppsala / PRIO dataset"], "organization": ["Buhaug & Rød"]}, "relations": [{"used_by": {"head": "Uppsala / PRIO dataset", "tail": "Buhaug & Rød"}}]}, "_meta": {"entry_id": 741, "dataset_name": "Uppsala / PRIO dataset"}}
+{"input": "#### **HUMANITARIAN AND STABLIZATION TRENDS**\n\nAt the onset of the Syrian emergency, refugee started fleeing neighboring Syria through Lebanon’s northern borders, settling first along the border in Wadi Khaled, where relief efforts were initially focused. However, many continued further south to arrive in Tripoli from where they often dispersed to its five adjacent districts, with a total Lebanese population of over 550,000 persons.\n\nAs of late May, Syrian refugees were spread in over 289 villages and lived mainly in very challenging urban and semi-urban contexts.\nApproximately 81 per cent of them are living in rented accommodation; however, refugees who are unable to cover the high rents resort to living in substandard accommodation including non-residential dwellings such as garages and unfinished buildings.\nPartners are working to rehabilitate these to improve the standard of living. There are 16 fewer informal settlements with 4,339 less people living in them compared to December 2014.\n\nAn outreach system through Community Development Centers (CDCs), Social Development Centers (SDCs) and Refugee Outreach Volunteers (ROVs) is in place to provide support and identify vulnerable refugees with specific needs and refer them to existing services. Access to healthcare facilities remains a challenge with only 8 public hospitals which are supported – with limited bed capacity- and 5 primary health care centers in the governorate, in addition to the high costs of secondary health care.\n\nThe MRR (Map of Risks and Resources) is piloted in Minieh and Dedde municipalities as a coordination tool among all actors to link the risks and needs of the community with the actual and planned interventions. The outcome of the exercise was presented to mayor of Minieh and a dedicated coordination body was set-up to strengthen the coordination among partners and with the municipality. This exercise is still on-going.\n\nA study conducted with northern citizens, including a majority of Lebanese, reported a continuous increase of positive relations between Lebanese and Syrians by 23 per cent and a ~~decrease in tensi~~ ons between December 2014 and March 2015, while people reporting that tensions remained t ~~he same are still the majority, with 66 per cent. This m~~ ay be due to the expanded presence of the Lebanese Army in the North follo ~~wing the late October clashes with militants in Tripo~~ li and the inclusion of vulnerable affected Lebanese and host community in the service delivery by the agencies.\n\nDespite all the efforts put in the area ~~, overall needs remain very high especially in the mos~~ t vulnerable localities indicated in the below map, while funds are decreasing. M ~~ost public institutions are considered very weak in No~~ rth Governorate.\n\n#### **MOST VULNERABLE LOCALITIES**\n\nThere are 31 vulnerable localities in the North, nine of which are classified as most vulnerable. Seventeen of the 31 localities are also considered as sub ~~stantial and high pressure where the ratio of refugees to deprived Lebanese is at least three to one or mor~~ e ~~Minie~~ ~~#~~ Zouq Bhannine ~~El Minié~~\n\n**Informal Tented**\n**Settlements**\n\n~~!!~~ ~~**!!!!**~~ ! ~~**!**~~ ~~!~~ ! ~~!~~ ! ~~!~~ ~~**!!**~~ ! ~~[!]~~ ~~!!~~ ! ~~**[!]**~~ ~~!~~ ~~**!**~~ ~~!!~~ ~~[!]~~ ~~!~~ ~~**!**~~ ~~!!!~~ ~~**!**~~ ~~!~~ ! ~~!~~ ~~**!**~~ **!** ~~!~~ ~~**!**~~ ~~!~~ ~~**!**~~ **!** ! ~~**!**~~ ~~!~~ ~~**!**~~ ! ~~!!!~~ ~~**!**~~ ~~!~~ ~~**!**~~ **!** ~~!~~ ! ~~!!!~~ **!** ~~!~~ ~~**!**~~ ! ~~!~~ ~~**!**~~ !! ~~!~~ **!** ~~**!**~~ ~~!!~~ ~~**!!!!**~~ ! ~~**!**~~ ~~!~~ ! ~~!~~ ! ~~!~~ ~~**!!**~~ ! ~~[!]~~ ~~!!~~ ! ~~**[!]**~~ ~~!~~ ~~!!~~ ~~Tripoli et Tabbaneh~~ ~~Tripoli El Qobbe~~ ~~!(~~ ~~Beddaoui~~ ~~!(~~ ~~!!~~ ~~!!! !!!!~~ ~~**!**~~ ~~!!!~~ ! ~~**!**~~ ~~!!!~~ ~~**!!**~~ ~~!!~~ **!** ~~**!**~~ ~~[!]~~ ~~**!!**~~ ~~!~~ ~~**!**~~ ~~[!]~~ ~~**!!**~~ ~~!!!!!!~~ ~~**!!!**~~ ~~!~~ ~~[!]~~ ~~!~~ ~~**!**~~ ~~!!!~~ ~~**!**~~ ! ~~!~~ ~~**!**~~ **!** ~~!~~ ~~**!**~~ ~~!~~ ~~**!**~~ **!** ! ~~**!**~~ ~~!~~ ~~**!**~~ ! ~~!!!~~ ~~**!**~~ ~~!~~ ~~**!**~~ ~~!!~~ **!** ~~!~~ !!! ~~!!!~~ **!** ~~!~~ ~~**!!!**~~ ! ~~!~~ ~~**!**~~ !! ~~!~~ **!** ~~**!**~~ ~~[!]~~ ~~**!**~~ ~~!~~ ~~**!**~~ **!** ~~**!**~~ ~~!~~ ~~[!]~~ ! ~~!~~ ~~[!]~~ ~~!~~ ~~**!**~~ ! ~~!!!!~~ ~~**!**~~ ~~Mejdlaiya Zgharta~~ P !\n\n~~Tripoli et Tell~~ ~~Tripoli~~ ~~!(~~ ~~#~~ ~~!!!~~ ~~**!**~~ **!** ~~!~~ ~~**!!**~~ ~~!~~ ~~**!**~~ **!** ! ~~**!**~~ ~~!!~~ ~~**!**~~ ~~!~~ ! ~~!!!~~ ~~**!**~~ ~~!~~ ! ~~**!!**~~ ~~!!~~ **!** ~~!~~ !!! ~~!!!~~ **!** ~~!!!!~~ ~~**!!!!!**~~ ! ~~!~~ ~~**!**~~ !! ~~!!~~ **!** ~~**!**~~ ~~[!]~~ ~~**!**~~ ~~!~~ ~~**!**~~ ~~!!~~ **!** ~~!~~ ~~**!**~~ **!** ~~**!**~~ ~~[!]~~ ~~**!**~~ ~~!!~~ ~~[!]~~ ! ~~!!~~ ~~**!**~~ ~~!~~ ~~**!**~~ ~~[!]~~ ~~!~~ ~~**!**~~ ! ~~!~~ ~~[!]~~ ~~**!!**~~ ~~!!!!!!!~~ ~~**!!**~~ ~~!~~ ~~**!**~~ ~~#~~ Informal Settlements ~~!!~~ ~~**!**~~ ~~!~~ ~~!!!!~~ ~~!~~ ~~!!~~ ~~**[!]**~~ !\n\n!\n\n~~Tripoli~~ ~~!~~ !\n\n#!!!!! ~~[!]~~ ~~!!~~\n\n!\n\n~~!~~ !! ~~!!~~ ~~**!**~~ ~~!!~~ ~~!!!~~ ~~!(~~ ~~Zgharta~~\n\n**!**\n\n~~!~~ ~~!~~ ! ~~[!]~~ ~~!~~ !! ! **!** !!!!!!!!!!!! **!** !! **!** !!! **!** !\n\n!\n\n!!! !!\n\n!!\n\n! ! ! **!** ! **!** ! **!** ! !!!!\n\n~~Miniyeh-D~~ anniyeh !!!! **!** !! **!** ! **!** !\n\n!\n\n!!\n\n! **!** ! !!!!!\n\n!!! **!!** !! **!!** !!!!! **!** ! **!** !\n\n! [!] !\n\n!!!!\n\n!\n\n!\n\n!\n\n~~High P~~ ressure ~~(5 Cadastres)~~ ~~Substantial Pressure~~ ~~(9 Cadastres)~~ ~~Most Vulnerable~~ 2nd Most Vulnerable ~~3rd Most Vulnerable~~ ~~4th Most Vulnerable~~ 5th Most Vulnerable ~~(~~ ~~Major cities~~ ~~Mina N~~ Tripoli el Haddadine ~~Tripoli ez Zeitoun~~ Enfé ~~!(~~ ~~!(~~ ~~Chekka~~ ~~Zgharta~~ ~~Koura~~ !\n\n!!!\n\n!!!!!!!\n\n! [!] !\n\n**!**\n\n~~Amioun~~ !( Batroun\n\n#\n\nPalestinian Camps !( Bcharreh Batroun Bcharré !(\n\n#### **HUMANITARIAN ACTORS**\n\n86 2 Tripoli El Minieh Dennie El Koura El Batroun Zagharta Bcharre Key contacts 9 6 12 2 7 3 3 2 7 86 6 12 2\n\n**UNHCR** Monica Noro, noro@unhcr.org **UNDP** Alain Chatry, alain.chatry@undp.org\n**Ministery of Social Affairs (MoSA)** Ziad Nadri, ziadnadri@gmail.com\n\n**North Governor** Mr Ramzi Nohra\n\n3 8 4 11 1 5 5 3 6 3 6 3 6 2 5 1 3 4 3 8 4 1 3 3 3 1 1 2 1 3 80 3 5 3 3 1 6 1 2 1 3 80 4 4 5 1 5 2 2 1 3 2 2 1 0 2 0 0 0 2 4 4 0 1\n\n**54 UN Agencies and NGOs operating in North**\n\nABAAD, AJEM Lebanon, Al Fayha'e, ANERA, ARCPA, Beddawi Pop Com, Beddawi Popular Committee, Beyond, CARE, CCP JAPAN, CISP, CLMC Lebanon, COOPI, Danish Red Cross, DRC, FAO, HDC, Heartland, HI, Himaya, HOOPS, HWA, IA, IMC, IOCC Lebanon, IOM, IQRAA, IR Lebanon, IRC, IRD, Lebanese Red Cross, MAP-UK, Mercy Corps, MoSA, MS Lebanon, MSL Lebanon, NRC, OXFAM, Pal_Scouts, Palestinian Scouts & Guides Association, PU-AMI, RESTART Lebanon, RET, RI, SCI, SFCG, Solidarités, UNDP, UNFPA, UNHCR, UNRWA, URDA, WCH, WHO\n\n**Disclaimer:** The boundaries and names shown on this map do not imply official endorsement or acceptance by the United Nations.\n**Data Source:** Lebanese Population - Central Administration of Statistics (CAS) year 2002 dataset,   Poverty data: CAS, UNDP and MoSA Living Conditions and Household Budget Survey 2004-5,\nSyrian Refugee Population - UNHCR as of 30/05/2015, Humanitarian Intervention Data - Activity Info, Palestinian Refugee Population- UNRWA", "output": {"entities": {"named_data": ["MRR (Map of Risks and Resources)"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "MRR (Map of Risks and Resources)", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 1370, "dataset_name": "MRR (Map of Risks and Resources)"}}
+{"input": "The Economic Competitiveness Index (ECI) provides a comprehensive analysis of various sectors' performance, benchmarking indicators across multiple countries. Published by the Global Trade Institute, this dataset serves as a crucial resource for policymakers and analysts alike to assess competitiveness and make informed decisions. Its findings, relevant for the year 2022, highlight significant trends in trade dynamics and industry growth.", "output": {"entities": {"named_data": ["Economic Competitiveness Index"], "organization": ["Global Trade Institute"], "acronym": ["ECI"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Economic Competitiveness Index", "tail": "Global Trade Institute"}}, {"has_acronym": {"head": "Economic Competitiveness Index", "tail": "ECI"}}, {"has_timeframe": {"head": "Economic Competitiveness Index", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The 2020 Gender Equality Assessment Report, published by the Global Institute for Women’s Empowerment, provides critical insights into women's economic participation across various regions. This report, used by the United Nations Development Programme (UNDP), highlights disparities and advances in economic opportunities for women in countries such as India and Nigeria. Additionally, the report utilized data from the Women’s Economic Empowerment Survey (WEES), which focuses on trends from 2018 to 2020, illustrating the shifting landscape of female participation in the labor market. The WEES has proven invaluable for policymakers aiming to craft targeted interventions.", "output": {"entities": {"named_data": ["Gender Equality Assessment Report", "Women’s Economic Empowerment Survey"], "organization": ["Global Institute for Women’s Empowerment", "United Nations Development Programme"], "acronym": ["WEES"], "year": ["2020", "2018 to 2020"], "geography": ["India", "Nigeria"]}, "relations": [{"has_organization": {"head": "Gender Equality Assessment Report", "tail": "Global Institute for Women’s Empowerment"}}, {"used_by": {"head": "Gender Equality Assessment Report", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Women’s Economic Empowerment Survey", "tail": "WEES"}}, {"has_timeframe": {"head": "Women’s Economic Empowerment Survey", "tail": "2018 to 2020"}}, {"has_geography": {"head": "Gender Equality Assessment Report", "tail": "India"}}, {"has_geography": {"head": "Gender Equality Assessment Report", "tail": "Nigeria"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
+{"input": "**Total Poverty** **Food Poverty** **Lower Total**\n**Estimation** **Poverty**\n\nParametric R$287 R$507\nNonparametric R$287 R$503\n_Source:_ Own calculations using POF 2017/18.\n\n**Total Poverty** **Food** **Lower Total**\n**Estimation** **Poverty** **Poverty**\n\nParametric R$251 R$443\nNon-parametric R$251 R$441\n_Source:_ Own calculations using POF 2017/18.\n\nDeaton and Zaidi (2002) advert to the fact that implicit rent, as elicited in POF and used here, is a hypothetical concept that could lead to estimations that are not usable.\n\nIn this paper we have presented our estimate of a poverty line for Brazil, using the CBN approach and based on the most recent data (POF 2017/18). Our preferred specification results in a food poverty line, accounting only for nutritional requirements, of R$258 (in 2018 Southeast urban prices) per person per month.\n\nIn comparison with earlier work, mainly based on POF 2003, our poverty lines are generally similar in real\nvalues, although methodologies differ, and consumption patterns have likely changed over time.\nConverted to January 2018 prices and considering São Paulo (mostly metropolitan) lines in the case of\nregional lines, previous estimates range from R$485 to R$532 (Rocha, 2007; Silveira et al., 2007). Ferreira\net al. (2003) used POF 1996 and estimated a lower poverty line of R$477 in January 2018 metropolitan\nSão Paulo prices. Only World Bank (2007) estimated a considerably lower poverty line of R$272 in January\n2018 metropolitan São Paulo prices.", "output": {"entities": {"named_data": ["POF"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "POF", "tail": "World Bank"}}]}, "_meta": {"entry_id": 229, "dataset_name": "POF"}}
+{"input": "Macro International, Demographic and Health Surveys, Beltsville, MD.\n\n<!-- PAGE 33 -->\n8. **Comparison with Monitoring Results for India**\n\n\nA recent monitoring study for Indian households (World Bank, 2002; Balakrishnan,\n\n\net al., 2002; Parikh, et al., 2001) has provided useful comparative information about\n\nsurvey suggests that progress has been quite limited. Of 686 biofuel-using households in\n\n\nour 7-region survey (including Dhaka), only 9 (1.3%) report using an improved stove: 4\n\nIn this paper, we investigate individuals' exposure to indoor air pollution (IAP). Using new survey data from Bangladesh, we analyze exposure at two levels: differences within households attributable\n\nIn this paper, we use our survey data to estimate the incidence of IAP exposure for family", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Demographic and Health Surveys", "tail": "World Bank"}}]}, "_meta": {"entry_id": 146, "dataset_name": "Demographic and Health Surveys"}}
+{"input": "Recent analyses of macroeconomic trends have revealed significant insights from the Global Financial Inclusion Report and the Annual Economic Overview. These data sources provide a comprehensive understanding of the factors influencing access to financial services across different regions.", "output": {"entities": {"named_data": ["Global Financial Inclusion Report", "Annual Economic Overview"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "The Conflict Assessment Report 2022, published by the Global Peace Institute, provides detailed insights into the rising fragility in conflict zones worldwide. This report has been widely used by various humanitarian organizations, including the Red Cross, to guide their intervention strategies. Additionally, the African Security Database (ASD) has proven invaluable for researchers analyzing security trends across the continent, particularly in the year 2020. The data from the ASD has been referenced in studies addressing violence in East Africa, emphasizing its critical role in shaping effective policies.", "output": {"entities": {"named_data": ["Conflict Assessment Report 2022", "African Security Database"], "organization": ["Global Peace Institute", "Red Cross"], "acronym": ["ASD"], "year": ["2022", "2020"], "geography": ["East Africa"]}, "relations": [{"has_organization": {"head": "Conflict Assessment Report 2022", "tail": "Global Peace Institute"}}, {"used_by": {"head": "Conflict Assessment Report 2022", "tail": "Red Cross"}}, {"has_acronym": {"head": "African Security Database", "tail": "ASD"}}, {"has_timeframe": {"head": "African Security Database", "tail": "2020"}}, {"has_geography": {"head": "African Security Database", "tail": "East Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "conflict, fragility, and violence"}}
+{"input": "Yes Yes Yes Control: Occupation/Sector Yes Yes Sample: Working outside camp Yes Sample Size 743 742 742 572 Source: World Bank Staff based on SESRE 2023. Note: Monthly earnings are collected for employees only (including work for government, NGOs, and private households). Log earnings are winsorized at the 1st and 99th percentile within the domain. Regression coefficients are transformed to percent change interpretation using. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 106 Table D.7: Determinants of employment outcomes (1) (2) (3) (4) (5) (6) (7) (8) Hosts Refugees Working High-Skill Ln Income Working High-Skill Work Outside Ln Income Ln Income Male 0.235*** 0.030* 0.270*** 0.033 0.031* 0.172*** 0.506*** 0.876*** (0.021) (0.018) (0.061) (0.033) (0.017) (0.049) (0.101) (0.226) Age 0.067*** 0.008** 0.064*** 0.045*** 0.004 -0.005 0.007 -0.043 (0.004) (0.004) (0.017) (0.005) (0.003) (0.013) (0.028) (0.046) Age Sq. -0.001*** -0.000* -0.001*** -0.001*** -0.000 0.000 -0.000 0.001 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) Educ: < Primary - - - - - - - - Educ: Primary -0.028 0.058*** 0.127 -0.042* 0.077** -0.104 -0.172 0.121 (0.020) (0.021) (0.107) (0.022) (0.032) (0.069) (0.122) (0.361) Educ: Secondary 0.115*** 0.506*** 0.700*** -0.045", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank", "World Bank Staff"]}, "relations": [{"has_organization": {"head": "SESRE 2023", "tail": "World Bank"}}, {"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 326, "dataset_name": "SESRE 2023"}}
+{"input": "The United Nations Economic Commission for Europe ’ s (UNECE) guidelines include a question on reason for migration, population with a refugee-like background and IDPs as non-core topics / questions (UNHCR 2016). 72 While most countries include questions on country of birth and citizenship, only about 40 percent include a question on year of migration, less than a quarter include a question on reason for international migration, and about a fifth include a question on the reason for internal migration (UNHCR 2016). 73 E. g. Kyrgyz Republic 1999 (refugee status), West Bank and Gaza 2007 (refugee status), Zambia 2000 and 2010 (purpose of stay), Germany 1970 (federal refugee identity card), Greece 2001 (reason for settling in Greece), Sudan and South Sudan 2008 (type of household including IDP and refugee), Liberia 1990 (ever displaced by war since 1990), Uganda 2014 (refugees). 74 UNHCR is collaborating with the Statistics Norway on systematically embedding forcibly displaced peoples in national statistics exercises and collaborates with national authorities and with UNFPA in various countries on the design of census exercises that include refugees, IDPs, returnees and stateless persons. 75 LSMS is a household survey program housed in the Bank's Development Research Group that provides technical assistance to national statistical offices in the design and implementation of multi-topic household surveys covering household behavior, welfare and interactions with government policies. All data gathered through the LSMS is published online in the Bank ’ s Central Microdata Catalog.", "output": {"entities": {"named_data": ["Central Microdata Catalog"], "organization": ["Bank"]}, "relations": [{"has_organization": {"head": "Central Microdata Catalog", "tail": "Bank"}}, {"used_by": {"head": "Central Microdata Catalog", "tail": "Bank"}}]}, "_meta": {"entry_id": 940, "dataset_name": "Central Microdata Catalog"}}
+{"input": "The Poverty Assessment Report 2022 conducted by the Economic Research Institute provides critical insights into the current state of poverty and inequality across various demographics in the country. This report highlights not only the national poverty headcount but also disaggregates the data to reveal disparities among different regions. Understanding these nuances is essential for policymakers and stakeholders aiming to address these pressing issues effectively.", "output": {"entities": {"named_data": ["Poverty Assessment Report 2022"], "organization": ["Economic Research Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Poverty Assessment Report 2022", "tail": "Economic Research Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "poverty headcount and inequality measurement"}}
+{"input": "**At a crossroads. Unaccompanied and separated children in their transition to adulthood in Italy**\n\nframed within the conceptual framework of the ‘triple transition’. For each thematic focus of the research, the literature review also provided key information on the normative framework and on the consequent current pathways of UASC in Italy to better frame both qualitative and quantitative methods.\n\nThe quantitative approach is based on the collection of primary and secondary data from official national or regional sources on specific issues:\n\n**•**    - the demographic characteristics of UASC, the condition linked to their legal status and their geographical\n\npresence in the three regions where the research was carried out;\n\n**•**    - a questionnaire addressed to the 39 Adult Learning Centres (CPIAs) (19 in Lombardy, ten in Latium and\n\nten in Sicily), with questions on the number of UASC students divided by age, gender and nationality;\n\n**•**    - the opinions of UASC on their education and training pathways and internship experiences through\n\ntwo U-Report on the Move polls. [3] The qualitative approach [4] is based on: (i) interviews and focus group discussions (FGDs) with UASC and former UASC (166 males and 19 females); (ii) 46 interviews with key social and institutional informants at the regional and national levels (educators, social workers, teachers, volunteer guardians, institutional representatives); and (iii) nine interviews with representatives of ministries [5] and of the United Nations agencies that commissioned the research. In order to investigate UASC pathways, the biographical interview was identified as the most suitable among several alternatives in order to record the experiences, perceptions, time scale of the processes and the life experiences of boys and girls, leaving the interviewees ample scope to express themselves freely (Bichi, 2002; O’Leary, 2004). Three specific themes (see below) were then examined through FGDs, which enabled the analysis of the scenarios both from individual and group viewpoints, with a focus on the opportunities and the choices that have been made (Morgan and Kruger, 1998).\n\nIn addition to the interviews, a case study was carried out in each of the three regions with the aim of identifying promising practices in the following: (i) supervised independent living and housing solutions in support of UASC in Lombardy; (ii) formal and informal relationships supporting the transition to adulthood of UASC in Latium; and (iii) monitoring and support to volunteer guardians in Sicily. The three case studies identified experiences with a positive impact on the life pathways of UASC and former UASC, i.e. those that facilitate their transition to adult life. The qualitative tools used for the collection of data in the case studies were chosen on the basis of each case study: semi-structured interview with the Coordinator of the UASC Social Service of the Municipality of Milan for the Lombardy case study; semi-structured interviews with stakeholders for the Palermo and Lombardy case studies; FGDs with stakeholders for the Latium case study; and biographical interviews with UASC and former UASC (in all regions).\n\nThe research protocol was reviewed externally by Health Media labs and presented to key stakeholders in March 2019 – Ministry of the Interior (MoI), Ministry of Labour and Social Policies (MLSP), Ministry of Education (MoE), Ministry of Justice (MoJ), the Protection system for holders of international protection and foreign unaccompanied minors (SIPROIMI) and the National Ombudsperson for Children and Adolescents.\n\n15", "output": {"entities": {"named_data": ["U-Report on the Move polls"], "organization": ["United Nations", "Health Media labs"]}, "relations": [{"has_organization": {"head": "U-Report on the Move polls", "tail": "United Nations"}}, {"used_by": {"head": "U-Report on the Move polls", "tail": "Health Media labs"}}]}, "_meta": {"entry_id": 1393, "dataset_name": "U-Report on the Move polls"}}
+{"input": "\"Life in Transition Survey, Transition Report 2020-2021: The State Strike Back\", [https://www.ebrd.com/publications/transition-report-](https://www.ebrd.com/publications/transition-report-202021) [202021.](https://www.ebrd.com/publications/transition-report-202021)\n\n\"Strengthening the business environment for productivity convergence,\" in OECD Economic Surveys: Romania 2022, OECD Publishing, Paris, [https://doi.org/10.1787/63318cf5-en.](https://doi.org/10.1787/63318cf5-en)\n\n_Encuesta_ _Dirigida_ _a_ _la_ _Población_ _Venezolana_ _que_ _Reside_ _en_ _El_ _País_ _(ENPOVE)_ is a special ized survey of Venezuelans living in Peru conducted by the National Institute of Statistics (INEI) in December 2018. The sample covers five main urban areas in the country where Venezuelan immigrants were most likely to be present. The survey collects data on the immi grant's origin, migration date, and details on their current employment. Importantly, a full module asks about the immigrant's experiences with locals, which includes questions about discrimination and hostile attitudes towards them. The respondent's current location is iden tified down to the _centro_ _poblado_ level, which roughly corresponds to an urban neighborhood or a rural town.\n\n_Encuesta_ _Nacional_ _de_ _Hogares_ _(ENAHO)_ is the Peruvian version of the Living Standards Measurement Survey, e.g. a nationally representative household survey collected monthly on a continuous basis. For our analysis, we use data from January 2007 to December 2020. The survey covers a wide variety of topics, including basic demographics, educational back ground, labor market conditions, crime victimization, and a module on respondent's percep tions about the main problems in the country and trust on different local and national level institutions. Observations are also spatially identified at the municipality level, but here we focus on variation in the Venezuelan share of the population at the province level, of which there are 196, as these are best representative of local labor markets.\n\n_Latin_ _American_ _Public_ _Opinion_ _Project_ _(LAPOP)_ is a opinion survey conducted bi-annually in all countries in Latin America and designed to be representative of urban populations. This was fielded in Peru in 2010, 2012, 2014, 2017 and 2019 and consists of about 2,000 observations from mostly urban areas. The survey questions are centered around politics,", "output": {"entities": {"named_data": ["ENPOVE"], "organization": ["National Institute of Statistics (INEI)"]}, "relations": [{"has_organization": {"head": "ENPOVE", "tail": "National Institute of Statistics (INEI)"}}]}, "_meta": {"entry_id": 313, "dataset_name": "ENPOVE"}}
+{"input": "[8] F. N. P. Nimoh, T. P. Beltramo, J. R. Fix, F. K. Appler, U. J. Pape, and L. A. Rios Rivera, ‘Understanding the Socioeconomic Conditions of\nthe Stateless Shona Community in Kenya: Results from the 2019 Socioeconomic Survey’, Dec. 2020. Accessed: Apr. 02, 2024. [Online].\nAvailable: https://documents.worldbank.org/en/publication/documentsreports/documentdetail/356511608745182603/Understanding-the-Socioeconomic-Conditions-of-the-Stateless-Shona-Community-inKenya-Results-from-the-2019-Socioeconomic-Survey\n\n[9] U. J. Pape _et al._, ‘How COVID-19 Continues to Affect Lives of Refugees in Kenya : Rapid Response Phone Survey - Rounds 1 to 5’, World\nBank Group, Washington, D.C., Policy Note 166098, Oct. 2021. Accessed: Oct. 10, 2024. [Online]. Available:\nhttps://documents1.worldbank.org/curated/en/202201637042522937/pdf/How-COVID-19-Continues-to-Affect-Lives-of-Refugeesin-Kenya-Rapid-Response-Phone-Survey-Rounds-1-to-5.pdf\n\n[10] Kenya National Bureau of Statistics and ICF, ‘Kenya Demographic and Health Survey 2022. Key Indicators Report’, KNBS and ICF,\n\nNairobi, Kenya, and Rockville, Maryland, USA, 2023. Accessed: Oct. 10, 2024. [Online]. Available: https://www.knbs.or.ke/wpcontent/uploads/2023/08/Kenya-Demographic-and-Health-Survey-2022-Key-Indicators-Report.pdf", "output": {"entities": {"named_data": ["Kenya Demographic and Health Survey 2022"], "organization": ["Kenya National Bureau of Statistics and ICF"]}, "relations": [{"has_organization": {"head": "Kenya Demographic and Health Survey 2022", "tail": "Kenya National Bureau of Statistics and ICF"}}]}, "_meta": {"entry_id": 1280, "dataset_name": "Kenya Demographic and Health Survey 2022"}}
+{"input": "between 132 million and 587 million poor people are exposed to flood risks (depending on which poverty definition is used). About 1.2 billion flood-exposed people live in lower- and uppermiddle-income countries. Taking into account the income levels of flood exposed populations is particularly important, as income is a relatively reliable proxy for people’s ability to mitigate, withstand, cope with, and recover from floods. For instance, while a large share of the Dutch population lives in flood risk areas, large-scale investments in flood protection infrastructure have enabled them to mitigate risks. Similarly, flood exposed populations in Canada or Japan are more likely to have access to rapid government support systems in post-disaster situations compared to people in Malawi or Bangladesh. Thus, action to strengthen disaster prevention and recovery capacity is most urgently needed in the hotspots where poverty and flood exposure coincide. References Braese, J., S. De Vries Robbe, J. Rentschler, Jun. (2020). Coastal Development between Opportunity and Disaster Risk: A Multisectoral Risk Assessment for Vietnam. Policy Research Working Paper; No. 9352. World Bank, Washington Bright, E. A., Rose, A. N. & Urban, M. L. _Landscan 2015 High-Resolution Global Population Data Set_ . (Oak Ridge National Laboratory (ORNL), Oak Ridge, TN,", "output": {"entities": {"named_data": ["Landscan 2015 High-Resolution Global Population Data Set"], "organization": ["Oak Ridge National Laboratory (ORNL)", "World Bank"]}, "relations": [{"has_organization": {"head": "Landscan 2015 High-Resolution Global Population Data Set", "tail": "Oak Ridge National Laboratory (ORNL)"}}, {"used_by": {"head": "Landscan 2015 High-Resolution Global Population Data Set", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1125, "dataset_name": "Landscan 2015 High-Resolution Global Population Data Set"}}
+{"input": "The Digital Technology Adoption Survey (DTAS) conducted by Tech Innovations Inc. provides crucial insights into the technology usage patterns among small and medium enterprises (SMEs) in Southeast Asia. This dataset, collected in 2022, is vital for understanding the barriers and facilitators of digital transformation in this region.", "output": {"entities": {"named_data": ["Digital Technology Adoption Survey", "DTAS"], "organization": ["Tech Innovations Inc."], "acronym": ["DTAS"], "year": ["2022"], "geography": ["Southeast Asia"]}, "relations": [{"has_organization": {"head": "Digital Technology Adoption Survey", "tail": "Tech Innovations Inc."}}, {"has_acronym": {"head": "Digital Technology Adoption Survey", "tail": "DTAS"}}, {"has_timeframe": {"head": "Digital Technology Adoption Survey", "tail": "2022"}}, {"has_geography": {"head": "Digital Technology Adoption Survey", "tail": "Southeast Asia"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "digital development and technology adoption"}}
+{"input": "The Food Security Assessment Report 2022 provides critical insights into the agricultural trends in Sub-Saharan Africa. This report, published by the International Food Policy Research Institute (IFPRI), highlights the increasing challenges faced by farmers in the region due to climate change and economic instability.", "output": {"entities": {"named_data": ["Food Security Assessment Report 2022"], "organization": ["International Food Policy Research Institute", "IFPRI"], "acronym": ["IFPRI"], "year": ["2022"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Food Security Assessment Report 2022", "tail": "International Food Policy Research Institute"}}, {"has_acronym": {"head": "International Food Policy Research Institute", "tail": "IFPRI"}}, {"has_timeframe": {"head": "Food Security Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Food Security Assessment Report 2022", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "agriculture, food security, and nutrition"}}
+{"input": "The Food Security Assessment Report 2022, published by the Global Agriculture Initiative, provides extensive insights into rural nutrition levels across several countries. This dataset has been instrumental for various organizations, including the International Food Policy Research Institute (IFPRI), which utilized the findings to support their analysis of agricultural policies in the region. Additionally, the report highlights trends in food access during the 2020–2022 period, shedding light on the challenges faced in countries like Mozambique and Ethiopia. Overall, this collaborative effort underscores the importance of reliable data in shaping effective food security strategies.", "output": {"entities": {"named_data": ["Food Security Assessment Report 2022", "Food Security Assessment Report"], "organization": ["Global Agriculture Initiative", "International Food Policy Research Institute"], "acronym": ["IFPRI"], "year": ["2022", "2020–2022"], "geography": ["Mozambique", "Ethiopia"]}, "relations": [{"has_organization": {"head": "Food Security Assessment Report 2022", "tail": "Global Agriculture Initiative"}}, {"used_by": {"head": "Food Security Assessment Report 2022", "tail": "International Food Policy Research Institute"}}, {"has_acronym": {"head": "International Food Policy Research Institute", "tail": "IFPRI"}}, {"has_timeframe": {"head": "Food Security Assessment Report", "tail": "2020–2022"}}, {"has_geography": {"head": "Food Security Assessment Report", "tail": "Mozambique"}}, {"has_geography": {"head": "Food Security Assessment Report", "tail": "Ethiopia"}}, {"has_timeframe": {"head": "Food Security Assessment Report 2022", "tail": "2022"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "agriculture, food security, and nutrition"}}
+{"input": "The 2022 Global Refugee Status Report (GRSR) provides comprehensive insights into the patterns of forced displacement across various countries. Published by the International Organization for Migration (IOM), this report covers data from 2010 to 2022, emphasizing trends and challenges faced by refugees in regions like the Middle East and North Africa. While the report is utilized by numerous NGOs for advocacy and policy development, it is essential to approach its findings critically, recognizing the limitations of data collection in conflict zones. The GRSR serves as a vital resource for understanding the evolving landscape of migration and displacement amid ongoing global challenges.", "output": {"entities": {"named_data": ["Global Refugee Status Report"], "organization": ["International Organization for Migration"], "acronym": ["GRSR"], "year": ["2022", "2010 to 2022"], "geography": ["Middle East and North Africa"]}, "relations": [{"has_acronym": {"head": "Global Refugee Status Report", "tail": "GRSR"}}, {"has_timeframe": {"head": "Global Refugee Status Report", "tail": "2010 to 2022"}}, {"has_geography": {"head": "Global Refugee Status Report", "tail": "Middle East and North Africa"}}, {"has_organization": {"head": "Global Refugee Status Report", "tail": "International Organization for Migration"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "forced displacement, refugees, and migration"}}
+{"input": "The Regional Learning Achievement Survey (RLAS) conducted in 2022 provides critical insights into student performance metrics across Southeast Asia. Designed to assess learning outcomes in mathematics and reading, the survey data is pivotal for educational policy reforms in the region. Meanwhile, the National Enrollment Data (NED) for 2021 highlights trends in school enrollment rates across various countries, emphasizing the disparities in access to education. Both datasets serve as valuable resources for researchers and policymakers alike, although they differ in their geographical scope and objectives. Source: elaboration based on RLAS and NED datasets.", "output": {"entities": {"named_data": ["Regional Learning Achievement Survey", "National Enrollment Data"], "organization": ["Regional Educational Authority"], "acronym": ["RLAS", "NED"], "year": ["2022", "2021"], "geography": ["Southeast Asia"]}, "relations": [{"has_acronym": {"head": "Regional Learning Achievement Survey", "tail": "RLAS"}}, {"has_timeframe": {"head": "Regional Learning Achievement Survey", "tail": "2022"}}, {"has_geography": {"head": "Regional Learning Achievement Survey", "tail": "Southeast Asia"}}, {"has_acronym": {"head": "National Enrollment Data", "tail": "ned"}}, {"has_timeframe": {"head": "National Enrollment Data", "tail": "2021"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The recent analysis of urban expansion in Southeast Asia heavily relies on data from the Land Use Change Assessment Report 2022 (LUCA 2022). This comprehensive dataset provides insights into land cover changes across various regions, particularly in countries like Vietnam and Thailand. Researchers at the Asian Development Bank have utilized this valuable resource to evaluate the impacts of rapid urbanization on environmental sustainability. The LUCA 2022 dataset not only covers the year 2022 but also includes historical data spanning back to 2010, facilitating a deeper understanding of long-term trends in land use.", "output": {"entities": {"named_data": ["Land Use Change Assessment Report 2022"], "organization": ["Asian Development Bank"], "acronym": ["LUCA"], "year": ["2022", "2010"], "geography": ["Southeast Asia", "Vietnam", "Thailand"]}, "relations": [{"has_acronym": {"head": "Land Use Change Assessment Report 2022", "tail": "LUCA"}}, {"has_timeframe": {"head": "Land Use Change Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Land Use Change Assessment Report 2022", "tail": "2010"}}, {"has_geography": {"head": "Land Use Change Assessment Report 2022", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Land Use Change Assessment Report 2022", "tail": "Vietnam"}}, {"has_geography": {"head": "Land Use Change Assessment Report 2022", "tail": "Thailand"}}, {"used_by": {"head": "Land Use Change Assessment Report 2022", "tail": "Asian Development Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "The Climate Change Adaptation Report 2022 (CCAR) provides vital insights into the impact of climate change on agricultural practices across various regions. This report, published by the Global Environmental Agency, highlights adaptation strategies utilized in Africa during the period 2015–2022. Furthermore, the Biodiversity Assessment Database 2021 (BAD) aggregates comprehensive data on species distribution and ecosystem health, with a particular focus on Southeast Asia. This database serves as a crucial tool for researchers and policymakers working towards sustainable development in the region.", "output": {"entities": {"named_data": ["Climate Change Adaptation Report 2022", "Biodiversity Assessment Database 2021"], "organization": ["Global Environmental Agency"], "acronym": ["CCAR", "BAD"], "year": ["2022", "2015–2022", "2021"], "geography": ["Africa", "Southeast Asia"]}, "relations": [{"has_acronym": {"head": "Climate Change Adaptation Report 2022", "tail": "CCAR"}}, {"has_timeframe": {"head": "Climate Change Adaptation Report 2022", "tail": "2015–2022"}}, {"has_geography": {"head": "Climate Change Adaptation Report 2022", "tail": "Africa"}}, {"has_acronym": {"head": "Biodiversity Assessment Database 2021", "tail": "BAD"}}, {"has_timeframe": {"head": "Biodiversity Assessment Database 2021", "tail": "2021"}}, {"has_geography": {"head": "Biodiversity Assessment Database 2021", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Climate Change Adaptation Report 2022", "tail": "Global Environmental Agency"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "environmental sustainability and natural resources"}}
+{"input": "The 2022 Energy Access Report, published by the Global Energy Agency, provides critical insights into the progress made in expanding electricity access in developing nations. This comprehensive report analyzes data collected from various regions and highlights the challenges that persist in achieving universal energy access.", "output": {"entities": {"named_data": ["2022 Energy Access Report"], "organization": ["Global Energy Agency"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "2022 Energy Access Report", "tail": "Global Energy Agency"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}
+{"input": "treatment distance of 50 km, we no longer see a significant effect. In panel Ab, the results are\n\nreplicated using a spatial lag model, meaning that we allow for nonlinear effects with distance.\n\nIn reality, it seems like the electricity rate is much higher before a mine (dashed line) than with\n\nan active mine (the blue line). However, when the results are decomposed by migrant status in\n\npanel Ac of figure A1 (Annex) we find that migrants are driving the lower electricity rate. In\n\nfact, among nonmigrants, the electricity rate is higher 0–10 km from an active mine, although\n\nit is slightly lower 10–20 km away. [14]\n\n**6.3 Distributional effects on wealth and inequality**\n\nTable 9 presents the effects of mining on asset wealth and on asset wealth inequality. Wealth\n\ndata are available in the form of a wealth index, but only for the two last DHS surveys.\n\nFollowing Fenske (2015) and Flatø and Kotsadam (2014), we calculate inequality by means of\n\na Gini coefficient (recoding the wealth variable to be positive only, and using the command\n\n13 It is also possible that mining companies compete with households for electricity if supply cannot be increased\nin the short run.\n\n---\n[14] In panels Ba, Bb, and Bc of Appendix figure A1, we analyze access to radio. We learn that access to", "output": {"entities": {"named_data": ["DHS surveys"], "organization": ["DHS", "Fenske (2015) and Flatø and Kotsadam (2014)"]}, "relations": [{"has_organization": {"head": "DHS surveys", "tail": "DHS"}}, {"used_by": {"head": "DHS surveys", "tail": "Fenske (2015) and Flatø and Kotsadam (2014)"}}]}, "_meta": {"entry_id": 1342, "dataset_name": "DHS surveys"}}
+{"input": "The Urban Mobility Assessment Report 2022, published by the Transport Research Agency, provides critical insights into transportation usage patterns across urban areas. In particular, the report has been invaluable for city planners in Jakarta, who have leveraged its findings to improve public transport systems and reduce congestion. The agency also produced the Infrastructure Development Index (IDI), which tracks nationwide infrastructure conditions over a ten-year period from 2015 to 2025. This index has been instrumental for the Indonesian Ministry of Transportation as they formulate strategic initiatives to enhance connectivity throughout the country. Furthermore, the Global Urban Survey (GUS) released by the International Urban Development Institute (IUDI) in 2021, highlights key challenges faced by urban environments worldwide. Local governments have cited the GUS data to advocate for sustainable urban development policies.", "output": {"entities": {"named_data": ["Urban Mobility Assessment Report 2022", "Infrastructure Development Index", "Global Urban Survey"], "organization": ["Transport Research Agency", "Indonesian Ministry of Transportation", "International Urban Development Institute"], "acronym": ["GUS"], "year": ["2022", "2015 to 2025", "2021"], "geography": ["Jakarta", "Indonesia"]}, "relations": [{"has_organization": {"head": "Urban Mobility Assessment Report 2022", "tail": "Transport Research Agency"}}, {"used_by": {"head": "Urban Mobility Assessment Report 2022", "tail": "city planners in Jakarta"}}, {"has_organization": {"head": "Infrastructure Development Index", "tail": "Transport Research Agency"}}, {"used_by": {"head": "Infrastructure Development Index", "tail": "Indonesian Ministry of Transportation"}}, {"has_timeframe": {"head": "Infrastructure Development Index", "tail": "2015 to 2025"}}, {"has_organization": {"head": "Global Urban Survey", "tail": "International Urban Development Institute"}}, {"used_by": {"head": "Global Urban Survey", "tail": "Local governments"}}, {"has_timeframe": {"head": "Global Urban Survey", "tail": "2021"}}, {"has_geography": {"head": "Global Urban Survey", "tail": "Indonesia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "urban infrastructure and transportation planning"}}
+{"input": "The recent study on urban household water access in Sub-Saharan Africa utilized the Urban Water Access Assessment Report 2022 conducted by the Global Water Institute. This dataset, which provides a comprehensive view of water availability, was then cited by UNICEF in their ongoing efforts to improve sanitation conditions in the region. Furthermore, the analysis draws on the results from the Hygiene Practices Survey (HPS) published by WaterAid, which covers data from 2021. This survey has been instrumental in shaping the hygiene education programs implemented in countries like Kenya and Ghana.", "output": {"entities": {"named_data": ["Urban Water Access Assessment Report 2022", "Hygiene Practices Survey"], "organization": ["Global Water Institute", "UNICEF", "WaterAid"], "acronym": ["HPS"], "year": ["2022", "2021"], "geography": ["Sub-Saharan Africa", "Kenya", "Ghana"]}, "relations": [{"has_organization": {"head": "Urban Water Access Assessment Report 2022", "tail": "Global Water Institute"}}, {"used_by": {"head": "Urban Water Access Assessment Report 2022", "tail": "UNICEF"}}, {"has_timeframe": {"head": "Urban Water Access Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Urban Water Access Assessment Report 2022", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Hygiene Practices Survey", "tail": "WaterAid"}}, {"used_by": {"head": "Hygiene Practices Survey", "tail": "UNICEF"}}, {"has_timeframe": {"head": "Hygiene Practices Survey", "tail": "2021"}}, {"has_geography": {"head": "Hygiene Practices Survey", "tail": "Kenya"}}, {"has_geography": {"head": "Hygiene Practices Survey", "tail": "Ghana"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "water, sanitation, and hygiene"}}
+{"input": "The recently published Food Security Assessment Report 2022 by the Global Agriculture Organization provides vital insights into agricultural productivity and food access across various regions. This dataset, which covers the year 2022, is utilized by several NGOs, including Food Aid International, to analyze the impact of food shortages in Sub-Saharan Africa. In addition, the Agricultural Trends Database (ATD), created by the International Food Institute, offers comprehensive data on crop yields and is frequently referenced in studies conducted by regional agricultural ministries throughout the 2019–2023 period.", "output": {"entities": {"named_data": ["Food Security Assessment Report 2022", "Agricultural Trends Database"], "organization": ["Global Agriculture Organization", "Food Aid International", "International Food Institute"], "acronym": ["Food Security Assessment Report", "ATD"], "year": ["2022", "2019–2023"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Food Security Assessment Report 2022", "tail": "Global Agriculture Organization"}}, {"used_by": {"head": "Food Security Assessment Report 2022", "tail": "Food Aid International"}}, {"has_timeframe": {"head": "Food Security Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Food Security Assessment Report 2022", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Agricultural Trends Database", "tail": "International Food Institute"}}, {"used_by": {"head": "Agricultural Trends Database", "tail": "regional agricultural ministries"}}, {"has_acronym": {"head": "Agricultural Trends Database", "tail": "ATD"}}, {"has_timeframe": {"head": "Agricultural Trends Database", "tail": "2019–2023"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "agriculture, food security, and nutrition"}}
+{"input": "The recent findings on gender disparities in the labor market highlight the importance of comprehensive data sources. The report from the Gender Equality Assessment provides crucial insights into the challenges women face in achieving economic empowerment. Understanding these dynamics is essential for policymakers aiming to create effective interventions to support women in the workforce.", "output": {"entities": {"named_data": ["Gender Equality Assessment"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "gender equality and women's economic empowerment"}}
+{"input": "The recent Agricultural Baseline Survey 2023 has provided valuable insights into rural practices and food security challenges faced by farmers in Ethiopia. This dataset, published by the Food and Agriculture Organization (FAO), is essential for understanding the current agricultural landscape and informing policy decisions.", "output": {"entities": {"named_data": ["Agricultural Baseline Survey 2023"], "organization": ["Food and Agriculture Organization"], "acronym": [], "year": ["2023"], "geography": ["Ethiopia"]}, "relations": [{"has_organization": {"head": "Agricultural Baseline Survey 2023", "tail": "Food and Agriculture Organization"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "agriculture, food security, and nutrition"}}
+{"input": "The Global Land Use Mapping Database (GLUMD) provides essential insights for geospatial analysis and remote sensing applications. Published by the GeoSpatial Research Institute, the database offers comprehensive data that assists policymakers in making informed decisions about land use and environmental management.", "output": {"entities": {"named_data": ["Global Land Use Mapping Database"], "organization": ["GeoSpatial Research Institute"], "acronym": ["GLUMD"], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Global Land Use Mapping Database", "tail": "GeoSpatial Research Institute"}}, {"has_acronym": {"head": "Global Land Use Mapping Database", "tail": "GLUMD"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics. Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013. The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare. 1. DATA AND TECHNICAL ISSUES Data Sets Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty, 5 UNCHR, 2013pg 13 6 UNHCR (22 November 2013), UNHCR (15 September 2014) 7 UNHCR (22 March 2013), Erdogan (2014), pg 14 8 This technical issue will be discussed further later in the paper.", "output": {"entities": {"named_data": ["Survey on Income and Living Conditions"], "organization": ["Turkish Statistical Institute"]}, "relations": [{"has_organization": {"head": "Survey on Income and Living Conditions", "tail": "Turkish Statistical Institute"}}]}, "_meta": {"entry_id": 740, "dataset_name": "Survey on Income and Living Conditions"}}
+{"input": "The Water Quality Assessment Report 2022 provides critical insights into the levels of contaminants found in drinking water across various regions. This report, produced by the Global Water Institute, serves as a key resource for policymakers and health officials aiming to ensure safe drinking water standards.", "output": {"entities": {"named_data": ["Water Quality Assessment Report 2022"], "organization": ["Global Water Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Water Quality Assessment Report 2022", "tail": "Global Water Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "water, sanitation, and hygiene"}}
+{"input": "future year by the counterpart observation in the CRU benchmark dataset (from the most\n\nrepresentative temperature/rainfall combination, derived from CRU data for 1980-2000).\n\ndeparture: the benchmark series from the CRU data. This translation step is necessary", "output": {"entities": {"named_data": ["CRU benchmark dataset"], "organization": ["CRU"]}, "relations": [{"has_organization": {"head": "CRU benchmark dataset", "tail": "CRU"}}]}, "_meta": {"entry_id": 393, "dataset_name": "CRU benchmark dataset"}}
+{"input": "Hosts Refugees Eritrean Somali South Sudanese Figure D.24: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.23: Household owns crops Source: World Bank Staff based on SESRE 2023. 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.25: Total value of livestock Source: World Bank Staff based on SESRE 2023. 0 5,000 10,000 15,000 20,000 25,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.26: Value per tropical livestock unit Source: World Bank Staff based on SESRE 2023. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.27: Household has non-farm business Source: World Bank Staff based on SESRE 2023. Annexes 110 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.28: Value of productive assets in households with business Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Percent 70 80 90 100 Hosts Refugees Hosts Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank Staff"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 340, "dataset_name": "SESRE 2023"}}
+{"input": "Furthermore, in some contexts, registration as an IDP can expire after a prescribed timeframe without regard to whether the person achieved a durable solution (e. g. after five years in Russia). The accuracy of IDP registers is greatly impacted by political considerations, particularly a government ’ s willingness to acknowledge internal displacement and to enable the humanitarian community to respond. Some countries may be reluctant to acknowledge the presence of IDPs, or may be inclined to understate numbers to demonstrate progress in military operations or limit assistance provided to IDPs. For example, in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs. Alternatively, aggregate numbers of IDPs in particular countries may be inflated to suggest a deterioration of the situation or to maximize humanitarian assistance. Therefore, access to IDP areas and the willingness of IDPs to be counted may be largely dependent on government policies. These political considerations can lead to disagreements on the data, undermine cooperation and in some cases even lead to reduced humanitarian funding. Profiling of IDP situations Profiling of IDP situations is a collaborative process aimed at generating reliable data that can be broadly agreed upon. As a collaborative process, it can be a crucial tool for generating agreement on persistent questions such as who is recognized as internally displaced within a given context, what are the most prevalent vulnerabilities caused by displacement, and how do IDPs fare compared to host populations. In sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs. 68 However, in many conflict-affected countries, governments lack the basic capacity to maintain Civil Registration and Vital Statistics (CRVS) systems including the registration of births and deaths in non-displacement situations, let alone the registration of IDPs displaced due to natural disasters or conflict. 69 IOM has introduced biometric registration systems in South Sudan, Sudan, DRC and Nigeria to circumvent these problems.", "output": {"entities": {"named_data": ["Civil Registration and Vital Statistics"], "organization": ["IOM"]}, "relations": [{"has_organization": {"head": "Civil Registration and Vital Statistics", "tail": "IOM"}}]}, "_meta": {"entry_id": 54, "dataset_name": "Civil Registration and Vital Statistics"}}
+{"input": "15 resulting in government forces recapturing rebel held territory, establishment of a rebel base or headquarters, rebel activity that is not battle related (e. g. presence or the killing of civilians), and territorial transfers. The dataset consists of 4, 145 battle events for the 1960 – 2004 period. In the present analysis, we use 2, 530 of these. The remaining events were dropped as they either were in countries not included in the analysis, or because information was missing for one of the key variables. Each conflict event is associated with geographic coordinates and a date of occurrence. This information allows for spatial and temporal modeling of conflict events. The dataset used in this article covers 14 countries in Central Africa. 6 of them had a conflict in the 1960 – 2004 period according to the Uppsala / PRIO Armed Conflict Dataset (Gleditsch et al., 2002): Angola, Burundi, Republic of Congo (Brazzaville), Democratic Republic of Congo (Zaire), Rwanda, and Uganda.", "output": {"entities": {"named_data": ["Uppsala / PRIO Armed Conflict Dataset"], "organization": ["Gleditsch et al."]}, "relations": [{"used_by": {"head": "Uppsala / PRIO Armed Conflict Dataset", "tail": "Gleditsch et al."}}]}, "_meta": {"entry_id": 788, "dataset_name": "Uppsala / PRIO Armed Conflict Dataset"}}
+{"input": "The Digital Connectivity Assessment (DCA) provides insights into internet usage patterns across various demographics. Conducted by the Global Tech Foundation in 2022, this assessment highlights the disparities in digital access within different regions, particularly in Sub-Saharan Africa. The findings underscore the pressing need for targeted policies to enhance connectivity in underserved areas. The DCA is especially relevant as governments and organizations seek to leverage technology for development, assisting in the formulation of initiatives aimed at bridging the digital divide.", "output": {"entities": {"named_data": ["Digital Connectivity Assessment"], "organization": ["Global Tech Foundation"], "acronym": ["DCA"], "year": ["2022"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Digital Connectivity Assessment", "tail": "DCA"}}, {"has_timeframe": {"head": "Digital Connectivity Assessment", "tail": "2022"}}, {"has_geography": {"head": "Digital Connectivity Assessment", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Digital Connectivity Assessment", "tail": "Global Tech Foundation"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "digital development and technology adoption"}}
+{"input": "The Climate Resilience Assessment Report 2022, published by the Global Environmental Institute (GEI), provides critical insights into regional vulnerabilities and adaptive capacities across various nations. In this report, the data focuses on South Asia, specifically highlighting the importance of addressing climate risks in Bangladesh. Meanwhile, the data from the Emergency Preparedness Survey (EPS) conducted by the International Disaster Management Organization (IDMO) is utilized by various stakeholders, including local governments and NGOs to develop comprehensive disaster response strategies. The EPS data, covering the period from 2019 to 2021, has proved invaluable for enhancing community resilience in affected areas.", "output": {"entities": {"named_data": ["Climate Resilience Assessment Report 2022", "Emergency Preparedness Survey"], "organization": ["Global Environmental Institute", "International Disaster Management Organization"], "acronym": ["GEI", "IDMO"], "year": ["2022", "2019 to 2021"], "geography": ["South Asia", "Bangladesh"]}, "relations": [{"has_organization": {"head": "Climate Resilience Assessment Report 2022", "tail": "Global Environmental Institute"}}, {"used_by": {"head": "Climate Resilience Assessment Report 2022", "tail": "various stakeholders"}}, {"has_geography": {"head": "Climate Resilience Assessment Report 2022", "tail": "South Asia"}}, {"has_geography": {"head": "Climate Resilience Assessment Report 2022", "tail": "Bangladesh"}}, {"has_acronym": {"head": "Climate Resilience Assessment Report", "tail": "GEI"}}, {"has_organization": {"head": "Emergency Preparedness Survey", "tail": "International Disaster Management Organization"}}, {"used_by": {"head": "Emergency Preparedness Survey", "tail": "local governments"}}, {"used_by": {"head": "Emergency Preparedness Survey", "tail": "NGOs"}}, {"has_timeframe": {"head": "Emergency Preparedness Survey", "tail": "2019 to 2021"}}, {"has_acronym": {"head": "Emergency Preparedness Survey", "tail": "IDMO"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "The 2020 National Fertility and Demographic Assessment Report (NFDA 2020) provides comprehensive insights into fertility trends across various regions in the country. Conducted by the National Institute of Population Studies, the report analyzes data collected from urban and rural areas, highlighting significant disparities in fertility rates. While the report emphasizes the need for targeted policy interventions, it is also cited by various NGOs working in the field of reproductive health. The NFDA 2020 underscores how demographic changes can influence economic development over the coming years.", "output": {"entities": {"named_data": ["2020 National Fertility and Demographic Assessment Report", "NFDA 2020"], "organization": ["National Institute of Population Studies", "NGOs"], "acronym": ["NFDA"], "year": ["2020"], "geography": ["country"]}, "relations": [{"has_acronym": {"head": "2020 National Fertility and Demographic Assessment Report", "tail": "NFDA"}}, {"has_timeframe": {"head": "2020 National Fertility and Demographic Assessment Report", "tail": "2020"}}, {"has_geography": {"head": "2020 National Fertility and Demographic Assessment Report", "tail": "country"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "demographics, fertility, and population growth"}}
+{"input": "**The World Bank**\nBalochistan Human Capital Investment Project (P166308)\n\n(FMS), and an environmental and social safeguards specialist/officer. [47] The PMUs will be fully authorized\nto implement the planned activities approved by the Project Steering Committee (PSC).\n\n46. **A Project Coordination Committee (PCC) will be set up to coordinate project implementation**\n\n**and a PSC will be set up to provide strategic guidance and oversight.** The PCC, co‐chaired by Secretaries\nHealth and Secondary Education, will meet quarterly. The PSC, chaired by the Additional Chief Secretary,\nwill meet biannually (see figure 2).\n\n**Figure 2. Institutional and Implementation Arrangements**\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n47. **Building on the results chain, the M&E framework identified indicators to track project**\n\n**implementation progress and impact.** The PDO‐level health indicators are taken from the RMNCHN\nindicators in the DHIS, while digitization and integration of various HMIS is an intermediate indicator. The\neducation indicators are taken from the EMIS. Where possible, relevant indicators will be disaggregated\nby gender. Discussions with the GoB and the UNHCR have confirmed, however, that beneficiary data by\nnationality will not be routinely collected or publicly released.\n\n48. **The project M&E will leverage and strengthen existing routine information systems, and finance**\n\n---\n[47] During the early phase of implementation, the Governance and Policy Program (GPP) PMU will provide back‐up support.", "output": {"entities": {"named_data": ["EMIS"], "organization": ["The World Bank"]}, "relations": [{"used_by": {"head": "EMIS", "tail": "The World Bank"}}]}, "_meta": {"entry_id": 866, "dataset_name": "EMIS"}}
+{"input": "GoR has progressive laws and policies on forced displacement.** The World Bank, in consultation with the United Nations High Commissioner for Refugees (UNHCR), has confirmed that Rwanda’s refugee protection framework is adequate for the purposes of access to the Window for Host Communities and Refugees (WHR). [5] Rwanda is a signatory to the 1951 Refugee Convention and the Global Compact on Refugees (GCR). The 2014 _Law_ _Relating to Refugees_ complies with international law and entrenches the right to work and freedom of movement. At the policy level, Rwanda’s _Strategic Plan for Refugee Inclusion 2019-2024_ made commitments to: (a) provide all [1 Data is from April 30, 2025. There are also 14,936 asylum-seekers in Rwanda: see https://data.unhcr.org/en/country/rwa](https://data.unhcr.org/en/country/rwa) [2 Loschmann, C., Bilgili, O. & Siegel, M. (2019) “Considering the benefits of hosting refugees: evidence of refugee camps influencing local labour](https://link.springer.com/article/10.1186/s40176-018-0138-2) [market activity and economic welfare in Rwanda,” IZA Journal of Development & Migration, pp. 1-23.](https://link.springer.com/article/10.1186/s40176-018-0138-2) 3 The self-reliance survey is part of the “Enhancing Self-Reliance and Preparedness for Forced Displacement in the Great Lakes Region” activity [(P500793) and draws on the global Refugee Self-Reliance Initiative index (www.refugeeselfreliance.org).](http://www.refugeeselfreliance.org/) 4 Phase I of Jya Mbere was approved on April 30, 2019, and will close", "output": {"entities": {"named_data": ["global Refugee Self-Reliance Initiative index"], "organization": ["United Nations High Commissioner for Refugees (UNHCR)", "The World Bank"]}, "relations": [{"has_organization": {"head": "global Refugee Self-Reliance Initiative index", "tail": "United Nations High Commissioner for Refugees (UNHCR)"}}, {"used_by": {"head": "global Refugee Self-Reliance Initiative index", "tail": "The World Bank"}}]}, "_meta": {"entry_id": 1197, "dataset_name": "global Refugee Self-Reliance Initiative index"}}
+{"input": "Therefore, in addition we adopted another adapted calculation method which is based on travel paths simulations using OD pairs from the JICA travel survey.\n\nThe conventional approach calculates shortest paths between every possible pair of nodes within the network whereas the adapted approach simulates the travel trajectories based on the multimodal transportation model and accounts for public transport waiting times, road speed limits, and designated origin and destination pairs acquired from the JICA commuter travel survey.\n\n**Acknowledgments:** We would like to thank GoMetro for leading the public transport mapping, and in\nparticular Bob Kabeya and Clayton Lane, as well as the enumerators that conducted these surveys. We thank\nShohei Nakamura, Takaaki Masaki and Mervy Ever Viboudoulou Vilpoux for their help in accessing\nKinshasa commuter and household surveys. We are grateful to Laurent Corroyer, Stephane Hallegatte and\nKirsten Homman for their inputs and feedback throughout this project. We are also grateful to Catalina\nOchoa for her review of an earlier draft of this paper and her thoughtful comments. This study was supported\nby the Global Facility for Disaster Risk Reduction and Recovery (GFDRR).\n\nThese data sets were combined with travel survey data containing travelers' socioeconomic attributes and trip parameters, as well as a high-resolution flood maps.", "output": {"entities": {"named_data": ["JICA travel survey"], "organization": ["JICA", "GoMetro"]}, "relations": [{"has_organization": {"head": "JICA travel survey", "tail": "JICA"}}, {"used_by": {"head": "JICA travel survey", "tail": "GoMetro"}}]}, "_meta": {"entry_id": 481, "dataset_name": "JICA travel survey"}}
+{"input": "**Electricity** **Capital** **GDP** **Consumption** **Stock** **Population** Median 3.84 3.65 3.45 1.44 Average 3.48 4.10 3.88 1.43 Standard Deviation 4.37 6.87 2.77 0.97 Skewness -1.58 1.56 0.81 0.11 Kurtosis 9.83 20.48 1.10 -0.33 _Sources: World Development Indicators, Penn World Tables Version 8, and author's calculations._\n\n<!-- PAGE 19 -->\n#### **VII. Hypothesis Testing**\n\nWe explore two basic questions. First, could night-time lights data serve as a good proxy for any of the\neconomic variables considered in this paper?\n\nA new satellite is generating superior night-time lights data. The data are generated by the Suomi National Polar-orbiting Partnership (SNPP) satellite series operated by the NASA - NOAA Joint Polar Satellite System that was launched in late 2011.\n\nobserved in cross-section surveys - even when controlling for other observable characteristics -   cannot directly be interpreted as reflecting a change in attitudes over the life-cycle.", "output": {"entities": {"named_data": ["World Development Indicators"], "organization": ["author"]}, "relations": [{"used_by": {"head": "World Development Indicators", "tail": "author"}}]}, "_meta": {"entry_id": 189, "dataset_name": "World Development Indicators"}}
+{"input": "Night-time lights data are a beneficial by-product of a meteorological satellite program. The data are\ncollected by the United States Air Force Defense Meteorological Satellite Program (DMSP). DMSP\nsatellites have been circling the earth since the 1970s in a polar orbit that allows observations of every\n\n1. Number of illuminated pixels with _DN_ >=6 within the borders of a country.\n2. Average _DN_ within all illuminated pixels.\n_Sources:_ NGDC v4, World Development Indicators for land area, and author's calculations.\n\nLong-run patterns in _AoL_ and _R_ are consistent with some country circumstances. For example, as shown\nin Table 1, a country with a rapidly expanding _AoL_ is more likely to be a country with a high urban\npopulation growth rate. China, Indonesia, Malaysia, Vietnam, and Yemen are examples. A country with\nshrinking _AoL_ could be in the early, painful stages of transition from a planned economy to a market\neconomy. Azerbaijan, Tajikistan and Ukraine are examples. Countries with growing average radiance, _R_,\n\nSouknilanh et al (2015) find their night-light based _GDP_ estimates are improved when supplemented by ground cover data from a second satellite (MODIS).\n\n16 Normally there is a quasi-fixed ratio of intermediates to gross output which slowly falls as productivity improves. Countries that import most of their intermediates will be subject to external shocks (trading partner demand, terms of trade) that disrupt this relationship. 17 From a sample of 166 countries in 2010, from the World Development Indicators.\n\n[26] The result is the v.4 DMSP stable lights data set with between 20 and 100 observations per year per pixel depending upon circumstances (Baugh et al.", "output": {"entities": {"named_data": ["Defense Meteorological Satellite Program"], "organization": ["Souknilanh et al (2015)"]}, "relations": [{"used_by": {"head": "Defense Meteorological Satellite Program", "tail": "Souknilanh et al (2015)"}}]}, "_meta": {"entry_id": 592, "dataset_name": "Defense Meteorological Satellite Program"}}
+{"input": "The recent report on global forced displacement highlights findings from the Refugee Assessment Survey (RAS) conducted by the International Organization for Migration (IOM) in 2022. The data from this assessment indicates significant trends in migration patterns, which were further analyzed by the United Nations High Commissioner for Refugees (UNHCR). Additional insights were drawn from the Forced Migration Data Set (FMDS) published by the World Bank in 2021, showcasing the economic impact of refugee movements across various regions. Moreover, the FMDS has been employed by multiple non-governmental organizations (NGOs) to inform their policies and responses to the refugee crisis in sub-Saharan Africa.", "output": {"entities": {"named_data": ["Refugee Assessment Survey", "Forced Migration Data Set"], "organization": ["International Organization for Migration", "United Nations High Commissioner for Refugees", "World Bank", "non-governmental organizations"], "acronym": ["RAS", "FMDS"], "year": ["2022", "2021"], "geography": ["sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Refugee Assessment Survey", "tail": "International Organization for Migration"}}, {"used_by": {"head": "Refugee Assessment Survey", "tail": "United Nations High Commissioner for Refugees"}}, {"has_organization": {"head": "Forced Migration Data Set", "tail": "World Bank"}}, {"used_by": {"head": "Forced Migration Data Set", "tail": "non-governmental organizations"}}, {"has_acronym": {"head": "Refugee Assessment Survey", "tail": "RAS"}}, {"has_acronym": {"head": "Forced Migration Data Set", "tail": "FMDS"}}, {"has_timeframe": {"head": "Refugee Assessment Survey", "tail": "2022"}}, {"has_timeframe": {"head": "Forced Migration Data Set", "tail": "2021"}}, {"has_geography": {"head": "Forced Migration Data Set", "tail": "sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "forced displacement, refugees, and migration"}}
+{"input": "The Domestic Revenue Mobilization Survey (DRMS) conducted in 2022 provides critical insights into revenue collection practices across various nations. This survey, which covers data from over 30 developing countries, is designed to assist policymakers in improving fiscal transparency and efficiency. Source: World Bank elaboration based on the DRMS. The findings highlight significant regional variations, particularly in Africa and Southeast Asia, and underscore the need for tailored strategies to enhance tax compliance in these regions.", "output": {"entities": {"named_data": ["Domestic Revenue Mobilization Survey"], "organization": ["World Bank"], "acronym": ["DRMS"], "year": ["2022"], "geography": ["Africa", "Southeast Asia"]}, "relations": [{"has_acronym": {"head": "Domestic Revenue Mobilization Survey", "tail": "DRMS"}}, {"has_timeframe": {"head": "Domestic Revenue Mobilization Survey", "tail": "2022"}}, {"has_geography": {"head": "Domestic Revenue Mobilization Survey", "tail": "Africa"}}, {"has_geography": {"head": "Domestic Revenue Mobilization Survey", "tail": "Southeast Asia"}}, {"used_by": {"head": "Domestic Revenue Mobilization Survey", "tail": "World Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "public financial management and domestic revenue"}}
+{"input": "## Secondary Data Review\n# GENDER-BASED VIOLENCE – Mozambique: Cyclone Idai and Floods\n\nAPRIL 2019 difficult to access. As a result of the cyclone and floods, people in remote affected areas face challenges accessing district hospitals.\n\nTo ensure a private and protective environment, there is a need to refurbish the maternity wards, where the survivors are treated. Further, the administrative offices of the Women and Children’s Protection desk need to be rehabilitated, so a private and protective environment can be restored for women and girls to report confidentially.\n\n**HIV:** It is estimated that almost 1.6 million people, including more than 90,000 children,\nare infected with HIV in Mozambique. More than half of those infected are women, and\n[15% of pregnant women aged 15–49 are HIV-positive (ODI, 2014). The prevalence rate](https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/9073.pdf) of HIV in the population aged 15-49 is 13.2% (15.4% in women and 10.1% in men).\nSofala has a higher rate (16.3%) than other affected districts [(DHS, 2015). Furthermore,](https://www.dhsprogram.com/pubs/pdf/AIS12/AIS12_SP.pdf) women and girls are particularly affected by the epidemic because they usually lack the\n[power to refuse unsafe sex, choose their partners or influence sexual behavior (ODI 2014).](https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/9073.pdf)\n\n**Sexual and Reproductive Health:** According to the latest estimation, about 438,000\nwomen affected by the cyclone are of reproductive age (15-49). Among those, 64,000 women are estimated to be pregnant and more than 37,000 women in affected areas are estimated to give birth in the next three months. Among those giving birth, up to 9,600 may be at risk of complications during their pregnancy in the next three months.\nThey will they will need access to functioning health facilities and care (UNFPA, 4/2019).\n\n**Shelter:** According to the Demographic Health Survey some 28% of people in Sofala did\nnot have electricity. A further 28.9% of people lived in a cement house, 36.9% in an earthen mud-house [(DHS, 2015).](https://www.dhsprogram.com/pubs/pdf/AIS12/AIS12_SP.pdf) According to the latest DTM there are currently over 13,600 people hosted in 24\n[displacement sites in Beira (DTM R2, 4/2019). This includes five planned sites, the remainder](https://displacement.iom.int/reports/moçambique-—-ciclone-tropical-idai-inquérito-dos-locais-de-deslocamento-beira-dondo-e) are temporary transit sites including schools and public buildings. Site plans of the five accommodation centers suggest that currently up to 5–6 families are sharing one tent,\n[and averagely 12m2 space is available per person (calculated based on CCCM site plans](https://www.humanitarianresponse.info/en/operations/mozambique/infographic/temporary-relocation-sites-layout) and\n[DTM Round 2, 4/2019). The SPHERE standards prescribe a minimum of 30m2 for camp-like](https://displacement.iom.int/reports/moçambique-—-ciclone-tropical-idai-inquérito-dos-locais-de-deslocamento-beira-dondo-e) settlements. Overcrowding and inadequate privacy for women and girls are observed, which is a risk to GBV.\nWhile the site plans are designed to follow minimum standards and provide suitable living conditions, many elements have contributed to increased vulnerabilities and overstretched capacity on site: ad-hoc relocation plans, lack of sensitization among surrounding communities, lack of communication to the population, and a lack of systematic allocation of tents (Protection Monitoring, 4/2019). It is also reported that more than half of the sites do not have adequate lighting in any common place; which is considered a major risk to GBV and can hamper the freedom of movement of women and girls [(DTM](https://displacement.iom.int/reports/moçambique-—-ciclone-tropical-idai-inquérito-dos-locais-de-deslocamento-beira-dondo-e)\n[R2, 4/2019). The MRA in Dondo district shows that in more than half surveyed locations,](https://displacement.iom.int/reports/moçambique-—-ciclone-tropical-idai-inquérito-dos-locais-de-deslocamento-beira-dondo-e) people have been sleeping in open areas. In Buzi all but one location there were surveyed families who reported they were sleeping outside (MRA, 4/2019). Sleeping out in the open is unsafe and can increase the risk of GBV to women and girls.\n\n**Documentation:** The affected population have lost most or all of their documents during\nthe cyclone. This include identity documentation and other vital certificates. An estimated 19% of people in Sofala already did not have birth certificates or registration in 2015\n[(DHS 2015). The lack of mechanisms to facilitate attainment of these documents is likely to](https://dhsprogram.com/pubs/pdf/AIS12/AIS12.pdf) hinder access to services, including enrolment in schools and engagement in some livelihood activities (Protection cluster, 4/2019). A lack of basic needs, or possibility to engage in livelihood activities, will lead to faster depletion of coping mechanisms of the affected population. This means that negative coping mechanisms including those that pose a higher risk to GBV will be adopted faster. A lack of engagement in school for children also increases the risk of child labor and exploitation.\n\n**Eviction** : Assessments in the displacement sites suggested that 11 (3 in Nhamatanga\n[district and 8 in Beira district) out of 41 sites reported there is an eviction threat (DTM R2,](https://displacement.iom.int/reports/moçambique-—-ciclone-tropical-idai-inquérito-dos-locais-de-deslocamento-beira-dondo-e)\n[4/2019). However the answer is perception-based and it is likely to indicate the lack of](https://displacement.iom.int/reports/moçambique-—-ciclone-tropical-idai-inquérito-dos-locais-de-deslocamento-beira-dondo-e) communication and sensitization to the displaced population. In multiple reports the importance for communities to get timely and clear information on relocation processes is emphasized (Protection cluster, 4/2019).\n\n**Child Protection:** Over 28% of households in Mozambique have adopted children [(DHS](https://dhsprogram.com/pubs/pdf/AIS12/AIS12.pdf)\n[2015).](https://dhsprogram.com/pubs/pdf/AIS12/AIS12.pdf) Almost 50% of young Mozambican women aged 20-24 years were married\n[before the age of 18, while 14% were married before the age of 15 (CARE, 3/2019).](https://reliefweb.int/sites/reliefweb.int/files/resources/Regional-RGA-Cyclone-Idai-29032019.pdf) According to MRA in Dondo, among children under 14 years old, a total of 22.3% were orphan by father (13.2%), by mother (6%) or both parents (3%). The assessment further showed that in six affected districts, helping out the family by doing chores and other work are among the main reason children (both boys and girls) are not attending schools.\nIn about 10% of locations assessed, communities reported that they have noticed there are children disappearing (or being trafficked). In almost 40% of assessed locations communities reported having come across children who are forced to work to provide 5", "output": {"entities": {"named_data": ["CCCM site plans"], "organization": ["DHS", "Protection Monitoring"]}, "relations": [{"has_organization": {"head": "CCCM site plans", "tail": "DHS"}}, {"used_by": {"head": "CCCM site plans", "tail": "Protection Monitoring"}}]}, "_meta": {"entry_id": 1382, "dataset_name": "CCCM site plans"}}
+{"input": "The Fertility and Population Growth Survey (FPGS), conducted in 2020, provides critical insights into demographic trends across various regions. This dataset, produced by the National Institute of Statistics, highlights key fertility indicators for countries such as Nigeria and Kenya. Notably, the FPGS has been instrumental in regional studies on population dynamics, often cited in research by organizations like UNICEF. Additionally, the 2021 Demographic Survey Assessment (DSA) focuses specifically on urban and rural population changes, covering data from all provinces of Mozambique. The DSA serves as a vital resource for policymakers and researchers, contributing to the understanding of demographic shifts during this period.", "output": {"entities": {"named_data": ["Fertility and Population Growth Survey", "Demographic Survey Assessment"], "organization": ["National Institute of Statistics", "UNICEF"], "acronym": ["FPGS", "DSA"], "year": ["2020", "2021"], "geography": ["Nigeria", "Kenya", "Mozambique"]}, "relations": [{"has_acronym": {"head": "Fertility and Population Growth Survey", "tail": "FPGS"}}, {"has_timeframe": {"head": "Fertility and Population Growth Survey", "tail": "2020"}}, {"has_geography": {"head": "Fertility and Population Growth Survey", "tail": "Nigeria"}}, {"has_geography": {"head": "Fertility and Population Growth Survey", "tail": "Kenya"}}, {"has_acronym": {"head": "Demographic Survey Assessment", "tail": "DSA"}}, {"has_timeframe": {"head": "Demographic Survey Assessment", "tail": "2021"}}, {"has_geography": {"head": "Demographic Survey Assessment", "tail": "Mozambique"}}, {"has_organization": {"head": "Fertility and Population Growth Survey", "tail": "National Institute of Statistics"}}, {"used_by": {"head": "Fertility and Population Growth Survey", "tail": "UNICEF"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "demographics, fertility, and population growth"}}
+{"input": "The Urban Mobility Assessment Report 2022, published by the International Transportation Institute (ITI), provides critical insights into urban traffic patterns in major cities. This dataset is widely used by the Metropolitan Planning Organizations (MPOs) to inform their transportation strategies and improve infrastructure. The analysis presented in the report highlights trends from 2018 to 2022, emphasizing the growing need for sustainable urban transit solutions. Additionally, the Global Urban Infrastructure Database (GUIDE), maintained by the World Research Council, is another crucial resource utilized by city planners across several countries, including Brazil and Kenya, to analyze urban development metrics.", "output": {"entities": {"named_data": ["Urban Mobility Assessment Report 2022", "Global Urban Infrastructure Database"], "organization": ["International Transportation Institute", "Metropolitan Planning Organizations", "World Research Council"], "acronym": ["ITI", "GUIDE"], "year": ["2022", "2018 to 2022"], "geography": ["Brazil", "Kenya"]}, "relations": [{"has_organization": {"head": "Urban Mobility Assessment Report 2022", "tail": "International Transportation Institute"}}, {"used_by": {"head": "Urban Mobility Assessment Report 2022", "tail": "Metropolitan Planning Organizations"}}, {"has_timeframe": {"head": "Urban Mobility Assessment Report 2022", "tail": "2018 to 2022"}}, {"has_geography": {"head": "Urban Mobility Assessment Report 2022", "tail": "major cities"}}, {"has_organization": {"head": "Global Urban Infrastructure Database", "tail": "World Research Council"}}, {"used_by": {"head": "Global Urban Infrastructure Database", "tail": "Metropolitan Planning Organizations"}}, {"has_geography": {"head": "Global Urban Infrastructure Database", "tail": "Brazil"}}, {"has_geography": {"head": "Global Urban Infrastructure Database", "tail": "Kenya"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "urban infrastructure and transportation planning"}}
+{"input": "The recent analysis of financial inclusion trends in Southeast Asia has leveraged data from the Southeast Asia Economic Monitoring Report 2022, published by the Asian Development Bank (ADB). This comprehensive report highlights critical indicators of economic performance and access to financial services across the region. Additionally, the World Bank has utilized the Financial Access Survey (FAS) data, which covers the years 2018-2021, to examine the correlation between economic growth and financial inclusion in countries like Indonesia and Thailand. Furthermore, the 2023 Microfinance Sector Assessment Report, produced by the International Finance Corporation (IFC), offers insights into the microfinance landscape, which has been cited by several NGOs working on poverty alleviation initiatives in the region.", "output": {"entities": {"named_data": ["Southeast Asia Economic Monitoring Report 2022", "Financial Access Survey", "2023 Microfinance Sector Assessment Report"], "organization": ["Asian Development Bank", "World Bank", "International Finance Corporation", "NGOs"], "acronym": ["FAS"], "year": ["2022", "2018-2021", "2023"], "geography": ["Southeast Asia", "Indonesia", "Thailand"]}, "relations": [{"has_organization": {"head": "Southeast Asia Economic Monitoring Report 2022", "tail": "Asian Development Bank"}}, {"used_by": {"head": "Southeast Asia Economic Monitoring Report 2022", "tail": "World Bank"}}, {"has_acronym": {"head": "Financial Access Survey", "tail": "FAS"}}, {"has_timeframe": {"head": "Financial Access Survey", "tail": "2018-2021"}}, {"has_geography": {"head": "Financial Access Survey", "tail": "Southeast Asia"}}, {"has_organization": {"head": "2023 Microfinance Sector Assessment Report", "tail": "International Finance Corporation"}}, {"used_by": {"head": "2023 Microfinance Sector Assessment Report", "tail": "NGOs"}}, {"has_geography": {"head": "2023 Microfinance Sector Assessment Report", "tail": "Southeast Asia"}}, {"has_geography": {"head": "2023 Microfinance Sector Assessment Report", "tail": "Indonesia"}}, {"has_geography": {"head": "2023 Microfinance Sector Assessment Report", "tail": "Thailand"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "The Education Access Survey (EAS) conducted by the Ministry of Education in 2022 provides critical insights into school enrollment patterns across the country. This dataset highlights significant trends in learning achievement among various demographics, informing policy decisions and resource allocation for future educational programs.", "output": {"entities": {"named_data": ["Education Access Survey"], "organization": ["Ministry of Education"], "acronym": ["EAS"], "year": ["2022"], "geography": ["country"]}, "relations": [{"has_organization": {"head": "Education Access Survey", "tail": "Ministry of Education"}}, {"has_acronym": {"head": "Education Access Survey", "tail": "EAS"}}, {"has_timeframe": {"head": "Education Access Survey", "tail": "2022"}}, {"has_geography": {"head": "Education Access Survey", "tail": "country"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The Global Gender Equity Survey (GGES) conducted in 2022 provides essential insights into women's participation in the labor force across various regions. This data, published by the International Institute for Women's Development (IIWD), has been instrumental for the United Nations Entity for Gender Equality and the Empowerment of Women (UN Women), which utilized the findings to inform policy recommendations. Furthermore, the Women’s Economic Empowerment Survey (WEES) 2021, released by the World Bank, also serves as a key resource for researchers aiming to assess economic disparities. UN Women frequently references the WEES data to advocate for gender-sensitive economic policies in developing nations.", "output": {"entities": {"named_data": ["Global Gender Equity Survey", "Women’s Economic Empowerment Survey"], "organization": ["International Institute for Women's Development", "UN Women", "World Bank"], "acronym": ["GGES", "WEES"], "year": ["2022", "2021"], "geography": ["developing nations"]}, "relations": [{"has_organization": {"head": "Global Gender Equity Survey", "tail": "International Institute for Women's Development"}}, {"used_by": {"head": "Global Gender Equity Survey", "tail": "UN Women"}}, {"has_acronym": {"head": "Global Gender Equity Survey", "tail": "GGES"}}, {"has_timeframe": {"head": "Global Gender Equity Survey", "tail": "2022"}}, {"has_organization": {"head": "Women’s Economic Empowerment Survey", "tail": "World Bank"}}, {"used_by": {"head": "Women’s Economic Empowerment Survey", "tail": "UN Women"}}, {"has_acronym": {"head": "Women’s Economic Empowerment Survey", "tail": "WEES"}}, {"has_timeframe": {"head": "Women’s Economic Empowerment Survey", "tail": "2021"}}, {"has_geography": {"head": "Women’s Economic Empowerment Survey", "tail": "developing nations"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
+{"input": "The recent findings on social protection initiatives draw heavily from the National Safety Nets Assessment Report (NSNAR) conducted in 2022, which provides comprehensive insights on the effectiveness of cash transfer programs across the nation. Additionally, the Global Social Protection Database (GSPD) covers the period from 2019 to 2023, offering a robust dataset for researchers and policymakers interested in evaluating social safety net programs in various countries. Furthermore, the 2020 Household Income Survey (HIS) has been made available, focusing on income distribution and poverty levels within urban regions of East Africa. These datasets are essential for informing policy decisions and ensuring the efficient allocation of resources to vulnerable populations.", "output": {"entities": {"named_data": ["National Safety Nets Assessment Report", "Global Social Protection Database", "Household Income Survey"], "organization": [], "acronym": ["NSNAR", "GSPD", "HIS"], "year": ["2022", "2019 to 2023", "2020"], "geography": ["East Africa"]}, "relations": [{"has_acronym": {"head": "National Safety Nets Assessment Report", "tail": "NSNAR"}}, {"has_timeframe": {"head": "National Safety Nets Assessment Report", "tail": "2022"}}, {"has_geography": {"head": "National Safety Nets Assessment Report", "tail": "East Africa"}}, {"has_acronym": {"head": "Global Social Protection Database", "tail": "GSPD"}}, {"has_timeframe": {"head": "Global Social Protection Database", "tail": "2019 to 2023"}}, {"has_acronym": {"head": "Household Income Survey", "tail": "HIS"}}, {"has_timeframe": {"head": "Household Income Survey", "tail": "2020"}}, {"has_geography": {"head": "Household Income Survey", "tail": "East Africa"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "social protection and safety nets"}}
+{"input": "The recent Poverty and Inequality Assessment Report 2023, published by the Economic Research Institute (ERI), highlights critical trends in economic disparity across various demographics. Data from this comprehensive report has been utilized by the United Nations Development Program (UNDP) to inform their policies in the region. The assessment framework includes a detailed analysis of poverty headcount ratios from 2018, providing vital insights into the socio-economic landscape of East Africa. For further reference, the dataset also includes international comparisons, which are crucial for understanding global poverty dynamics. Source: ERI elaboration based on Poverty and Inequality Assessment Report 2023.", "output": {"entities": {"named_data": ["Poverty and Inequality Assessment Report 2023"], "organization": ["Economic Research Institute", "United Nations Development Program"], "acronym": ["UNDP"], "year": ["2023", "2018"], "geography": ["East Africa"]}, "relations": [{"has_organization": {"head": "Poverty and Inequality Assessment Report 2023", "tail": "Economic Research Institute"}}, {"used_by": {"head": "Poverty and Inequality Assessment Report 2023", "tail": "United Nations Development Program"}}, {"has_acronym": {"head": "United Nations Development Program", "tail": "UNDP"}}, {"has_timeframe": {"head": "Poverty and Inequality Assessment Report 2023", "tail": "2023"}}, {"has_timeframe": {"head": "Poverty and Inequality Assessment Report 2023", "tail": "2018"}}, {"has_geography": {"head": "Poverty and Inequality Assessment Report 2023", "tail": "East Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "poverty headcount and inequality measurement"}}
+{"input": "and more immediate impact on food access, we are able to find additional empirical support\n\nfor the last explanation. Given data and information constraints, it is not possible to estimate\n\nSundberg, R., and E. Melander. 2013. \"Introducing the UCDP Georeferenced Event Dataset.\"\n\nNotes: This table compares mobile phone ownership in the November 2017 WFP mobile phone survey and the 2014 Household Budget Survey (HBS), where the 2014 HBS summary statistics are restricted to the share of the population that resides in a household that owns at least one mobile phone.\n\nWe gratefully thank Claudio Montenegro, David Newhouse and Minh Nguyen for their help with the I2D2 database. We gratefully acknowledge the generous support of the World Bank (Office of the Senior Vice-President and Chief Economist and Social Urban Rural and Resilience Global Practice), the Cities Program of the International Growth Center (Grant number 89408), the GWU Institute for International Economic Policy and the GWU Center for International Business Education and Research.\n\nData Used to Estimate the Returns**   **Sources.** The data source for the analysis is the _International_ _Income_ _Distribution_   _Database_ (I2D2) of the World Bank. The database consists of a large number of   individual-level surveys and census samples.", "output": {"entities": {"named_data": ["2014 Household Budget Survey"], "organization": ["World Bank", "GWU Institute for International Economic Policy"]}, "relations": [{"has_organization": {"head": "2014 Household Budget Survey", "tail": "World Bank"}}, {"used_by": {"head": "2014 Household Budget Survey", "tail": "GWU Institute for International Economic Policy"}}]}, "_meta": {"entry_id": 81, "dataset_name": "2014 Household Budget Survey"}}
+{"input": "The recent analysis highlights the need for enhanced policies to support women's economic empowerment. Drawing on insights from the Women's Economic Participation Survey, which gathered data on various aspects of employment and entrepreneurship among women, this report underscores the importance of targeted interventions. Additionally, it emphasizes the role of educational programs in bridging the skills gap for women in traditionally male-dominated industries.", "output": {"entities": {"named_data": ["Women's Economic Participation Survey"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "gender equality and women's economic empowerment"}}
+{"input": "The recent findings from the East Africa Trade Assessment Report indicate significant trends in trade dynamics across the region. The report was published by the African Development Bank, highlighting critical insights into industrial competitiveness. Various stakeholders, including local governments and trade associations, have begun to reference the report in their strategic planning sessions. This underscores the growing importance of data-driven insights in shaping policy and investment decisions in East Africa's economic landscape.", "output": {"entities": {"named_data": ["East Africa Trade Assessment Report"], "organization": ["African Development Bank", "local governments", "trade associations"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "East Africa Trade Assessment Report", "tail": "African Development Bank"}}, {"used_by": {"head": "East Africa Trade Assessment Report", "tail": "local governments"}}, {"used_by": {"head": "East Africa Trade Assessment Report", "tail": "trade associations"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "In recent years, the Economic Competitiveness Survey (ECS) conducted by the International Trade Organization (ITO) has provided critical insights into global trade dynamics. This dataset, covering the years 2020 to 2022, reveals important trends in market accessibility across various regions. The findings from the ECS have been extensively utilized by the Global Business Alliance (GBA) to inform their annual reports on trade policies. Furthermore, the ITO is currently developing an updated version of this dataset to include emerging markets, which is expected to be released in late 2023. By leveraging data from the ECS, the GBA aims to enhance its advocacy efforts in promoting fair trade practices worldwide.", "output": {"entities": {"named_data": ["Economic Competitiveness Survey", "ECS"], "organization": ["International Trade Organization", "ITO", "Global Business Alliance", "GBA"], "acronym": ["ECS"], "year": ["2020 to 2022", "2023"], "geography": ["global", "emerging markets"]}, "relations": [{"has_organization": {"head": "Economic Competitiveness Survey", "tail": "International Trade Organization"}}, {"used_by": {"head": "Economic Competitiveness Survey", "tail": "Global Business Alliance"}}, {"has_timeframe": {"head": "Economic Competitiveness Survey", "tail": "2020 to 2022"}}, {"has_acronym": {"head": "Economic Competitiveness Survey", "tail": "ECS"}}, {"has_timeframe": {"head": "Economic Competitiveness Survey", "tail": "2023"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The Food and Agriculture Organization (FAO) has released the Global Agricultural Monitoring Survey, which provides critical insights into agricultural trends and food security across member countries. FAO's commitment to enhancing the understanding of food systems is evident through this comprehensive dataset, which has been widely referenced by a variety of international organizations in their efforts to analyze and improve global agricultural practices.", "output": {"entities": {"named_data": ["Global Agricultural Monitoring Survey"], "organization": ["Food and Agriculture Organization", "FAO"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Global Agricultural Monitoring Survey", "tail": "Food and Agriculture Organization"}}, {"used_by": {"head": "Global Agricultural Monitoring Survey", "tail": "FAO"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "agriculture, food security, and nutrition"}}
+{"input": "Of all countries in the World Bank's WDI, only 60%\n(40% of the analyzed countries) had an inflation figure for the most recent 2019-2020 period. The IFS\ndata reported similarly on only half of the countries. This was last checked on August 31, 2021; WDI\ndata identifier FP.CPI.TOTL.ZG, and IFS data identifier PCPI ~~P~~ C ~~P~~ P ~~P~~ T.\n\nThe paper highlights the new price monitoring capabilities using surveys from the World Food Programme (WFP) gathered in 25 fragile and conflict-affected countries.\n\nSubnational food prices have been surveyed in many countries for years by\nhumanitarians to inform their country operations. Well-known data bases are\nthose from the WFP, FEWS NET and the Food and Agricultural Organization\n(FAO). [6] The paper focuses on raw monthly data from the WFP, but parts of the\ndiscussion, and particularly the methods developed here, could apply to similar\ndata sets. [7]\n\nThe paper gathered all end-of-August data available from the WFP Vulnerability Analysis and Mapping (VAM) unit as of September 21, 2021.", "output": {"entities": {"named_data": ["WFP Vulnerability Analysis and Mapping (VAM) unit"], "organization": ["World Food Programme", "the paper"]}, "relations": [{"has_organization": {"head": "WFP Vulnerability Analysis and Mapping (VAM) unit", "tail": "World Food Programme"}}, {"used_by": {"head": "WFP Vulnerability Analysis and Mapping (VAM) unit", "tail": "the paper"}}]}, "_meta": {"entry_id": 511, "dataset_name": "WFP Vulnerability Analysis and Mapping (VAM) unit"}}
+{"input": "nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016).", "output": {"entities": {"named_data": ["Living Standards Measurement Study"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "Living Standards Measurement Study", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 1059, "dataset_name": "Living Standards Measurement Study"}}
+{"input": "The 2022 Global Fertility Trends Report (GFTR) provides crucial insights into demographic shifts across various countries. Utilizing data from the recent Population Dynamics Assessment 2021, researchers have highlighted significant changes in fertility rates, particularly in Southeast Asia. The report, published by the International Fertility Organization, emphasizes the importance of timely data in understanding the implications of these changes. Additionally, the Demographic and Health Survey (DHS) for Kenya, conducted in 2020, offers localized data that further supports the findings from the Global Fertility Trends Report. This survey has been instrumental for policymakers aiming to enhance reproductive health services in the region. \n\nSource: International Fertility Organization elaboration based on Population Dynamics Assessment 2021 and DHS for Kenya.", "output": {"entities": {"named_data": ["Global Fertility Trends Report", "Population Dynamics Assessment 2021", "Demographic and Health Survey"], "organization": ["International Fertility Organization"], "acronym": ["GFTR", "DHS"], "year": ["2022", "2021", "2020"], "geography": ["Kenya", "Southeast Asia"]}, "relations": [{"has_acronym": {"head": "Global Fertility Trends Report", "tail": "GFTR"}}, {"has_timeframe": {"head": "Global Fertility Trends Report", "tail": "2022"}}, {"has_geography": {"head": "Global Fertility Trends Report", "tail": "Southeast Asia"}}, {"has_acronym": {"head": "Demographic and Health Survey", "tail": "DHS"}}, {"has_timeframe": {"head": "Demographic and Health Survey", "tail": "2020"}}, {"has_geography": {"head": "Demographic and Health Survey", "tail": "Kenya"}}, {"has_timeframe": {"head": "Population Dynamics Assessment 2021", "tail": "2021"}}, {"used_by": {"head": "Population Dynamics Assessment 2021", "tail": "International Fertility Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "demographics, fertility, and population growth"}}
+{"input": "**Fondazione ISMU**\n\nthe age of 16) and young migrants (up to 22 years of age). Subsequently, Percorso II funded 850 work integration pathways followed by a third phase, which, with a new notice, provides for the financing of a further 170 pathways.\nThese pathways are supported by the Protezione Unità a Obiettivo Integrazione (PUOI, Protection Unit for Integration) project for 4,500 additional beneficiaries, currently in the start-up phase. See https://www.anpalservizi.it/home 49 With respect to the traineeships carried out under Percorso I, “the entire pathway and, in particular, the traineeship experience is generally considered positive by the recipients, since they allow an improvement in living conditions, not only in economic terms; they increase the technical-professional knowledge and skills” (Report of Evaluation of the Percorso Action I by MLSP-Anpal services, 2017, p. 29 et seq.).\n\n50 For further information on these forms of semi-independence, see the Lombardy case study in chapter 7.\n\n51 Family foster care is one of the forms of alternative care provided for by the United Nations Guidelines on Alternative Care for Children, but it is not limited to this. The Guidelines aim to improve the implementation of the Convention on the Rights of the Child on the protection and welfare of children without parental care, or at risk of losing it, defining the desirable guidelines for policy.\n\n52 For further details, see: Youth, ExCom 66th meeting, 31 May 2016, www.refworld.org/pdfid/5800cdea4.pdf 53 The data that emerged from the research were not clear as to whether these procedures are mainly determined by the lack of requests to initiate these procedures at the same time, or rather, as a legal procedure of the individual police stations.\n\n54 The difficulties faced in Sicily in particular have also been evidenced by recent studies (Save the Children, 2017– 2019), where it emerged that reception facilities sometimes struggle to cope with a lack of resources to ensure logistics in transporting UASC to schools or CPIAs. Another problem arises with the transfer from one facility to another (from the first to the second level of reception, upon turning 18) in which school enrolment is not always guaranteed or the attended hours recognized.\n\n55 For an overview of the phenomenon, see WRC (2019).\n\n56 ‘Agency’ refers to the ability of an agent (natural person or other subject) to act independently in the world and to make free choices (Giddens, 1992).\n\n57 See the definition in the Glossary.\n\n58 For further information on the issue of social housing, see: Ferri et al. 2017.\n\n59 Bourdieu and Wacquant (1992) define social capital as “the sum of the resources, both real and virtual, that come from an individual or a group through a solid network of relationships, partly institutionalized, of mutual acquaintance and identification”. For further information on the concepts of formal and informal relations in the field of migration, see Ambrosini (2014: 171–174; idem 2011).\n\n60 The central idea of this theatre methodology, conceived over 50 years ago in Brazil by the pedagogue Paulo Freire, is to use the language of drama to understand and transform the daily oppressive, realities, small and large, towards liberation. This theatre method has also been adopted by Matemù and other meeting centres in Rome, such as Laboratorio 53 and Asinitas.\n\n61 The institutional working group was composed of: the Civil Court of Palermo, the Guardianship Judge, the Prosecutor's Office at the Juvenile Court of Palermo, the Police Headquarters of Palermo, the University of Palermo, the Provincial Health Authority of Palermo, and the Regional Education Authority for Sicily.\n\n62 The cartella sociale, or social file, records information related to the reception and social inclusion path of UASC in Italy. It is included in the national database of the Ministry of Labour and Social Policies.\n\n63 Council of Europe, Ministers’ Deputies, CM/Rec(2019)4, Recommendation CM/Rec(2019)4 of the Committee of Ministers to member States on supporting young refugees in transition to adulthood.\n\n64 https://data.unicef.org/topic/adolescents/overview/0/ 65 https://www.unicef.org/sites/default/files/2019-04/UN-Convention-Rights-Child-text.pdf 66 Ibid 67 https://gbvguidelines.org/wp/wp-content/uploads/2015/09/2015-IASC-Gender-based-Violence-Guidelines_lo-res.pdf 68 www.unhcr.org/sexual-and-gender-based-violence.html<www.unhcr.org/sexual-and-gender-based-violence.html> 69 https://www.unicef.org/sites/default/files/2019-04/UN-Convention-Rights-Child-text.pdf 70 (www.gazzettaufficiale.it/eli/id/2017/04/21/17G00062/sg) 71 www.europarl.europa.eu/RegData/etudes/BRIE/2017/608728/EPRS_BRI(2017)608728_EN.pdf 72 (https://www.camera.it/parlam/leggi/deleghe/98286dl.htm) 73 (https://fra.europa.eu/sites/default/files/fra_uploads/fra-2018-it-guardianship-legal-update_en.pdf); www gazzettaufficiale.it/eli/id/2017/04/21/17G00062/sg)(www.gazzettaufficiale.it/eli/id/2017/04/21/17G00062/sg.\n\nIxxv https://www.garanteinfanzia.org/sites/default/files/linee_guida_per_il_diritto_allo_studio_delle_alunne_e_degli_ alunni_fuori_dalla_famiglia_di_origine.pdf Ixxvi See: www.miur.gov.it 92", "output": {"entities": {"named_data": ["cartella sociale"], "organization": ["Ministry of Labour and Social Policies", "Fondazione ISMU"]}, "relations": [{"has_organization": {"head": "cartella sociale", "tail": "Ministry of Labour and Social Policies"}}, {"used_by": {"head": "cartella sociale", "tail": "Fondazione ISMU"}}]}, "_meta": {"entry_id": 1396, "dataset_name": "cartella sociale"}}
+{"input": "The African Energy Access Database (AEAD) provides comprehensive data on energy consumption across various regions in Africa, enabling stakeholders to assess progress towards universal energy access by 2030. Published by the African Development Agency (ADA), this dataset serves as a critical resource for policymakers and researchers. In particular, the recent Energy Transition Assessment Report 2022, used by the International Renewable Energy Council (IREC), analyzes trends in renewable energy adoption based on the data from AEAD. The assessment highlights significant disparities in energy access between urban and rural areas within countries like Nigeria and Kenya, underscoring the urgent need for targeted interventions. \n\nSource: ADA elaboration based on AEAD.", "output": {"entities": {"named_data": ["African Energy Access Database", "Energy Transition Assessment Report 2022"], "organization": ["African Development Agency", "International Renewable Energy Council"], "acronym": ["AEAD", "IREC"], "year": ["2030", "2022"], "geography": ["Africa", "Nigeria", "Kenya"]}, "relations": [{"has_organization": {"head": "African Energy Access Database", "tail": "African Development Agency"}}, {"used_by": {"head": "African Energy Access Database", "tail": "International Renewable Energy Council"}}, {"has_acronym": {"head": "African Energy Access Database", "tail": "AEAD"}}, {"has_timeframe": {"head": "Energy Transition Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "African Energy Access Database", "tail": "2030"}}, {"has_geography": {"head": "African Energy Access Database", "tail": "Africa"}}, {"has_geography": {"head": "Energy Transition Assessment Report 2022", "tail": "Nigeria"}}, {"has_geography": {"head": "Energy Transition Assessment Report 2022", "tail": "Kenya"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "energy access and renewable transitions"}}
+{"input": "the intensity of conflicts, and iv) population density in refugee host countries by region. Appendix B presents the list of variables along with description and summary statistics. 5. Empirical strategy To estimate the impact of refugee inflow19 on host community ’ s livelihood strategy choice, we use the following basic econometric model: 𝑌𝑌𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑅𝑅𝑅𝑅𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝜈𝜈 + 𝜀𝜀𝑖𝑖 (1) Where, 𝑖𝑖 indexes a household, 𝑌𝑌𝑖𝑖 is an outcome variable of interest (livelihood diversification or commercialization of agriculture), 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 is the measure of refugee inflow, i. e., the refugee population (average of 2017 and 2018) in the nearest refugee camp weighted by the inverse of distance of the household to the refugee camps, 𝑋𝑋𝑖𝑖 is a set of household controls, 𝜈𝜈 is kebele fixed effects, and 𝜀𝜀𝑖𝑖 is the error term. Several variables, from the DRDIP data set, were used as controls in our model.", "output": {"entities": {"named_data": ["DRDIP data set"], "organization": ["our"]}, "relations": [{"used_by": {"head": "DRDIP data set", "tail": "our"}}]}, "_meta": {"entry_id": 119, "dataset_name": "DRDIP data set"}}
+{"input": "Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Predicted total expenditure Predicted poverty rate Figure 5.16: Poverty incidence decreases with education of the household head Source: World Bank Staff based on SESRE 2023. Refugees’ Aspirations 52 A larger proportion of refugees work inside the camp across the distribution. Yet, better-off refugees are more likely to work inside the camp. Regarding location, although working outside the camp is shown to have significant wage effects (see Chapter 3), the data show that the poorest in-camp refugees are more likely to work outside the camp than the richest (Figure 5.18d), an effect apparently driven by refugees from South Sudan and Somalia, who are poorer overall. Regression results show that an increase in the share of employed household members is associated with increased household expenditure for in-camp refugees, their hosts, and out-of-camp refugees (Table D.12 in Annex D). The predicted poverty rate decreases with the share of employed household members, indicating that employment is essential to lowering poverty for in- camp refugees (Figure 5.19). 0 10 20 30 40 50 60 70 80 90 100 Poorest", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank", "World Bank Staff"]}, "relations": [{"has_organization": {"head": "SESRE 2023", "tail": "World Bank"}}, {"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 175, "dataset_name": "SESRE 2023"}}
+{"input": "The Water Access Survey 2022, conducted by the Global Water Initiative, provides essential insights into the state of water supply across rural regions of Sub-Saharan Africa. Notably, this dataset is utilized by the World Health Organization for their annual health reports. The survey includes information on access to clean water and sanitation facilities, playing a crucial role in policy-making decisions. Similarly, the Sanitation and Hygiene Assessment (SHA) provides a comprehensive overview of sanitation conditions from 2019 to 2021. The SHA data is produced by the International Hygiene Coalition and is frequently referenced by UNICEF in its efforts to improve sanitation across various countries. Both datasets are vital for understanding the link between water access and public health outcomes in the region.", "output": {"entities": {"named_data": ["Water Access Survey 2022", "Sanitation and Hygiene Assessment"], "organization": ["Global Water Initiative", "World Health Organization", "International Hygiene Coalition", "UNICEF"], "acronym": ["SHA"], "year": ["2022", "2019 to 2021"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Water Access Survey 2022", "tail": "Global Water Initiative"}}, {"used_by": {"head": "Water Access Survey 2022", "tail": "World Health Organization"}}, {"has_timeframe": {"head": "Sanitation and Hygiene Assessment", "tail": "2019 to 2021"}}, {"has_organization": {"head": "Sanitation and Hygiene Assessment", "tail": "International Hygiene Coalition"}}, {"used_by": {"head": "Sanitation and Hygiene Assessment", "tail": "UNICEF"}}, {"has_geography": {"head": "Water Access Survey 2022", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "water, sanitation, and hygiene"}}
+{"input": "schooling decisions, occupational choice, and savings (Cobb-Clark et al., 2016; Heckman et al., 2006). In Ethiopia, higher LOC has been shown to predict farmer adoption of modern agricultural technologies (Taffesse and Tadesse, 2017). In refugee populations, low LOC also correlate with depression, anxiety, and psychological distress (Schlechter et al., 2023; Tsionis et al., 2022). Higher LOC has also been found to improve employment and socio-economic integration among immigrants and refugees in Germany (Hahn et al., 2019; Thum, 2014). Compared to hosts, South Sudanese refugees perceive less personal control over their lives and destinies. Based on SESRE data, the index used to construct a measure of personal control over one’s life is an unweighted average of 10 LOC- related questions. The index (Likert scale) ranges from 1—”little control over one’s life”—to 4—”more control over one’s life”. The index is 0.12 points (.25 standard deviations) lower for refugees than hosts indicating they feel they have lower control over their lives and destinies, and this difference is statistically significant. This difference, however, is driven by South Sudanese refugees. When comparing LOC by country of origin, we find that there is only for South Sudanese there is a significant difference between refugees and hosts", "output": {"entities": {"named_data": ["SESRE data"], "organization": ["Hahn et al."]}, "relations": [{"used_by": {"head": "SESRE data", "tail": "Hahn et al."}}]}, "_meta": {"entry_id": 484, "dataset_name": "SESRE data"}}
+{"input": "The Global Financial Inclusion Index (GFII) reveals critical insights into the accessibility of financial services across various regions. The data encompasses the years 2018 to 2022, highlighting trends in the financial behaviors of households in sub-Saharan Africa. Additionally, the Economic Trends Assessment Report 2021 provides a comprehensive overview of macroeconomic changes and their impact on growth in East Asia. This assessment is informed by the GFII dataset and has been used by various financial institutions to formulate policies aimed at improving financial access.", "output": {"entities": {"named_data": ["Global Financial Inclusion Index", "Economic Trends Assessment Report 2021"], "organization": ["financial institutions"], "acronym": ["GFII"], "year": ["2018 to 2022", "2021"], "geography": ["sub-Saharan Africa", "East Asia"]}, "relations": [{"has_acronym": {"head": "Global Financial Inclusion Index", "tail": "GFII"}}, {"has_timeframe": {"head": "Global Financial Inclusion Index", "tail": "2018 to 2022"}}, {"has_geography": {"head": "Global Financial Inclusion Index", "tail": "sub-Saharan Africa"}}, {"has_timeframe": {"head": "Economic Trends Assessment Report 2021", "tail": "2021"}}, {"has_geography": {"head": "Economic Trends Assessment Report 2021", "tail": "East Asia"}}, {"used_by": {"head": "Economic Trends Assessment Report 2021", "tail": "financial institutions"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "The 2022 Refugee Insights Report, published by the Global Migration Organization (GMO), provides a comprehensive analysis of forced displacement trends worldwide. This report, using data collected from various regional assessments, has been instrumental for agencies like the United Nations High Commissioner for Refugees (UNHCR) in strategizing their response to the refugee crisis. Additionally, the South Asia Migration Survey (SAMS) 2021, conducted by the Regional Migration Authority (RMA), offers critical insights specific to migration patterns in South Asia. Both datasets are crucial for policymakers and humanitarian organizations working to address the needs of displaced populations.", "output": {"entities": {"named_data": ["2022 Refugee Insights Report", "South Asia Migration Survey"], "organization": ["Global Migration Organization", "United Nations High Commissioner for Refugees", "Regional Migration Authority"], "acronym": [], "year": ["2022", "2021"], "geography": ["worldwide", "South Asia"]}, "relations": [{"has_organization": {"head": "2022 Refugee Insights Report", "tail": "Global Migration Organization"}}, {"used_by": {"head": "2022 Refugee Insights Report", "tail": "United Nations High Commissioner for Refugees"}}, {"has_timeframe": {"head": "2022 Refugee Insights Report", "tail": "2022"}}, {"has_geography": {"head": "2022 Refugee Insights Report", "tail": "worldwide"}}, {"has_organization": {"head": "South Asia Migration Survey", "tail": "Regional Migration Authority"}}, {"has_timeframe": {"head": "South Asia Migration Survey", "tail": "2021"}}, {"has_geography": {"head": "South Asia Migration Survey", "tail": "South Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "forced displacement, refugees, and migration"}}
+{"input": "The Global Trade Assessment Report 2022 provides valuable insights into trade dynamics and economic competitiveness for various countries. This report, published by the International Trade Organization, highlights trends and policy implications that can significantly impact international trade relations.", "output": {"entities": {"named_data": ["Global Trade Assessment Report 2022"], "organization": ["International Trade Organization"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Global Trade Assessment Report 2022", "tail": "International Trade Organization"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The Climate Impact Assessment Report 2022, published by the International Climate Initiative (IKI), provides critical insights into the effects of climate change on vulnerable populations. This report emphasizes the need for enhanced resilience strategies to mitigate disaster risks and adapt to ongoing environmental changes. The findings are expected to guide policy decisions and are being utilized by various stakeholders across the globe, particularly in regions most affected by climate-related disasters.", "output": {"entities": {"named_data": ["Climate Impact Assessment Report 2022"], "organization": ["International Climate Initiative (IKI)"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Climate Impact Assessment Report 2022", "tail": "International Climate Initiative (IKI)"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**Unit of** **Data Source /**\n**Indicator Name** **Corporate** **End Target** **Frequency**\n**Measure** **[Baseline ]** **Methodology**\n\nthree months after civic engagement training.\n\nPercentage of beneficiaries taking a more active role in their communities disabled Percentag e 0.00 50.00 Twice, once before civic engagement training and again at least three months after civic engagement training completion.\n\nFor participants in Component 1 and 2 of the project, the Baseline Survey and Post-Training Completion Survey will be used for data collection.\nFor individuals trained only as part of Component 3, a separate pre-training survey will be conducted as well as a follow-up survey conducted at least three months after civic engagement training.\n\n**Responsibility for**\n**Data Collection**\n\nM&E Specialist Description: Total number of individuals trained in civic engagement topics who report a higher level of community activity compared to the level reported before receiving training and support.\n\n**Intermediate Results Indicators**\n\n**Responsibility for**\n**Data Collection**\n\nPage 27 of 34\n\n**Indicator Name** **Corporate**\n\n**Unit of**\n**Measur**\n**e**\n\n**Data Source /**\n**Baseline** **End Target** **Frequency**\n**Methodology**", "output": {"entities": {"named_data": ["Baseline Survey and Post-Training Completion Survey"], "organization": ["The World Bank"]}, "relations": [{"has_organization": {"head": "Baseline Survey and Post-Training Completion Survey", "tail": "The World Bank"}}]}, "_meta": {"entry_id": 833, "dataset_name": "Baseline Survey and Post-Training Completion Survey"}}
+{"input": "The Digital Connectivity Assessment 2022, published by the International Telecommunication Union (ITU), provides critical insights into internet accessibility across low-income countries. This data set is being extensively used by the United Nations Development Programme (UNDP) to analyze the progress of global digital inclusion initiatives. In addition, the Mobile Technology Adoption Study (MTAS) conducted by GSMA in 2021 highlights mobile technology trends in East Africa, which has been cited by various NGOs for policy formulation. Furthermore, the African Internet Usage Report 2023, produced by the African Union, offers a comprehensive overview of internet engagement in the region, particularly utilized by regional governments to shape their digital strategies.", "output": {"entities": {"named_data": ["Digital Connectivity Assessment 2022", "Mobile Technology Adoption Study", "African Internet Usage Report 2023"], "organization": ["International Telecommunication Union", "United Nations Development Programme", "GSMA", "African Union"], "acronym": ["MTAS"], "year": ["2022", "2021", "2023"], "geography": ["low-income countries", "East Africa", "Africa"]}, "relations": [{"has_organization": {"head": "Digital Connectivity Assessment 2022", "tail": "International Telecommunication Union"}}, {"used_by": {"head": "Digital Connectivity Assessment 2022", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Mobile Technology Adoption Study", "tail": "MTAS"}}, {"has_timeframe": {"head": "Mobile Technology Adoption Study", "tail": "2021"}}, {"has_geography": {"head": "Mobile Technology Adoption Study", "tail": "East Africa"}}, {"has_organization": {"head": "African Internet Usage Report 2023", "tail": "African Union"}}, {"used_by": {"head": "African Internet Usage Report 2023", "tail": "regional governments"}}, {"has_timeframe": {"head": "African Internet Usage Report 2023", "tail": "2023"}}, {"has_geography": {"head": "African Internet Usage Report 2023", "tail": "Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "digital development and technology adoption"}}
+{"input": "The Climate Resilience Assessment Report 2022 provides vital insights into the adaptive capabilities of vulnerable communities in the Pacific Islands. This dataset, produced by the Regional Disaster Risk Management Agency, serves as a critical resource for policymakers and researchers seeking to enhance disaster preparedness in the region.", "output": {"entities": {"named_data": ["Climate Resilience Assessment Report 2022"], "organization": ["Regional Disaster Risk Management Agency"], "acronym": [], "year": ["2022"], "geography": ["Pacific Islands"]}, "relations": [{"has_organization": {"head": "Climate Resilience Assessment Report 2022", "tail": "Regional Disaster Risk Management Agency"}}, {"has_timeframe": {"head": "Climate Resilience Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Climate Resilience Assessment Report 2022", "tail": "Pacific Islands"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014.", "output": {"entities": {"named_data": ["Displaced People Survey"], "organization": ["Listening to Displaced People Survey"]}, "relations": [{"has_organization": {"head": "Displaced People Survey", "tail": "Listening to Displaced People Survey"}}]}, "_meta": {"entry_id": 255, "dataset_name": "Displaced People Survey"}}
+{"input": "**average or gross earnings.** The median\nnet earnings of Ukrainian refugees in Q2 2024 were PLN 4,000 in SEIS, and PLN 3,767 in NBP (2024) surveys. This is 84% and 79% of the national median, respectively. This estimate would be most likely lower, if data allowed us to look at average or gross earnings. First, a comparison of average instead of median earnings would show a larger gap between Ukrainian refugees and all workers in the economy, as the national average is inflated by top incomes, which are less often earned by Ukrainian refugees than", "output": {"entities": {"named_data": ["NBP (2024) surveys"], "organization": ["NBP"]}, "relations": [{"has_organization": {"head": "NBP (2024) surveys", "tail": "NBP"}}]}, "_meta": {"entry_id": 1355, "dataset_name": "NBP (2024) surveys"}}
+{"input": "The South Asian Economic Vulnerability Assessment (SAEVA) published by the Asian Development Bank in 2022 provides valuable insights into the socioeconomic challenges faced by the region. The report has been extensively used by various NGOs, including the International Rescue Committee, to shape their poverty alleviation strategies. Furthermore, data from the 2021 Regional Social Protection Analysis (RSPA) has been integrated into the policy frameworks of several local governments, aiding them in improving their safety nets and support systems. Both datasets highlight the pressing need for evidence-based interventions in underrepresented communities across South Asia.", "output": {"entities": {"named_data": ["South Asian Economic Vulnerability Assessment", "Regional Social Protection Analysis"], "organization": ["Asian Development Bank", "International Rescue Committee"], "acronym": ["SAEVA", "RSPA"], "year": ["2022", "2021"], "geography": ["South Asia"]}, "relations": [{"has_organization": {"head": "South Asian Economic Vulnerability Assessment", "tail": "Asian Development Bank"}}, {"used_by": {"head": "South Asian Economic Vulnerability Assessment", "tail": "International Rescue Committee"}}, {"has_acronym": {"head": "South Asian Economic Vulnerability Assessment", "tail": "SAEVA"}}, {"has_timeframe": {"head": "South Asian Economic Vulnerability Assessment", "tail": "2022"}}, {"has_geography": {"head": "South Asian Economic Vulnerability Assessment", "tail": "South Asia"}}, {"has_organization": {"head": "Regional Social Protection Analysis", "tail": "International Rescue Committee"}}, {"has_acronym": {"head": "Regional Social Protection Analysis", "tail": "RSPA"}}, {"has_timeframe": {"head": "Regional Social Protection Analysis", "tail": "2021"}}, {"has_geography": {"head": "Regional Social Protection Analysis", "tail": "South Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "social protection and safety nets"}}
+{"input": "The recent analysis of deforestation patterns in the Amazon Rainforest utilized data from the Brazilian Land Use Change Database (BLUCD) from 2018 to 2020. This comprehensive dataset provides critical insights into land cover transformations, which were further explored by researchers from the Environmental Research Institute. The Global Land Monitoring Survey (GLMS) conducted in 2021 adds another layer of detail, specifically focusing on satellite imagery and its application in urban planning across South America. Notably, the spatial datasets contribute significantly to our understanding of environmental shifts, though some researchers have raised concerns about data accuracy in certain regions.", "output": {"entities": {"named_data": ["Brazilian Land Use Change Database", "Global Land Monitoring Survey"], "organization": ["Environmental Research Institute"], "acronym": ["BLUCD", "GLMS"], "year": ["2018 to 2020", "2021"], "geography": ["Amazon Rainforest", "South America"]}, "relations": [{"has_acronym": {"head": "Brazilian Land Use Change Database", "tail": "BLUCD"}}, {"has_timeframe": {"head": "Brazilian Land Use Change Database", "tail": "2018 to 2020"}}, {"has_geography": {"head": "Brazilian Land Use Change Database", "tail": "Amazon Rainforest"}}, {"has_acronym": {"head": "Global Land Monitoring Survey", "tail": "GLMS"}}, {"has_timeframe": {"head": "Global Land Monitoring Survey", "tail": "2021"}}, {"has_geography": {"head": "Global Land Monitoring Survey", "tail": "South America"}}, {"used_by": {"head": "Global Land Monitoring Survey", "tail": "Environmental Research Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "The Renewable Energy Access Survey (REAS) aims to provide insights into the energy consumption patterns across various regions, focusing on the years 2020 to 2023. This comprehensive dataset covers 15 countries in Sub-Saharan Africa, enabling researchers and policymakers to assess the effectiveness of energy transition strategies. Source: elaborations based on the Renewable Energy Access Survey. However, while the survey provides valuable data, it does not capture all regions, leaving out significant areas like North Africa.", "output": {"entities": {"named_data": ["Renewable Energy Access Survey"], "organization": [], "acronym": ["REAS"], "year": ["2020 to 2023"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Renewable Energy Access Survey", "tail": "REAS"}}, {"has_timeframe": {"head": "Renewable Energy Access Survey", "tail": "2020 to 2023"}}, {"has_geography": {"head": "Renewable Energy Access Survey", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}
+{"input": "The 2022 Poverty Assessment Report, published by the Ministry of Economic Development in Zambia, provides comprehensive insights into the national poverty headcount. This dataset, which outlines various dimensions of poverty from 2017 to 2022, has been instrumental for researchers, including the African Development Bank, who utilized it in their recent studies on regional inequality. The report, also referred to as the PAR 2022, is critical for understanding socio-economic disparities across different districts in Zambia, making it a valuable resource for policy makers and development practitioners alike.", "output": {"entities": {"named_data": ["Poverty Assessment Report", "PAR 2022"], "organization": ["Ministry of Economic Development", "African Development Bank"], "acronym": ["PAR"], "year": ["2022", "2017 to 2022"], "geography": ["Zambia"]}, "relations": [{"has_organization": {"head": "Poverty Assessment Report", "tail": "Ministry of Economic Development"}}, {"used_by": {"head": "Poverty Assessment Report", "tail": "African Development Bank"}}, {"has_acronym": {"head": "Poverty Assessment Report", "tail": "PAR"}}, {"has_timeframe": {"head": "Poverty Assessment Report", "tail": "2017 to 2022"}}, {"has_geography": {"head": "Poverty Assessment Report", "tail": "Zambia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "poverty headcount and inequality measurement"}}
+{"input": "The recent study highlights key insights into macroeconomic trends affecting financial inclusion across various regions. Notably, the Global Financial Inclusion Report and the Economic Development Tracker offer a comprehensive overview of the barriers faced by underserved populations. Understanding these dynamics is essential for policymakers to design effective strategies that promote access to financial services.", "output": {"entities": {"named_data": ["Global Financial Inclusion Report", "Economic Development Tracker"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "The recently released Kenya Demographic and Health Survey 2022 (KDHS 2022) provides invaluable insights into the reproductive health trends within the country. This dataset, produced by the Kenya National Bureau of Statistics, is extensively used by organizations such as UNFPA and WHO to inform health policies aimed at improving maternal health. Additionally, the Zambia Fertility and Family Planning Assessment Report 2021 is another critical resource produced by the Zambia Statistics Agency, which is utilized by various NGOs working to enhance family planning services across the region. Both datasets highlight significant issues related to fertility rates and population growth, underscoring the need for effective intervention strategies in these areas.", "output": {"entities": {"named_data": ["Kenya Demographic and Health Survey 2022", "Zambia Fertility and Family Planning Assessment Report 2021"], "organization": ["Kenya National Bureau of Statistics", "UNFPA", "WHO", "Zambia Statistics Agency"], "acronym": ["KDHS 2022"], "year": ["2022", "2021"], "geography": ["Kenya", "Zambia"]}, "relations": [{"has_organization": {"head": "Kenya Demographic and Health Survey 2022", "tail": "Kenya National Bureau of Statistics"}}, {"used_by": {"head": "Kenya Demographic and Health Survey 2022", "tail": "UNFPA"}}, {"used_by": {"head": "Kenya Demographic and Health Survey 2022", "tail": "WHO"}}, {"has_organization": {"head": "Zambia Fertility and Family Planning Assessment Report 2021", "tail": "Zambia Statistics Agency"}}, {"used_by": {"head": "Zambia Fertility and Family Planning Assessment Report 2021", "tail": "NGOs"}}, {"has_acronym": {"head": "Kenya Demographic and Health Survey 2022", "tail": "KDHS 2022"}}, {"has_timeframe": {"head": "Kenya Demographic and Health Survey 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Zambia Fertility and Family Planning Assessment Report 2021", "tail": "2021"}}, {"has_geography": {"head": "Kenya Demographic and Health Survey 2022", "tail": "Kenya"}}, {"has_geography": {"head": "Zambia Fertility and Family Planning Assessment Report 2021", "tail": "Zambia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "demographics, fertility, and population growth"}}
+{"input": "The recent analysis of urban transportation challenges highlights the significance of the Urban Infrastructure Assessment Report. This report provides valuable insights into the current state of public transport systems and their impact on urban mobility. Key findings emphasize the need for improved connectivity and infrastructure investment to meet the growing demands of urban populations.", "output": {"entities": {"named_data": ["Urban Infrastructure Assessment Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "urban infrastructure and transportation planning"}}
+{"input": "The 2023 Digital Usage Report published by the Tech Insights Agency provides comprehensive data on technology adoption trends across various demographics in Brazil. This dataset, known for its detailed analysis, has been utilized by the Brazilian Ministry of Communication to inform policy decisions aimed at enhancing digital infrastructure in underserved regions. In parallel, the Global Connectivity Assessment (GCA) conducted by the International Data Forum sheds light on global internet accessibility and is cited by numerous organizations, including the United Nations Development Programme (UNDP) for assessing progress in sustainable development goals across Africa. These datasets not only reflect the current state of digital development but also influence strategic initiatives by governments and NGOs worldwide.", "output": {"entities": {"named_data": ["2023 Digital Usage Report", "Global Connectivity Assessment"], "organization": ["Tech Insights Agency", "Brazilian Ministry of Communication", "International Data Forum", "United Nations Development Programme"], "acronym": [], "year": ["2023"], "geography": ["Brazil", "Africa"]}, "relations": [{"has_organization": {"head": "2023 Digital Usage Report", "tail": "Tech Insights Agency"}}, {"used_by": {"head": "2023 Digital Usage Report", "tail": "Brazilian Ministry of Communication"}}, {"has_geography": {"head": "2023 Digital Usage Report", "tail": "Brazil"}}, {"has_organization": {"head": "Global Connectivity Assessment", "tail": "International Data Forum"}}, {"used_by": {"head": "Global Connectivity Assessment", "tail": "United Nations Development Programme"}}, {"has_geography": {"head": "Global Connectivity Assessment", "tail": "Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "digital development and technology adoption"}}
+{"input": "observed in PISA data. Looking at graduates of pre-university technical education (mainly technical stream secondary education) one finds an interesting pattern comparing three specializations (Figure 1b). Gender disparities are high in Electronics and Automation, less in Software Development and were recently eliminated in Network and Database Design. The final example of Network and Database Design in Figure 1b shows that gender disparity can be overcome in a short period of time. Two recent studies provide a detailed general analysis of policy options. [18] For STEAM courses, the causes relate to: (i) enjoyment of reading and preference for mathematics; (ii) self-efficacy or belief in own capabilities, often reinforced by teachers; (iii) parental support or lack thereof due to low socioeconomic status; (iv) teacher preparation to deal effectively with diversity. 16 Source: Government Budget for 2024 and MEP School Census data from 2023. 17 Program-Based budgeting at a government-wide level is being implemented under the Fiscal Management Improvement Project (P172352, Loan 9075-CR), known as _Hacienda Digital_ . Investing in readiness to implement program-based budgeting at MEP, the biggest Ministry (in terms of budget and staff) is a priority for the Government of Costa Rica. 18 [Desigualdades por género en Primaria y Secundaria,", "output": {"entities": {"named_data": ["MEP School Census data"], "organization": ["MEP", "Government of Costa Rica"]}, "relations": [{"has_organization": {"head": "MEP School Census data", "tail": "MEP"}}, {"used_by": {"head": "MEP School Census data", "tail": "Government of Costa Rica"}}]}, "_meta": {"entry_id": 1140, "dataset_name": "MEP School Census data"}}
+{"input": "The Global Education Performance Survey (GEPS) provides comprehensive data on learning achievements and school enrollment rates across various countries. This survey, conducted in 2022, includes detailed statistics for Sub-Saharan Africa, highlighting critical trends in education. Such data is essential for policymakers and educators aiming to improve educational outcomes in the region. While the survey is widely used by various educational organizations, it is crucial to approach the findings with an understanding of the local contexts and limitations inherent in the data collection methods.", "output": {"entities": {"named_data": ["Global Education Performance Survey"], "organization": ["educational organizations"], "acronym": ["GEPS"], "year": ["2022"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Global Education Performance Survey", "tail": "GEPS"}}, {"has_timeframe": {"head": "Global Education Performance Survey", "tail": "2022"}}, {"has_geography": {"head": "Global Education Performance Survey", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "7. The MSNA, which ran in 7 countries: Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and Slovakia 8. Equivalized as per [Eurostat methodology. Essentially income per person, but with household members beyond the first one](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Equivalised_income) assigned weights less than 1.\n9. With the poverty line defined as 50% of the median national equivalized income 10. This figure differs from the one reported in the [previous year’s publication due to changes to methodology, which includes a](https://data.unhcr.org/en/documents/download/108068) different treatment of households that report zero income\n\n**4**", "output": {"entities": {"named_data": ["the MSNA"], "organization": ["MSNA"]}, "relations": [{"has_organization": {"head": "the MSNA", "tail": "MSNA"}}]}, "_meta": {"entry_id": 1268, "dataset_name": "the MSNA"}}
+{"input": "Recent analysis on gender equality highlights the findings from the Gender Equality in Employment Survey conducted in 2022, which provides valuable insights into the barriers women face in the labor market. This survey presents a comprehensive overview of women's participation in various sectors and underscores the need for policy reforms to support women's economic empowerment. Such data is crucial for understanding progress and identifying areas where targeted interventions are necessary to promote equality in employment opportunities.", "output": {"entities": {"named_data": ["Gender Equality in Employment Survey"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "gender equality and women's economic empowerment"}}
+{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19", "output": {"entities": {"named_data": ["EPR-ER data"], "organization": ["we"]}, "relations": [{"used_by": {"head": "EPR-ER data", "tail": "we"}}]}, "_meta": {"entry_id": 514, "dataset_name": "EPR-ER data"}}
+{"input": "recently introduced the first version of their Global Internal Displacement Database (GIDD) that allows users to explore, filter and sort IDMC ’ s data to produce graphs and tables, and export underlying data. 102 Such platforms need to incorporate safeguards to protect the privacy and confidentiality of individuals ’ data. UNHCR and Statistics Norway are currently leading an initiative to improve forced displacement statistics with the participation of national statistical agencies. This process began with the presentation of the “ Report on Statistics on Refugees and IDPs ” at the 46th session of the UN Statistical Commission in March 2015, 103 followed by an international conference in Turkey in October 2015. 104 The conference set in motion a process for national statistical agencies to collaborate to develop a set of recommendations that both countries and international organizations can use to improve data collection, reporting, data disaggregation, and overall quality, including the preparation of International Recommendations for Refugee Statistics (IRRS). Progress on this agenda was discussed at the 47th session of UNSD held in New York in March 2016, where it was recommended that the expert group should also include IDPs in its scope of work (UNSD 2016). 105 The current initiative is focused on refugees, asylum-seekers and IDPs but would ideally be extended to host communities and returnees.", "output": {"entities": {"named_data": ["Global Internal Displacement Database"], "organization": ["UNHCR", "Statistics Norway"]}, "relations": [{"has_organization": {"head": "Global Internal Displacement Database", "tail": "UNHCR"}}, {"used_by": {"head": "Global Internal Displacement Database", "tail": "Statistics Norway"}}]}, "_meta": {"entry_id": 552, "dataset_name": "Global Internal Displacement Database"}}
+{"input": "The Renewable Energy Access Survey (REAS) conducted by the Global Energy Institute in 2022 provides critical insights into the progress made towards improving energy access in developing regions. This dataset highlights the challenges and opportunities faced by households in accessing renewable energy sources.", "output": {"entities": {"named_data": ["Renewable Energy Access Survey"], "organization": ["Global Energy Institute"], "acronym": ["REAS"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Renewable Energy Access Survey", "tail": "Global Energy Institute"}}, {"has_acronym": {"head": "Renewable Energy Access Survey", "tail": "REAS"}}, {"has_timeframe": {"head": "Renewable Energy Access Survey", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}
+{"input": "The 2022 Water Management Assessment Report (WMAR) provides comprehensive insights into water usage patterns and sanitation facilities across various regions. It highlights the disparities in access to clean water and adequate sanitation in rural and urban areas of Brazil. The report, produced by the Ministry of Water Resources, serves as a crucial reference for policymakers and NGOs working in the water and sanitation sector. While the findings primarily focus on the year 2022, the data also reflects trends observed from 2015 to 2021, thereby offering a more extended timeframe for analysis. Such evaluations are essential for targeting interventions efficiently and improving living conditions.", "output": {"entities": {"named_data": ["Water Management Assessment Report", "WMAR"], "organization": ["Ministry of Water Resources"], "acronym": ["WMAR"], "year": ["2022", "2015 to 2021"], "geography": ["Brazil"]}, "relations": [{"has_acronym": {"head": "Water Management Assessment Report", "tail": "WMAR"}}, {"has_timeframe": {"head": "Water Management Assessment Report", "tail": "2022"}}, {"has_timeframe": {"head": "Water Management Assessment Report", "tail": "2015 to 2021"}}, {"has_geography": {"head": "Water Management Assessment Report", "tail": "Brazil"}}, {"has_organization": {"head": "Water Management Assessment Report", "tail": "Ministry of Water Resources"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "water, sanitation, and hygiene"}}
+{"input": "these mines. **Figure 2 Gold mines and DHS clusters in Ghana** **Panel A Gold mines and 20 km buffer zones Panel B Gold mines, DHS clusters, and 100 km buffer zones** 4 The distances are radii from mine center point, and form concentric circles around the mine. 5 The DHS and the GLSS data are representative at the regional level, and not at the district level. Since the regional level is too aggregated, we do the analysis at the district level, but note that the sample may not be representative. 8", "output": {"entities": {"named_data": ["GLSS"], "organization": ["the DHS"]}, "relations": [{"used_by": {"head": "GLSS", "tail": "the DHS"}}]}, "_meta": {"entry_id": 1218, "dataset_name": "GLSS"}}
+{"input": "refugees in Addis Ababa residing in rented houses. Most Eritrean refugee households (61 percent) live in temporary30 shelters provided by the UN or NGOs, whereas 50 percent of Somali refugee households and 71 percent of South Sudanese refugee households live in UN or NGO-provided permanent31 shelters. In Addis Ababa, 97 percent of refugee households live in rented houses. SESRE data also show that OCP refugees pay higher rents than hosts; on average, refugees pay roughly ETB 31,600 per year per adult equivalent, while hosts pay slightly less than half of that (ETB 18,700) (Annex D, Figure D.14). Refugees do not qualify for government 29 At least having difficulty with seeing, hearing, walking, remembering, selfcare or communicating. 30 Temporary shelters have walls mainly made of tent, plastic cover, and irons sheet. 31 Permanent shelters have walls mainly made of wood, mud, non-plastered blocks. 0 5 10 15 20 25 30 35 40 In camp Addis Ababa Total In camp Addis Ababa Total In camp Addis Ababa Total Stunted Underweight Wasted Hosts Refugees Percent Figure 2.18: Child nutritional indicators Source: World Bank Staff based on SESRE 2023. 0 1 2 3 4 5 6 Hosts Refugees Hosts Refugees Hosts Refugees In camp", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank", "World Bank Staff"]}, "relations": [{"has_organization": {"head": "SESRE 2023", "tail": "World Bank"}}, {"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 946, "dataset_name": "SESRE 2023"}}
+{"input": "3 The exact list of socioeconomic variables in the DHS is not identical to that on the JFPR application form. We\nderive the index of socioeconomic status in the DHS from variables describing: the ownership of a bicycle, cart,\nboat, motorbike, car, truck, radio, television; the conditions of the dwelling such as hard roofing and finished\nflooring; the availability of electric lighting; the main source of drinking water; the type of toilet facilities; and the\nmain type of cooking fuel used.\n\nbeneficiaries was done by the Local Management Committee (LMC) of a JFPR _secondary_ school. In\n\nThe JFPR scholarship program established a cut-off in the maximum number of scholarships\n\nFinally, the paper presents evidence of heterogeneity in the JFPR program effects. For this", "output": {"entities": {"named_data": ["DHS"], "organization": ["Local Management Committee"]}, "relations": [{"used_by": {"head": "DHS", "tail": "Local Management Committee"}}]}, "_meta": {"entry_id": 407, "dataset_name": "DHS"}}
+{"input": "cents. The universe of blocks (“ manzanas ”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To en- sure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process. 8 As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children ’ s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communi- ties, which account for 80 % of refugees worldwide (Climate Center 2022). We address these difficulties, leveraging all available information on Venezuelan settlements across the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit orga- nization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically. 9 Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found.", "output": {"entities": {"named_data": ["2018 population census"], "organization": ["iMMAP"]}, "relations": [{"used_by": {"head": "2018 population census", "tail": "iMMAP"}}]}, "_meta": {"entry_id": 375, "dataset_name": "2018 population census"}}
+{"input": "Urban planning efforts in East Africa have recently been bolstered by the findings from the Urban Infrastructure Assessment 2022, a comprehensive dataset published by the African Development Bank (AfDB). This dataset highlights critical infrastructure gaps in the region and is being utilized by various local governments to inform their transportation strategies. Furthermore, the 2023 Urban Mobility Survey (UMS) has been instrumental in tracking changes in urban transport patterns and is cited extensively by the United Nations Habitat as an essential resource for developing sustainable urban policies. Both datasets are pivotal for advancing the region's infrastructure planning initiatives.", "output": {"entities": {"named_data": ["Urban Infrastructure Assessment 2022", "Urban Mobility Survey"], "organization": ["African Development Bank", "United Nations Habitat"], "acronym": ["AfDB", "UMS"], "year": ["2022", "2023"], "geography": ["East Africa"]}, "relations": [{"has_organization": {"head": "Urban Infrastructure Assessment 2022", "tail": "African Development Bank"}}, {"used_by": {"head": "Urban Infrastructure Assessment 2022", "tail": "local governments"}}, {"has_acronym": {"head": "Urban Infrastructure Assessment", "tail": "AfDB"}}, {"has_timeframe": {"head": "Urban Infrastructure Assessment 2022", "tail": "2022"}}, {"has_geography": {"head": "Urban Infrastructure Assessment 2022", "tail": "East Africa"}}, {"has_organization": {"head": "Urban Mobility Survey", "tail": "United Nations Habitat"}}, {"used_by": {"head": "Urban Mobility Survey", "tail": "United Nations Habitat"}}, {"has_acronym": {"head": "Urban Mobility Survey", "tail": "UMS"}}, {"has_timeframe": {"head": "Urban Mobility Survey", "tail": "2023"}}, {"has_geography": {"head": "Urban Mobility Survey", "tail": "East Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "urban infrastructure and transportation planning"}}
+{"input": "REGIONAL BUREAU FOR SOUTHERN AFRICA\n\n###### **POPULATION OF CONCERN IN SOUTHERN AFRICA REGION**\n\n30 September 2022\n\n**PoCs IN SOUTHERN** **AFRICA REGION***\n\n**521,512** **REF**\n\n**41,283** **REF**\n\n**13,762** **ASY**\n\n**1,291** **ASY**\n\n**5,526,022** **IDP**\n\n**REF**\n\n**ASY**\n\n**OOC**\n\n**RET**\n\n**KEY FIGURES**\n\n## **8,572,919**\n\nTotal Population of concern\n\n###### **1,099,585**\n\nRefugees, asylum-seekers, other\nof concern & returnees**\n\n**785,119**\n\n**278,090**\n\n**36,165**\n\n**211**\n\n###### **7,473,334**\n\nConflict induced and Natural Disaster IDPs\n\nNatural Disaster IDPs\n\n**528,466**\n\n**7%**\n\n**6,419,356**\n\n**86%**\n\nIDPs RET\n\n**525,512**\n\n**7.0%**\n\ndo not imply official endorsement or acceptance by the United Nations\n\n**Author: UNHCR DIMA - RSA** Contact : rsarbdima@unhcr.org **Source:** UNHCR Primes, Government, IOM, OCHA, UNHCR\n\n**Author: UNHCR DIMA - RSA** Contact : rsarbdima@unhcr.org **Source: REF, ASY, OOC, RET** (UNHCR PRIMES, Government); **IDP DRC** (OCHA); IDP Zimbabwe & Mozambique (IOM); **IDP ROC** (Government, Ministry of Social Affairs and Humanitarian Action (MASAH).\n\n*PoCs = Persons of Concern   ** REF = Refugee; ASY = Asylum-seeker; OOC = Other person of concern; RET = Returnee. DRC = Democratic Republic of the Congo  ROC = Republic of the Congo Date of creation : 30 September\n2022\n\nFor more information visit: UNHCR Data Portal", "output": {"entities": {"named_data": ["UNHCR Primes"], "organization": ["UNHCR DIMA - RSA"]}, "relations": [{"used_by": {"head": "UNHCR Primes", "tail": "UNHCR DIMA - RSA"}}]}, "_meta": {"entry_id": 145, "dataset_name": "UNHCR Primes"}}
+{"input": "The analysis of gender wage gaps reveals significant disparities in earnings between men and women. Insights were drawn from the National Gender Equality Survey and the Women’s Economic Empowerment Assessment. These datasets provide valuable context for understanding the barriers women face in the labor market and the policies needed to address these challenges.", "output": {"entities": {"named_data": ["National Gender Equality Survey", "Women’s Economic Empowerment Assessment"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
+{"input": "The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father ’ s schooling, mother ’ s schooling, and local supply of schools in subdistrict of birth. 9 These exclusions resulted in a sample of 4, 139 males and 4, 131 females, which are referred to as the male and female full samples, respectively. 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts. 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA. 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8, 312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father ’ s schooling), and 7 observations (missing mother ’ s schooling).", "output": {"entities": {"named_data": ["2010 school census"], "organization": ["Ministry of Education"]}, "relations": [{"used_by": {"head": "2010 school census", "tail": "Ministry of Education"}}]}, "_meta": {"entry_id": 139, "dataset_name": "2010 school census"}}
+{"input": "The Economic Competitiveness Assessment Report (ECAR) for Southeast Asia, published in 2022, provides critical insights into regional trade dynamics and competitiveness. This dataset, collected by the International Trade Organization (ITO), includes data from 2019 to 2021, focusing on key economic indicators across member countries. In addition, the Southeast Asia Trade Database (SEATD) has been utilized extensively to analyze trade flows and barriers, covering the years 2018-2022 across the ASEAN region. While researchers have cited the ECAR, the SEATD remains the primary source for trade volume data, underscoring the importance of these datasets in shaping policy recommendations.", "output": {"entities": {"named_data": ["Economic Competitiveness Assessment Report", "Southeast Asia Trade Database"], "organization": ["International Trade Organization", "ITO"], "acronym": ["ECAR", "SEATD"], "year": ["2022", "2019", "2021", "2018-2022"], "geography": ["Southeast Asia", "ASEAN"]}, "relations": [{"has_acronym": {"head": "Economic Competitiveness Assessment Report", "tail": "ECAR"}}, {"has_timeframe": {"head": "Economic Competitiveness Assessment Report", "tail": "2022"}}, {"has_geography": {"head": "Economic Competitiveness Assessment Report", "tail": "Southeast Asia"}}, {"has_acronym": {"head": "Southeast Asia Trade Database", "tail": "SEATD"}}, {"has_timeframe": {"head": "Southeast Asia Trade Database", "tail": "2018-2022"}}, {"has_geography": {"head": "Southeast Asia Trade Database", "tail": "ASEAN"}}, {"has_organization": {"head": "Economic Competitiveness Assessment Report", "tail": "International Trade Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "Amid ongoing instability, the Central African Republic (CAR) has been the focus of several crucial data assessments. The Conflict Dynamics Assessment Report 2022, published by the International Crisis Group, provides in-depth insights into the socio-political landscape of the region. This dataset has been extensively used by the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) to inform their humanitarian responses. Additionally, the CAR Fragility Index (CFI), compiled by the World Bank, offers vital metrics for evaluating fragility in the country and is cited frequently by local NGOs working on peacebuilding initiatives. These datasets, especially the CFI, are critical for understanding the complexities of conflict and are integral to shaping effective interventions in CAR.", "output": {"entities": {"named_data": ["Conflict Dynamics Assessment Report 2022", "CAR Fragility Index"], "organization": ["International Crisis Group", "United Nations Office for the Coordination of Humanitarian Affairs", "World Bank", "local NGOs"], "acronym": ["CFI"], "year": ["2022"], "geography": ["Central African Republic"]}, "relations": [{"has_organization": {"head": "Conflict Dynamics Assessment Report 2022", "tail": "International Crisis Group"}}, {"used_by": {"head": "Conflict Dynamics Assessment Report 2022", "tail": "United Nations Office for the Coordination of Humanitarian Affairs"}}, {"has_organization": {"head": "CAR Fragility Index", "tail": "World Bank"}}, {"used_by": {"head": "CAR Fragility Index", "tail": "local NGOs"}}, {"has_acronym": {"head": "CAR Fragility Index", "tail": "CFI"}}, {"has_timeframe": {"head": "Conflict Dynamics Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Conflict Dynamics Assessment Report 2022", "tail": "Central African Republic"}}, {"has_geography": {"head": "CAR Fragility Index", "tail": "Central African Republic"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "conflict, fragility, and violence"}}
+{"input": "According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men.", "output": {"entities": {"named_data": ["SWMENA survey"], "organization": ["Status of Women in the Middle East and North Africa Survey Project", "World Bank"]}, "relations": [{"has_organization": {"head": "SWMENA survey", "tail": "Status of Women in the Middle East and North Africa Survey Project"}}, {"used_by": {"head": "SWMENA survey", "tail": "World Bank"}}]}, "_meta": {"entry_id": 541, "dataset_name": "SWMENA survey"}}
+{"input": "**FRAGILITY AND POPULATION MOVEMENT IN AFGHANISTAN**\n\nThird, _**particular attention should be devoted to minimizing any possible negative impact of**_ _**displacement on human capital investments for future generations.**_ Evidence suggests that the lack human capital is the main determinants of the risk of poverty and that households are likely to respond to negative shocks by pulling children out of school. While a comprehensive safety net system could help mitigating such negative consequences, bureaucratic barriers such as residency status and transferability of school records could negatively impact displaced or, more generally, mobile populations. Moreover, given the prevalence of mobility and displacement in Afghanistan, greater focus should be devoted to investing in functional literacy and skill-development programs that display greater portability and provide displaced individuals with greater access to economic opportunities, wherever they end up being.\n\nLastly, evidence suggests that migrants will likely continue to converge towards Afghanistan’s urban centers as they seek better security, jobs, and services. _Urbanization trends require immediate_ _intervention by local authorities to increase shelter capacity and access to services. National and_ _provincial authorities should further recognize that, in the medium and long term,_ _**local integration in**_ _**urban and semi-urban areas is inevitable and it requires adequate planning**_ _to maximize the returns_ _from urban agglomeration, for example by investing in connectivity and accessibility, while ensuring_ _access to basic services and a minimum standard of living._\n\n## **ENDNOTES**\n\n1. UN Population Movement Bulletin, Issue 5, 7 Sept, 2016.\n\n2. Based on UNHCR assisted returns data, 78 percent of returns occurred between 2002 and 2006. Districts with “high” intensity of returns in 2007 had an average share of returnees over the population of 70 percent.\n\n3. Returnee households are considered to have “reintegrated” successfully if they were able to return to the place where they used to live before displacement, and if they were able to achieve—on average—socio-economic outcomes and legal protection in line with those of the local/host population. Household ability to reintegrate successfully might depend on several factors related to the social, economic and institutional conditions prevailing in the host community, as well as on the physical, human and social capital accumulated by returnee households while in asylum, and on their returns “on arrival”.\n\n4. According to NRVA 2007–08 data, approximately 85 percent of Afghan households reported to have been negatively affected by a “large influx of returnees” during the 12 months preceding the survey.\n\n5. Demographic and Health Survey (2014).\n\n6. The youth bulge is defined as the share of youth aged 15–24 to the adult population aged 15+.\n\n7. Growth is expected to remain slow over coming years, reflecting weak demand, increasing output gap and the lack of fiscal space for increasing social transfers in order to boost short-term economic growth (World Bank, 2016; _Navigating Risk and Uncertainty in Afghanistan_ ).\n\n8. Urdal, Henrik. 2004. _The devil in the demographics: The effect of youth bulges on domestic armed conflict,_ 1950–2000. Social Development Papers: Conflict and Reconstruction Paper 14.\n\n9. Low-technology, rain-fed agriculture remains the country’s primary sector of employment, especially for its poorest and more vulnerable people.\n\n10. ALCS 2013–14.\n\n11. Probability of having a household member abroad was estimated using a Linear Probability model and ALCS 2013–14 data. Controls include a dummy indicating whether the household feels insecure in the district of residence; the number of security incidents per thousand inhabitants in the district of residence; composition and employment outcomes at the household level; dummy variables identifying returnee households, IDP households and households migrating for economic reasons; a dummy indicating urban residence and quintiles of a wealth index to proxy for household welfare.\n\n11", "output": {"entities": {"named_data": ["ALCS 2013–14 data"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "ALCS 2013–14 data", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1375, "dataset_name": "ALCS 2013–14 data"}}
+{"input": "The recent Energy Access Report 2022, published by the Global Energy Initiative, provides comprehensive insights into the state of energy access across Sub-Saharan Africa. This dataset reveals that over 600 million people in the region still lack reliable electricity. The report, utilizing data from the Renewable Energy Transition Database (RETD), aims to assist policymakers and stakeholders in improving energy sustainability. Furthermore, the International Renewable Energy Agency (IRENA) has been instrumental in analyzing trends based on the RETD, which covers energy access data from 2015 to 2022. Additionally, the data is supported by findings from the Africa Energy Outlook 2023, which has been cited by numerous academic institutions striving for better energy solutions.", "output": {"entities": {"named_data": ["Energy Access Report 2022", "Renewable Energy Transition Database", "Africa Energy Outlook 2023"], "organization": ["Global Energy Initiative", "International Renewable Energy Agency", "IRENA"], "acronym": ["RETD"], "year": ["2022", "2015 to 2022", "2023"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Energy Access Report 2022", "tail": "Global Energy Initiative"}}, {"has_geography": {"head": "Energy Access Report 2022", "tail": "Sub-Saharan Africa"}}, {"used_by": {"head": "Renewable Energy Transition Database", "tail": "International Renewable Energy Agency"}}, {"has_acronym": {"head": "Renewable Energy Transition Database", "tail": "RETD"}}, {"has_timeframe": {"head": "Renewable Energy Transition Database", "tail": "2015 to 2022"}}, {"used_by": {"head": "Africa Energy Outlook 2023", "tail": "numerous academic institutions"}}, {"has_timeframe": {"head": "Africa Energy Outlook 2023", "tail": "2023"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "energy access and renewable transitions"}}
+{"input": "The National Environmental Sustainability Survey (NESS) conducted by the Environmental Protection Agency (EPA) in 2022 provides crucial insights into the public's awareness and attitudes toward sustainability practices across the nation.", "output": {"entities": {"named_data": ["National Environmental Sustainability Survey"], "organization": ["Environmental Protection Agency"], "acronym": ["NESS"], "year": ["2022"], "geography": ["nation"]}, "relations": [{"has_organization": {"head": "National Environmental Sustainability Survey", "tail": "Environmental Protection Agency"}}, {"has_acronym": {"head": "National Environmental Sustainability Survey", "tail": "NESS"}}, {"has_timeframe": {"head": "National Environmental Sustainability Survey", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
+{"input": "The Gender Equity Assessment for Southeast Asia (GEASA) was published in 2021 and offers valuable insights into barriers faced by women in the workforce across the region. This assessment, produced by the Asian Development Bank, highlights disparities in labor force participation rates. The findings have been utilized by various NGOs focusing on women's empowerment. Furthermore, the Economic Participation Survey 2019 (EPS2019) provides complementary data, covering six countries including Thailand and Vietnam, which reinforces the need for gender-responsive policies. The EPS2019 has become a critical resource for policymakers aiming to improve women's economic status through targeted interventions.", "output": {"entities": {"named_data": ["Gender Equity Assessment for Southeast Asia", "Economic Participation Survey 2019"], "organization": ["Asian Development Bank", "NGOs"], "acronym": ["GEASA", "EPS2019"], "year": ["2021", "2019"], "geography": ["Southeast Asia", "Thailand", "Vietnam"]}, "relations": [{"has_acronym": {"head": "Gender Equity Assessment for Southeast Asia", "tail": "GEASA"}}, {"has_timeframe": {"head": "Gender Equity Assessment for Southeast Asia", "tail": "2021"}}, {"has_geography": {"head": "Gender Equity Assessment for Southeast Asia", "tail": "Southeast Asia"}}, {"used_by": {"head": "Gender Equity Assessment for Southeast Asia", "tail": "NGOs"}}, {"has_acronym": {"head": "Economic Participation Survey 2019", "tail": "EPS2019"}}, {"has_timeframe": {"head": "Economic Participation Survey 2019", "tail": "2019"}}, {"has_geography": {"head": "Economic Participation Survey 2019", "tail": "Thailand"}}, {"has_geography": {"head": "Economic Participation Survey 2019", "tail": "Vietnam"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
+{"input": "the original variables used to predict fragility as controls without changing results. It is only in the post-war period that exclusion and refugees become a factor that influences foreign investment flows. Finally, the results we find are robust across all datasets of foreign investment we use. These results make it at least plausible that political exclusion and refugees matter because they predict a relapse to more intense violence. As final piece of evidence for this idea we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD). We collected data on political risk evaluations from ONDD who, according to their annual report, insured transactions worth about 7 billion EUR in 2011. The variable we use measures the risk of a credit default for rea- sons beyond the control of the debtor, i. e. due to political or financial macroeconomic events. We choose this variable because it provides the most consistent time-series in the ONDD data. ONDD measures both short- and mid-term risk on a scale from 1 (low risk) to 7 (high risk). Table 12, columns (1) and (4) show that risk ratings are decreasing in peacetime. Note that, as before, we control for country fixed effects which implies that we look at changes within country.", "output": {"entities": {"named_data": ["ONDD data"], "organization": ["ONDD"]}, "relations": [{"has_organization": {"head": "ONDD data", "tail": "ONDD"}}]}, "_meta": {"entry_id": 1, "dataset_name": "ONDD data"}}
+{"input": "Operators/Assemblers Craf/Related Trade Workers Skilled Agricultural Workers Service/Sales Workers Clerical Support Workers Tech/Associate Professionals Managers/Professionals Percent Figure D.19: Occupation by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Inside the camp Outside the camp Percent Figure D.20: Work location by survey domains Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 40 45 50 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.21: Hours per week by survey domains Source: World Bank Staff based on SESRE 2023. Annexes 109 0 10 20 30 40 50 60 70 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.22: Hourly earnings by survey domains Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.24: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.23: Household owns crops Source: World Bank Staff based on SESRE 2023. 0", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank Staff"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 725, "dataset_name": "SESRE 2023"}}
+{"input": "We sourced bilateral trade value data from WITS and bilateral tariff data from a medley of sources, presented in Appendix Table A1. As part of this procedure, all entries in the two composite regions (rest of Western Asia and rest of Northern Africa) were split and assigned the split values to the newly created economies, while all entries for the two composite regions from the GTAP database were removed from the database. Each entry was split using the most thematically relevant external source. Sectoral GDP shares were used to split consumption and production values, trade data were used to split export and import values, and tariff information was used to assign tariff values. Export shares were used to split further production and consumption information into the final set of industries presented in Table 1. For internal consistency purposes, the required accounting relationships were imposed on the split database", "output": {"entities": {"named_data": ["WITS"], "organization": ["We"]}, "relations": [{"used_by": {"head": "WITS", "tail": "We"}}]}, "_meta": {"entry_id": 1029, "dataset_name": "WITS"}}
+{"input": "1. Introduction Sub-Saharan Africa is the youngest region in the world, and the world ’ s fastest-growing. Between 2010 and 2025, the number of people between 15 and 24 will grow to 250 million – a net increase of nearly 50 percent. Over the next decade, roughly one million young people will enter the labor market each month in Sub-Saharan Africa. However, these young people often enter the labor market too early and unprepared. Although access to education is growing, illiteracy remains high and schooling low: among the 32 Sub-Saharan African countries in the Barro-Lee (2010) data set, nearly 40 percent of women aged 15 and above have received no education at all; and the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average. 1 This lack of preparedness contributes to a growing problem of youth unemployment. Quantifying the level of unemployment is bedeviled by lack of data and measurement issues. Household and labor force surveys throughout Africa usually record unemployment rates of less than 10 percent, 2 but those figures belie the extent of underemployment and vulnerability. Those same surveys indicate that the vast majority of working adults have insecure work in the informal sector, on the family farm, or in less- productive or unremunerated labor.", "output": {"entities": {"named_data": ["Edstats data"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "Edstats data", "tail": "World Bank"}}]}, "_meta": {"entry_id": 676, "dataset_name": "Edstats data"}}
+{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel.", "output": {"entities": {"named_data": ["EPR-ER data"], "organization": ["we"]}, "relations": [{"used_by": {"head": "EPR-ER data", "tail": "we"}}]}, "_meta": {"entry_id": 389, "dataset_name": "EPR-ER data"}}
+{"input": "The 2022 National Revenue Survey conducted by the Ministry of Finance of Sri Lanka provides insights into the country's tax collection mechanisms and public expenditure management. This dataset, rich in detail, has been leveraged by the Asian Development Bank to inform their upcoming report on fiscal sustainability in the region. Additionally, the 2020 Public Expenditure Review Data (PERD) from the International Monetary Fund outlines trends in public spending across various sectors and has been used by local government agencies to enhance budgetary frameworks. Furthermore, the South Asia Public Financial Management Database (SAPFMD) serves as a critical resource, offering longitudinal data from 2015 to 2021, and is widely utilized by international development partners such as the World Bank to assess financial governance in South Asian nations.", "output": {"entities": {"named_data": ["National Revenue Survey", "Public Expenditure Review Data", "South Asia Public Financial Management Database"], "organization": ["Ministry of Finance", "Asian Development Bank", "International Monetary Fund", "local government agencies", "World Bank"], "acronym": ["National Revenue Survey", "Public Expenditure Review Data", "South Asia Public Financial Management Database"], "year": ["2022", "2020", "2015 to 2021"], "geography": ["Sri Lanka", "South Asia"]}, "relations": [{"has_organization": {"head": "National Revenue Survey", "tail": "Ministry of Finance"}}, {"used_by": {"head": "National Revenue Survey", "tail": "Asian Development Bank"}}, {"has_organization": {"head": "Public Expenditure Review Data", "tail": "International Monetary Fund"}}, {"used_by": {"head": "Public Expenditure Review Data", "tail": "local government agencies"}}, {"has_acronym": {"head": "Public Expenditure Review Data", "tail": "PERD"}}, {"has_timeframe": {"head": "South Asia Public Financial Management Database", "tail": "2015 to 2021"}}, {"has_organization": {"head": "South Asia Public Financial Management Database", "tail": "World Bank"}}, {"used_by": {"head": "South Asia Public Financial Management Database", "tail": "international development partners"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "public financial management and domestic revenue"}}
+{"input": "The Global Land Use Mapping Database (GLUMD), produced by the International Institute for Ecological Research, offers comprehensive data on land use changes across various regions. This dataset is crucial for informing policy decisions and planning efforts, particularly in developing countries affected by rapid urbanization.", "output": {"entities": {"named_data": ["Global Land Use Mapping Database"], "organization": ["International Institute for Ecological Research"], "acronym": ["GLUMD"], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Global Land Use Mapping Database", "tail": "International Institute for Ecological Research"}}, {"has_acronym": {"head": "Global Land Use Mapping Database", "tail": "GLUMD"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "The recent analysis of financial inclusion in the region relied heavily on the 2022 Financial Access Survey conducted by the Global Financial Insights Organization. This data highlights the disparities in access to banking services across various demographics, providing a comprehensive overview of the state of financial services in emerging economies.", "output": {"entities": {"named_data": ["2022 Financial Access Survey"], "organization": ["Global Financial Insights Organization"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "2022 Financial Access Survey", "tail": "Global Financial Insights Organization"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "The Urban Infrastructure Assessment Report 2022, published by the Global Development Organization, provides crucial data on transportation networks across various cities. This dataset, which focuses on urban mobility, is extensively used by the City Planning Consortium to inform their strategic planning initiatives. Additionally, the Transportation Performance Database (TPD), released in 2021 by the Urban Studies Institute, offers detailed metrics on public transit reliability and is utilized by multiple city authorities to improve services in metropolitan regions. The TPD covers major cities including New York, London, and Tokyo, ensuring a comprehensive view of global urban transport challenges.", "output": {"entities": {"named_data": ["Urban Infrastructure Assessment Report 2022", "Transportation Performance Database"], "organization": ["Global Development Organization", "City Planning Consortium", "Urban Studies Institute"], "acronym": ["Transportation Performance Database"], "year": ["2022", "2021"], "geography": ["New York", "London", "Tokyo"]}, "relations": [{"has_organization": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "Global Development Organization"}}, {"used_by": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "City Planning Consortium"}}, {"has_organization": {"head": "Transportation Performance Database", "tail": "Urban Studies Institute"}}, {"used_by": {"head": "Transportation Performance Database", "tail": "multiple city authorities"}}, {"has_acronym": {"head": "Transportation Performance Database", "tail": "TPD"}}, {"has_timeframe": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Transportation Performance Database", "tail": "2021"}}, {"has_geography": {"head": "Transportation Performance Database", "tail": "New York"}}, {"has_geography": {"head": "Transportation Performance Database", "tail": "London"}}, {"has_geography": {"head": "Transportation Performance Database", "tail": "Tokyo"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "urban infrastructure and transportation planning"}}
+{"input": "The recent analysis of the Urban Poverty Assessment Report 2023 conducted by the National Statistics Office highlights critical insights into income inequality in urban areas. This dataset, published by the National Statistics Office, has been utilized by various NGOs, including the Urban Development Initiative, to inform their strategic planning and community outreach programs. The report indicates substantial variations in poverty levels across different cities, with specific data for 2022 captured in the assessment. The Urban Poverty Assessment Report (UPAR) serves as a crucial resource for policymakers, researchers, and civil society organizations aiming to address urban poverty effectively.", "output": {"entities": {"named_data": ["Urban Poverty Assessment Report 2023", "Urban Poverty Assessment Report"], "organization": ["National Statistics Office", "Urban Development Initiative"], "acronym": ["Urban Poverty Assessment Report", "UPAR"], "year": ["2023", "2022"], "geography": []}, "relations": [{"has_organization": {"head": "Urban Poverty Assessment Report 2023", "tail": "National Statistics Office"}}, {"used_by": {"head": "Urban Poverty Assessment Report 2023", "tail": "Urban Development Initiative"}}, {"has_acronym": {"head": "Urban Poverty Assessment Report", "tail": "UPAR"}}, {"has_timeframe": {"head": "Urban Poverty Assessment Report 2023", "tail": "2023"}}, {"has_timeframe": {"head": "Urban Poverty Assessment Report", "tail": "2022"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "poverty headcount and inequality measurement"}}
+{"input": "The International Organization for Migration (IOM) recently published the Global Migration Data Report, which compiles essential statistics on migration trends across various regions. This report has become a crucial resource for policymakers and researchers alike, enabling them to understand the complexities of migration flows. Various organizations, including the United Nations High Commissioner for Refugees (UNHCR), have utilized this data in their assessments to inform strategic planning and policy formulation surrounding forced displacement issues.", "output": {"entities": {"named_data": ["Global Migration Data Report"], "organization": ["International Organization for Migration", "United Nations High Commissioner for Refugees"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Global Migration Data Report", "tail": "International Organization for Migration"}}, {"used_by": {"head": "Global Migration Data Report", "tail": "United Nations High Commissioner for Refugees"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "forced displacement, refugees, and migration"}}
+{"input": "Recent analyses have revealed significant disparities in access to employment opportunities for women across various sectors. The findings from the Women’s Economic Empowerment Assessment provide crucial insights into these inequalities, highlighting the barriers that prevent women from thriving in the workforce. Stakeholders must engage with this data to develop targeted interventions that address these systemic issues.", "output": {"entities": {"named_data": ["Women’s Economic Empowerment Assessment"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "gender equality and women's economic empowerment"}}
+{"input": "The transition to renewable energy sources has become increasingly crucial in addressing global energy access challenges. Recent findings from the Renewable Energy Access Survey provide important insights into the trends and barriers faced by various regions. Additionally, the Annual Energy Transition Report sheds light on the progress made in different countries towards achieving sustainable energy goals.", "output": {"entities": {"named_data": ["Renewable Energy Access Survey", "Annual Energy Transition Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "energy access and renewable transitions"}}
+{"input": "The Global Climate Adaptation Survey (GCAS) conducted by the Environmental Monitoring Agency in 2022 provides crucial insights into the effectiveness of climate resilience strategies across various regions. Following the publication of the GCAS, the United Nations Development Programme (UNDP) utilized this dataset to inform its Climate Resilience Framework, which aims to enhance adaptation measures in vulnerable communities. Additionally, the Urban Disaster Risk Report 2021, released by the International Disaster Research Institute, highlights significant vulnerabilities in urban areas and has been cited by numerous non-governmental organizations working in disaster risk reduction to tailor their interventions more effectively.", "output": {"entities": {"named_data": ["Global Climate Adaptation Survey", "Climate Resilience Framework", "Urban Disaster Risk Report 2021"], "organization": ["Environmental Monitoring Agency", "United Nations Development Programme", "International Disaster Research Institute", "non-governmental organizations"], "acronym": ["Global Climate Adaptation Survey", "GCAS"], "year": ["2022", "2021"], "geography": []}, "relations": [{"has_organization": {"head": "Global Climate Adaptation Survey", "tail": "Environmental Monitoring Agency"}}, {"used_by": {"head": "Global Climate Adaptation Survey", "tail": "United Nations Development Programme"}}, {"has_timeframe": {"head": "Global Climate Adaptation Survey", "tail": "2022"}}, {"has_organization": {"head": "Urban Disaster Risk Report 2021", "tail": "International Disaster Research Institute"}}, {"used_by": {"head": "Urban Disaster Risk Report 2021", "tail": "non-governmental organizations"}}, {"has_timeframe": {"head": "Urban Disaster Risk Report 2021", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "`o` Data was collected through phone calls made in November 2021 focusing on the families that received\nassistance during the second semester of 2021.\n\n`o` 15 enumerators supported this exercise (10 women and 5 men).\n\n_[2 UNHCR Global Focus, available at https://reporting.unhcr.org/brazil#toc-populations](https://reporting.unhcr.org/brazil#toc-populations)_\n\n_[3 Full text of this research is available at: https://openknowledge.worldbank.org/bitstream/handle/10986/35358/Integration-of-Venezuelan-Refugees-and-Migrants-in-](https://openknowledge.worldbank.org/bitstream/handle/10986/35358/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf?sequence=1&isAllowed=y)_\n_[Brazil.pdf?sequence=1&isAllowed=y](https://openknowledge.worldbank.org/bitstream/handle/10986/35358/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf?sequence=1&isAllowed=y)_\n\n_4 For more information, see_\n_[https://app.powerbi.com/view?r=eyJrIjoiMmVmNGNkOWEtZjQ2Yi00ZjFlLWExMzQtMjAxNjg2YjMxMzM3IiwidCI6IjE1ODgyNjJkLTIzZmItNDNiNC1iZDZlLWJjZTQ5Y](https://app.powerbi.com/view?r=eyJrIjoiMmVmNGNkOWEtZjQ2Yi00ZjFlLWExMzQtMjAxNjg2YjMxMzM3IiwidCI6IjE1ODgyNjJkLTIzZmItNDNiNC1iZDZlLWJjZTQ5YzhlNjE4NiIsImMiOjh9)_\n_[zhlNjE4NiIsImMiOjh9](https://app.powerbi.com/view?r=eyJrIjoiMmVmNGNkOWEtZjQ2Yi00ZjFlLWExMzQtMjAxNjg2YjMxMzM3IiwidCI6IjE1ODgyNjJkLTIzZmItNDNiNC1iZDZlLWJjZTQ5YzhlNjE4NiIsImMiOjh9)_\n\nwww.unhcr.org 3", "output": {"entities": {"named_data": ["UNHCR Global Focus"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR Global Focus", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 323, "dataset_name": "UNHCR Global Focus"}}
+{"input": "Recent studies highlight the critical impact of violence on community resilience. The Fragility Assessment Report 2022 provides detailed insights into the socio-economic challenges faced by affected populations. Meanwhile, the Conflict Dynamics Survey offers valuable data on the evolving nature of conflict in various regions. These datasets serve as essential resources for policymakers and researchers aiming to address issues of fragility and violence.", "output": {"entities": {"named_data": ["Fragility Assessment Report 2022", "Conflict Dynamics Survey"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "conflict, fragility, and violence"}}
+{"input": "The recent Labor Market Dynamics Study (LMDS) conducted by the National Bureau of Statistics in 2022 provides comprehensive insights into employment trends across various sectors. This dataset highlights significant changes in workforce participation and skill requirements, essential for policymakers and educators. The findings will serve as a crucial resource for further analysis and planning in labor market strategies.", "output": {"entities": {"named_data": ["Labor Market Dynamics Study"], "organization": ["National Bureau of Statistics"], "acronym": ["LMDS"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Labor Market Dynamics Study", "tail": "National Bureau of Statistics"}}, {"has_acronym": {"head": "Labor Market Dynamics Study", "tail": "LMDS"}}, {"has_timeframe": {"head": "Labor Market Dynamics Study", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "employment, labor markets, and skills development"}}
+{"input": "Recent analyses of the economic landscape in Sub-Saharan Africa have extensively utilized the Economic Inclusion Survey (EIS) conducted by the African Development Bank. This survey has provided valuable insights into the financial behaviors of households across the region. Moreover, the International Monetary Fund has cited the results from the Global Financial Access Report (GFAR) in their latest report, highlighting trends in access to financial services among vulnerable populations.", "output": {"entities": {"named_data": ["Economic Inclusion Survey", "Global Financial Access Report"], "organization": ["African Development Bank", "International Monetary Fund"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Economic Inclusion Survey", "tail": "African Development Bank"}}, {"used_by": {"head": "Global Financial Access Report", "tail": "International Monetary Fund"}}, {"has_organization": {"head": "Global Financial Access Report", "tail": "International Monetary Fund"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "Figure 5: Significant Historical Crises as a Share of Total Forced Displaced 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes IDPs protected or assisted by UNHCR, asylum-seekers and refugees. Excludes IDPs not protected or assisted by UNHCR and Palestinian refugees under UNRWA ’ s mandate. Figure 6: Top 15 Countries of Origin end-2015 Source: IDMC Global Report on Internal Displacement 2016, UNHCR Global Trends 2015, UNRWA A small number of countries carry the burden of hosting the majority of refugees. Historically since 1991, 15 asylum countries, overwhelmingly in the developing world, have hosted more than 50 percent of refugees and asylum-seekers (see Figure 7). 44 By the end of 2015, while almost all countries in the world were hosting refugees, the burden was unevenly shared (see Figure 8). Only seven countries hosted more 44 Major host countries are identified based on the cumulative number of refugees and asylum-seekers over the period 1991-2015.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR Statistical Online Population Database", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 285, "dataset_name": "UNHCR Statistical Online Population Database"}}
+{"input": "5. **Balochistan has been facing security challenges due in part to sharing borders with Afghanistan**\n\n**and Iran and to tribal conflict.** These security challenges result in an uncertain investment environment\ncontributing to the untapped economic potential of Balochistan, largely based on natural resource\nproduction (for example, natural gas, coal, and minerals). [5] As a consequence, Balochistan has experienced\na slower economic growth over the years compared to other provinces (1.4 percent in FY14/15, compared\nto 7.0 percent in Khyber Pakhtunkhwa, 4.3 percent in Punjab, and 3.2 percent in Sindh over the same\nperiod) [ 6] and high poverty rates, reaching 67.3 percent in the isolated district of Dera Bugti. [7]\n\n6. **Pakistan has had a protracted refugee situation since the 1970s, hosting 5 million Afghans at its**\n\n---\n[5] Asian Development Bank. 2005. _Balochistan Economic Report._\n[7] World Bank. 2019. Data4Pakistan‐District Development Portal (accessed on August 28, 2019).", "output": {"entities": {"named_data": ["Data4Pakistan‐District Development Portal"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "Data4Pakistan‐District Development Portal", "tail": "World Bank"}}]}, "_meta": {"entry_id": 859, "dataset_name": "Data4Pakistan‐District Development Portal"}}
+{"input": "In recent years, the Water Quality Assessment Report 2022, published by the Global Water Institute, has been instrumental in evaluating the safety of drinking water across various regions. This report, which focuses on 12 specific countries, including Bangladesh and Kenya, has been utilized by local governments and NGOs, such as Clean Water Initiative, for policy formulation and community outreach programs. Simultaneously, the Sanitation Facilities Survey (SFS) 2021, published by the International Sanitation Coalition, provides valuable insights into sanitation access and usage. The SFS data is extensively cited by organizations like the World Health Organization to design effective health interventions aimed at improving sanitation standards worldwide. Both datasets highlight the critical need for improved water and sanitation infrastructure, demonstrating the significant role of evidence-based data in driving policy changes.", "output": {"entities": {"named_data": ["Water Quality Assessment Report 2022", "Sanitation Facilities Survey (SFS) 2021"], "organization": ["Global Water Institute", "Clean Water Initiative", "International Sanitation Coalition", "World Health Organization"], "acronym": ["Sanitation Facilities Survey"], "year": ["2022", "2021"], "geography": ["Bangladesh", "Kenya"]}, "relations": [{"has_organization": {"head": "Water Quality Assessment Report 2022", "tail": "Global Water Institute"}}, {"used_by": {"head": "Water Quality Assessment Report 2022", "tail": "Clean Water Initiative"}}, {"has_timeframe": {"head": "Water Quality Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Water Quality Assessment Report 2022", "tail": "Bangladesh"}}, {"has_geography": {"head": "Water Quality Assessment Report 2022", "tail": "Kenya"}}, {"has_organization": {"head": "Sanitation Facilities Survey (SFS) 2021", "tail": "International Sanitation Coalition"}}, {"used_by": {"head": "Sanitation Facilities Survey (SFS) 2021", "tail": "World Health Organization"}}, {"has_acronym": {"head": "Sanitation Facilities Survey", "tail": "SFS"}}, {"has_timeframe": {"head": "Sanitation Facilities Survey (SFS) 2021", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "water, sanitation, and hygiene"}}
+{"input": "According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men.", "output": {"entities": {"named_data": ["Middle East and North Africa Survey Project"], "organization": ["Status of Women in the Middle East and North Africa Survey Project", "World Bank"]}, "relations": [{"has_organization": {"head": "Middle East and North Africa Survey Project", "tail": "Status of Women in the Middle East and North Africa Survey Project"}}, {"used_by": {"head": "Middle East and North Africa Survey Project", "tail": "World Bank"}}]}, "_meta": {"entry_id": 541, "dataset_name": "Middle East and North Africa Survey Project"}}
+{"input": "The Poverty and Inequality Assessment Report 2022 offers critical insights into the socio-economic conditions of various regions. Conducted by the Global Development Institute, the assessment highlights disparities in income levels among different demographics in the region. This report also provides comprehensive data for policy-makers and researchers aiming to address poverty alleviation efforts effectively. The findings specifically focus on countries in Southeast Asia, making the data highly relevant for targeted interventions. The Poverty and Inequality Assessment Report (PIAR) thus serves as a crucial resource for understanding these complex issues.", "output": {"entities": {"named_data": ["Poverty and Inequality Assessment Report 2022", "Poverty and Inequality Assessment Report", "Poverty and Inequality Assessment", "PIAR"], "organization": ["Global Development Institute"], "acronym": ["Poverty and Inequality Assessment", "PIAR"], "year": ["2022"], "geography": ["Southeast Asia"]}, "relations": [{"has_acronym": {"head": "Poverty and Inequality Assessment Report", "tail": "PIAR"}}, {"has_timeframe": {"head": "Poverty and Inequality Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Poverty and Inequality Assessment Report", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Poverty and Inequality Assessment Report", "tail": "Global Development Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "poverty headcount and inequality measurement"}}
+{"input": "In recent years, the Global Poverty Assessment Report 2022 has provided critical insights into poverty trends across various regions. This comprehensive report, published by the International Institute of Poverty Studies (IIPS), analyzes data from the Poverty Headcount Survey (PHS) which encompasses responses from 2017 to 2021. The PHS, used extensively by researchers at UNICEF, highlights significant disparities in poverty levels throughout South Asia and Sub-Saharan Africa. Moreover, the Inequality Measurement Database (IMD), which offers data from 2015 to 2020, covers multiple nations, including Brazil and India. Understanding these datasets is essential for policymakers aiming to address inequality effectively.", "output": {"entities": {"named_data": ["Global Poverty Assessment Report 2022", "Poverty Headcount Survey", "Inequality Measurement Database"], "organization": ["International Institute of Poverty Studies", "UNICEF"], "acronym": ["PHS", "IMD"], "year": ["2022", "2017 to 2021", "2015 to 2020"], "geography": ["South Asia", "Sub-Saharan Africa", "Brazil", "India"]}, "relations": [{"has_acronym": {"head": "Poverty Headcount Survey", "tail": "PHS"}}, {"has_timeframe": {"head": "Poverty Headcount Survey", "tail": "2017 to 2021"}}, {"has_geography": {"head": "Poverty Headcount Survey", "tail": "South Asia"}}, {"has_geography": {"head": "Poverty Headcount Survey", "tail": "Sub-Saharan Africa"}}, {"has_acronym": {"head": "Inequality Measurement Database", "tail": "IMD"}}, {"has_timeframe": {"head": "Inequality Measurement Database", "tail": "2015 to 2020"}}, {"has_geography": {"head": "Inequality Measurement Database", "tail": "Brazil"}}, {"has_geography": {"head": "Inequality Measurement Database", "tail": "India"}}, {"has_organization": {"head": "Global Poverty Assessment Report 2022", "tail": "International Institute of Poverty Studies"}}, {"used_by": {"head": "Poverty Headcount Survey", "tail": "UNICEF"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "poverty headcount and inequality measurement"}}
+{"input": "The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics. Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013. The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare. 1. DATA AND TECHNICAL ISSUES Data Sets Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty, 5 UNCHR, 2013pg 13 6 UNHCR (22 November 2013), UNHCR (15 September 2014) 7 UNHCR (22 March 2013), Erdogan (2014), pg 14 8 This technical issue will be discussed further later in the paper.", "output": {"entities": {"named_data": ["Labor Force Survey"], "organization": ["Turkish Statistical Institute"]}, "relations": [{"has_organization": {"head": "Labor Force Survey", "tail": "Turkish Statistical Institute"}}]}, "_meta": {"entry_id": 740, "dataset_name": "Labor Force Survey"}}
+{"input": "High-skill occupations include managers, professionals, and associate professionals. Ln Earnings is the log of monthly earnings winsorized at the 1st and 99th percentiles. All other models are linear probability models. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 107 Table D.8: Refugee Household Reliance on NGOs/Donations (1) (2) (3) (4) All Eritrea Somali South Sudan Years in Ethiopia -0.007** -0.017** -0.011** -0.001 (0.003) (0.006) (0.005) (0.005) Member works outside the camp -0.080** -0.335*** -0.180*** -0.001 (0.035) (0.099) (0.051) (0.041) Region Fixed Effects Yes No No No Demographic Controls Yes Yes Yes Yes N 1252 423 412 417 Source: World Bank Staff based on SESRE 2023. Note: Each column is a linear probability model where the outcome is a binary indicator of whether the household relies primarily on donations for income. Demographic controls include the household size and the share within each age and education group. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Table D.9: Determinants of refugee-host earnings gap (1) (2) (3) Earnings Earnings Earnings Refugee (% difference from hosts) -24.9** -22.4** -18.5* (0.137) (0.115) (0.114)", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank", "World Bank Staff"]}, "relations": [{"has_organization": {"head": "SESRE 2023", "tail": "World Bank"}}, {"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 292, "dataset_name": "SESRE 2023"}}
+{"input": "The Renewable Energy Access Survey (REAS) conducted in 2022 highlights significant progress in energy access across sub-Saharan Africa. This survey, published by the African Development Bank, provides valuable insights into the adoption of renewable technologies. The data from REAS is utilized by various NGOs, including the Energy Sustainability Initiative (ESI), to develop targeted programs aimed at enhancing energy accessibility. Further analysis from the World Bank's Energy Transition Database (ETD) reveals trends from 2018 to 2021, shedding light on the economic impacts of renewable energy investments. Both datasets are critical for understanding the current landscape of energy access in regions such as West Africa.", "output": {"entities": {"named_data": ["Renewable Energy Access Survey", "Energy Transition Database"], "organization": ["African Development Bank", "Energy Sustainability Initiative", "World Bank"], "acronym": ["REAS", "ETD"], "year": ["2022", "2018 to 2021"], "geography": ["sub-Saharan Africa", "West Africa"]}, "relations": [{"has_organization": {"head": "Renewable Energy Access Survey", "tail": "African Development Bank"}}, {"used_by": {"head": "Renewable Energy Access Survey", "tail": "Energy Sustainability Initiative"}}, {"has_acronym": {"head": "Renewable Energy Access Survey", "tail": "REAS"}}, {"has_timeframe": {"head": "Energy Transition Database", "tail": "2018 to 2021"}}, {"has_organization": {"head": "Energy Transition Database", "tail": "World Bank"}}, {"has_geography": {"head": "Renewable Energy Access Survey", "tail": "sub-Saharan Africa"}}, {"has_geography": {"head": "Energy Transition Database", "tail": "West Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "energy access and renewable transitions"}}
+{"input": "confirmed the results found in international literature presented above. The most important result is that Ukrainian refugees who are fluent in Polish earn a net wage premium of about PLN 700 (+16% relative to refugee net median wage, PLN 1,000 gross wage) when compared to those with beginner language skills. This result is stable across different model specifications. Note that such an earnings gain would bring the median net wage of a Ukrainian refugee (estimated based on the SEIS UNHCR survey in chapter 2) from 80% to 98% of the median in the economy as a whole (or from 80% to 93% according to Ukrainian refugee’s median in the NBP’s 2024 survey), almost closing the gap to the economy as a whole in these terms. It is in fact higher than the PLN 500 median net wage premium of the pre-war Ukrainian migrants over Ukrainian refugees in the NBP (2024) survey, even though 68% of the former and only 28% of the latter said they had a high level of fluency in Polish.\n\n31", "output": {"entities": {"named_data": ["NBP (2024) survey"], "organization": ["NBP"]}, "relations": [{"has_organization": {"head": "NBP (2024) survey", "tail": "NBP"}}]}, "_meta": {"entry_id": 1330, "dataset_name": "NBP (2024) survey"}}
+{"input": "fact that they are time consuming and therefore expensive to collect. At least three other less resource\nintensive alternatives to the FAO approach have been suggested to derive hunger numbers:\n\nanthropometric data, self-assessments, and direct use of HCES.\n\nquicker and cheaper to collect than full HCES efforts. However, how well they correlate with other\n\nThe third approach, and the one that we concentrate on here, is to use HCES to derive hunger statistics\n\nHCES are positioned between the single subjective hunger question and the intensive 24-hour recall.", "output": {"entities": {"named_data": ["HCES"], "organization": ["FAO", "we"]}, "relations": [{"has_organization": {"head": "HCES", "tail": "FAO"}}, {"used_by": {"head": "HCES", "tail": "we"}}]}, "_meta": {"entry_id": 692, "dataset_name": "HCES"}}
+{"input": "The recent findings from the Food Security Assessment 2023 have been instrumental for policymakers as they address critical issues relating to agricultural productivity. This assessment was produced by the International Food Policy Research Institute (IFPRI) and has been referenced by various organizations, including the Food and Agriculture Organization (FAO), who are working to implement sustainable food security strategies. The insights provided by this dataset have been vital in shaping responses to food scarcity challenges across various regions.", "output": {"entities": {"named_data": ["Food Security Assessment 2023"], "organization": ["International Food Policy Research Institute", "Food and Agriculture Organization"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Food Security Assessment 2023", "tail": "International Food Policy Research Institute"}}, {"used_by": {"head": "Food Security Assessment 2023", "tail": "Food and Agriculture Organization"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "agriculture, food security, and nutrition"}}
+{"input": "First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I in- clude a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows. 8 Third, both the size of a country and the size of the government may affect a government ’ s ability to detect and punish evaders. I include the 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government effectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the government ’ s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not significant at the p <. 05 level. 8I included two additional measures in the model neither of which were significant at the p < 0. 05 level. One is a measure of citizens ’ approval of how well their local government council provides citizens with the information about the councils budget (i. e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4).", "output": {"entities": {"named_data": ["World Bank Governance indicator"], "organization": ["World Bank", "Kaufmann, Kraay and Mastruzzi"]}, "relations": [{"has_organization": {"head": "World Bank Governance indicator", "tail": "World Bank"}}, {"used_by": {"head": "World Bank Governance indicator", "tail": "Kaufmann, Kraay and Mastruzzi"}}]}, "_meta": {"entry_id": 142, "dataset_name": "World Bank Governance indicator"}}
+{"input": "# Appendix. Model Calibration\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 the Polish economy was felt as combined four different shocks: to population, labour supply, average propensity to save, and productivity, with shocks to population and labour supply being balanced by equivalent shocks in Eastern Europe [53] .\nMoreover, as part of assumed increase in spending by Ukrainians in Poland was financed by savings from Ukraine this was balanced by equivalent negative shock on investment in Eastern Europe.\n\nShock to population was calibrated to match data for residents of Poland from Statistics Poland and number of refugees based on PESEL UKR. Equivalent shock in Eastern Europe was calculated using data for population in this region from World Population Prospects UN.", "output": {"entities": {"named_data": ["World Population Prospects"], "organization": ["World Population Prospects UN", "Deloitte Economic Institute"]}, "relations": [{"has_organization": {"head": "World Population Prospects", "tail": "World Population Prospects UN"}}, {"used_by": {"head": "World Population Prospects", "tail": "Deloitte Economic Institute"}}]}, "_meta": {"entry_id": 1308, "dataset_name": "World Population Prospects"}}
+{"input": "Access to safe drinking water remains a critical challenge in many regions. Recent findings from the Global Water Resources Assessment indicate that millions still lack basic water services. Additionally, the Comprehensive Sanitation Survey sheds light on the sanitation conditions faced by communities around the world, emphasizing the need for improved infrastructure and policy interventions.", "output": {"entities": {"named_data": ["Global Water Resources Assessment", "Comprehensive Sanitation Survey"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "water, sanitation, and hygiene"}}
+{"input": "The recent Energy Access Insights 2022 report, published by the Global Energy Partnership (GEP), highlights significant improvements in renewable energy access across various regions. This data has been utilized by the United Nations Development Programme (UNDP) to assess progress toward Sustainable Development Goal 7. Furthermore, the African Renewable Energy Database (ARED) provides comprehensive statistics on energy production and consumption from 2018 to 2021. The GEP's collaboration with local governments emphasizes the importance of region-specific strategies for enhancing energy infrastructure, particularly in Sub-Saharan Africa.", "output": {"entities": {"named_data": ["Energy Access Insights 2022 report", "African Renewable Energy Database"], "organization": ["Global Energy Partnership", "United Nations Development Programme"], "acronym": ["GEP", "ARED"], "year": ["2022", "2018 to 2021"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Energy Access Insights 2022 report", "tail": "Global Energy Partnership"}}, {"used_by": {"head": "Energy Access Insights 2022 report", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Global Energy Partnership", "tail": "GEP"}}, {"has_timeframe": {"head": "African Renewable Energy Database", "tail": "2018 to 2021"}}, {"has_geography": {"head": "African Renewable Energy Database", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "energy access and renewable transitions"}}
+{"input": "40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR statistical population online dataset", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 973, "dataset_name": "UNHCR statistical population online dataset"}}
+{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Survey). [6] Only 2 percent of respondents to a recent survey reported that they were working and had work permits. About 65 percent of the beneficiaries of the Emergency Social Safety Net Program (ESSN), a temporary humanitarian program, report that their main source of income is short-term informal work. [7] This will become a more significant problem once the ESSN comes to an end.\n\n13. **One of the most important contextual factors that limits formal job creation is the poor access**\n**to financing among firms.** Credit service provision is less developed in many provinces where refugees\nlive and work. According to the World Bank Enterprise Survey, most respondents (76 percent) in the affected regions assert that access to finance deteriorated loan terms and conditions (interest rates, maturity, and collateral requirements). [8] Poor access to longer-term financing limits enterprises from investing, increasing production capacity, and providing sustainable employment opportunities. After high tax rates, access to finance is perceived as a top constraint on firms, particularly small and medium enterprises (SMEs), seeking to carry out and expand business in Turkey. [9] Limited access to finance can also have a negative impact on labor market outcomes, resulting in higher unemployment, higher workforce informality, and lower employment growth. Limited access to credit is also problematic among large enterprises (LEs) because these have the potential to create more jobs, especially among refugees, including higher-quality formal jobs. [10] 14. **Banks do not usually have adequately structured resources to offer medium- to long-term**\n**maturities to most firms, mostly because of the short term of their liability base, thus leaving firms,**\n**mostly SMEs, open to severe liquidity and interest rate risk.** **[11]** Lack of cash flow-based financing and high\ncollateral requirements constrain access to finance among SMEs. [12] After the global financial crisis and strong rebalancing in the economy after August 2018, major banks have significantly cut their exposure to SMEs and LEs. The banking system has limited access to long‐term financing. It is funded mostly by relatively stable customer deposits that mature in less than three months, while most of the lending is concentrated in loans for more than three months. The result is a negative liquidity gap, that is, more liquid liabilities than assets, or a liquidity mismatch risk, which peaks in the one- to five‐year maturity range. These imbalances are reflected in bank loan portfolios and the liability structure of enterprises. The bank‐dominated financial sector thus has only a limited ability to provide the maturity critical to support SMEs and LEs that need to make long‐term investments, expand production capacity, and increase employment. In order to address the problems mentioned above, the government introduced some measures to improve SMEs access to finance and their entrepreneurial capacities, that could result effective in the medium to long-term: 6 Turkish Red Crescent and World Food Programme. 2019. _Refugees in Turkey. Livelihoods Survey Findings._ Ankara: Turk Kizilay and World Food Programme.\n7 World Bank and World Food Programme. 2019. _Vulnerability and Protection of Refugees in Turkey: Findings from the Rollout of_ _the Largest Humanitarian Cash Assistance Program in the World_ . Washington, DC: World Bank and World Food Programme.\n8 Enterprise Surveys (database), International Finance Corporation and World Bank, Washington, DC, https://www.enterprisesurveys.org/.\n9 Enterprise Surveys (database), International Finance Corporation and World Bank, Washington, DC., https://www.enterprisesurveys.org/.\n10 Ayyagari, M., A. Demirgüç-Kunt, and V. Maksimovic. 2011. “Small vs. Young Firms Across the World: Contribution to Employment, Job Creation, and Growth.” Policy Research Working Paper 5631, World Bank, Washington, DC.\n11 World Bank. 2014. _Turkey’s Transitions: Integration, Inclusion, Institutions_ . Report 90509-TR. Washington, DC: World Bank.\n12 World Bank 2014 and 2018 data of the Survey on the Access to Finance of Enterprises (database), European Central Bank, Frankfurt, https://www.ecb.europa.eu/stats/ecb_surveys/safe/html/index.en.html.\n\nPage 11 of 86", "output": {"entities": {"named_data": ["Survey on the Access to Finance of Enterprises"], "organization": ["European Central Bank", "World Bank"]}, "relations": [{"has_organization": {"head": "Survey on the Access to Finance of Enterprises", "tail": "European Central Bank"}}, {"used_by": {"head": "Survey on the Access to Finance of Enterprises", "tail": "World Bank"}}]}, "_meta": {"entry_id": 890, "dataset_name": "Survey on the Access to Finance of Enterprises"}}
+{"input": "Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka\nand Guillaume (2018). The data are primarily based on sub-national GDP per capita data constructed by\nGennaioli, _et al._ (2013) and covers 82 countries, representing 85% of the global population and 92% of\nglobal total GDP (PPP) in 2015. Population data is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).\n\nwe use World Bank income group classifications to divide the world into developing countries (that includes low-income, lower-middle and upper-middle income countries), and high-income countries.\n\nWe also use the Global Aridity Index and Potential Evapotranspiration Climate Database (Trabucco and Zomer 2019) to differentiate grid cells based on their aridity.", "output": {"entities": {"named_data": ["Global Aridity Index and Potential Evapotranspiration Climate Database"], "organization": ["we"]}, "relations": [{"used_by": {"head": "Global Aridity Index and Potential Evapotranspiration Climate Database", "tail": "we"}}]}, "_meta": {"entry_id": 1039, "dataset_name": "Global Aridity Index and Potential Evapotranspiration Climate Database"}}
+{"input": "9 Beans, for instance, which are one of the most important food purchases and items in Brazil, have a raw calorie intake of 306 kcal per 100 g in TBCA (Feijão, carioca), while in the prepared form, TBCA assigns 71 kcal per 100 g, assuming a composition of 50 percent beans and 50 percent water to the meal.\n\nTable 2 shows the top 10 food groups in POF in terms of average calorie intake per 100g. It shows that\n'Meat' is the most expensive food group, followed by 'Breads, Cakes and Pies'. On the other hand, 'Kitchen\nOil' is displayed as the most caloric food group, as expected. It is followed by 'Flour Derivatives' (which\nincludes pasta). The cheapest food group is 'Beans and Legumes', while the least caloric is 'Fruits'.\nTherefore, the data - including our calorie mapping - show some expected patterns of caloric and price\ndistribution across food groups.\n\n_Source:_ Own calculations using POF 2017/18 data and TBCA, based on food items categorizations by IBGE.\n\nFor this, we construct a consumption aggregate using the data from POF. The consumption aggregate is based on household expenditures on goods and services.", "output": {"entities": {"named_data": ["TBCA"], "organization": ["IBGE", "Own"]}, "relations": [{"has_organization": {"head": "TBCA", "tail": "IBGE"}}, {"used_by": {"head": "TBCA", "tail": "Own"}}]}, "_meta": {"entry_id": 223, "dataset_name": "TBCA"}}
+{"input": "An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer. { Table 3 here} Calculating Missing Gender Splits Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5. Combining Migrant Definitions Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see http: / / unstats. un. org / unsd / methods / m49 / m49regin. htm, with the countries of Oceania aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV. 18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock"], "organization": ["United Nations", "the authors"]}, "relations": [{"has_organization": {"head": "Trends in International Migrant Stock", "tail": "United Nations"}}, {"used_by": {"head": "Trends in International Migrant Stock", "tail": "the authors"}}]}, "_meta": {"entry_id": 191, "dataset_name": "Trends in International Migrant Stock"}}
+{"input": "Recent analyses of the Global Learning Achievement Survey (GLAS) reveal significant disparities in educational outcomes across various regions. The survey, conducted in 2021, focuses on assessing the reading and math skills of students in primary education. This dataset provides crucial insights into the educational challenges faced by countries in Sub-Saharan Africa, including Kenya and Uganda. Education stakeholders are encouraged to utilize the findings from the GLAS to tailor interventions that address these disparities and promote equitable learning opportunities for all children.", "output": {"entities": {"named_data": ["Global Learning Achievement Survey"], "organization": ["education stakeholders"], "acronym": ["GLAS"], "year": ["2021"], "geography": ["Sub-Saharan Africa", "Kenya", "Uganda"]}, "relations": [{"has_acronym": {"head": "Global Learning Achievement Survey", "tail": "GLAS"}}, {"has_timeframe": {"head": "Global Learning Achievement Survey", "tail": "2021"}}, {"has_geography": {"head": "Global Learning Achievement Survey", "tail": "Sub-Saharan Africa"}}, {"has_geography": {"head": "Global Learning Achievement Survey", "tail": "Kenya"}}, {"has_geography": {"head": "Global Learning Achievement Survey", "tail": "Uganda"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Survey). [6] Only 2 percent of respondents to a recent survey reported that they were working and had work permits. About 65 percent of the beneficiaries of the Emergency Social Safety Net Program (ESSN), a temporary humanitarian program, report that their main source of income is short-term informal work. [7] This will become a more significant problem once the ESSN comes to an end.\n\n13. **One of the most important contextual factors that limits formal job creation is the poor access**\n**to financing among firms.** Credit service provision is less developed in many provinces where refugees\nlive and work. According to the World Bank Enterprise Survey, most respondents (76 percent) in the affected regions assert that access to finance deteriorated loan terms and conditions (interest rates, maturity, and collateral requirements). [8] Poor access to longer-term financing limits enterprises from investing, increasing production capacity, and providing sustainable employment opportunities. After high tax rates, access to finance is perceived as a top constraint on firms, particularly small and medium enterprises (SMEs), seeking to carry out and expand business in Turkey. [9] Limited access to finance can also have a negative impact on labor market outcomes, resulting in higher unemployment, higher workforce informality, and lower employment growth. Limited access to credit is also problematic among large enterprises (LEs) because these have the potential to create more jobs, especially among refugees, including higher-quality formal jobs. [10] 14. **Banks do not usually have adequately structured resources to offer medium- to long-term**\n**maturities to most firms, mostly because of the short term of their liability base, thus leaving firms,**\n**mostly SMEs, open to severe liquidity and interest rate risk.** **[11]** Lack of cash flow-based financing and high\ncollateral requirements constrain access to finance among SMEs. [12] After the global financial crisis and strong rebalancing in the economy after August 2018, major banks have significantly cut their exposure to SMEs and LEs. The banking system has limited access to long‐term financing. It is funded mostly by relatively stable customer deposits that mature in less than three months, while most of the lending is concentrated in loans for more than three months. The result is a negative liquidity gap, that is, more liquid liabilities than assets, or a liquidity mismatch risk, which peaks in the one- to five‐year maturity range. These imbalances are reflected in bank loan portfolios and the liability structure of enterprises. The bank‐dominated financial sector thus has only a limited ability to provide the maturity critical to support SMEs and LEs that need to make long‐term investments, expand production capacity, and increase employment. In order to address the problems mentioned above, the government introduced some measures to improve SMEs access to finance and their entrepreneurial capacities, that could result effective in the medium to long-term: 6 Turkish Red Crescent and World Food Programme. 2019. _Refugees in Turkey. Livelihoods Survey Findings._ Ankara: Turk Kizilay and World Food Programme.\n7 World Bank and World Food Programme. 2019. _Vulnerability and Protection of Refugees in Turkey: Findings from the Rollout of_ _the Largest Humanitarian Cash Assistance Program in the World_ . Washington, DC: World Bank and World Food Programme.\n8 Enterprise Surveys (database), International Finance Corporation and World Bank, Washington, DC, https://www.enterprisesurveys.org/.\n9 Enterprise Surveys (database), International Finance Corporation and World Bank, Washington, DC., https://www.enterprisesurveys.org/.\n10 Ayyagari, M., A. Demirgüç-Kunt, and V. Maksimovic. 2011. “Small vs. Young Firms Across the World: Contribution to Employment, Job Creation, and Growth.” Policy Research Working Paper 5631, World Bank, Washington, DC.\n11 World Bank. 2014. _Turkey’s Transitions: Integration, Inclusion, Institutions_ . Report 90509-TR. Washington, DC: World Bank.\n12 World Bank 2014 and 2018 data of the Survey on the Access to Finance of Enterprises (database), European Central Bank, Frankfurt, https://www.ecb.europa.eu/stats/ecb_surveys/safe/html/index.en.html.\n\nPage 11 of 86", "output": {"entities": {"named_data": ["World Bank Enterprise Survey"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "World Bank Enterprise Survey", "tail": "World Bank"}}, {"used_by": {"head": "World Bank Enterprise Survey", "tail": "World Bank"}}]}, "_meta": {"entry_id": 890, "dataset_name": "World Bank Enterprise Survey"}}
+{"input": "The 2022 National Safety Nets Assessment Report, produced by the Ministry of Social Development in Zambia, offers critical insights into the effectiveness of various social protection programs. This dataset has been widely utilized by the International Labour Organization (ILO) to analyze the impact of these programs on poverty alleviation. Additionally, the report includes data segmented by urban and rural geographies, allowing for a nuanced understanding of regional disparities. The assessment also draws on the Social Protection Survey (SPS) — an acronym used in prior studies — which covers data from 2020 to 2021 and was generated by the World Bank. This collaboration between local and international organizations underscores the importance of robust data in shaping effective policies.", "output": {"entities": {"named_data": ["National Safety Nets Assessment Report", "Social Protection Survey"], "organization": ["Ministry of Social Development", "International Labour Organization", "World Bank"], "acronym": ["SPS"], "year": ["2022", "2020 to 2021"], "geography": ["Zambia", "urban", "rural"]}, "relations": [{"has_organization": {"head": "National Safety Nets Assessment Report", "tail": "Ministry of Social Development"}}, {"used_by": {"head": "National Safety Nets Assessment Report", "tail": "International Labour Organization"}}, {"has_geography": {"head": "National Safety Nets Assessment Report", "tail": "Zambia"}}, {"has_timeframe": {"head": "Social Protection Survey", "tail": "2020 to 2021"}}, {"has_organization": {"head": "Social Protection Survey", "tail": "World Bank"}}, {"has_acronym": {"head": "Social Protection Survey", "tail": "SPS"}}, {"has_geography": {"head": "Social Protection Survey", "tail": "urban"}}, {"has_geography": {"head": "Social Protection Survey", "tail": "rural"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "social protection and safety nets"}}
+{"input": "the OECD, a highly skilled workforce, and a transformative digitalization and management agenda. **Foundational Learning** 5. **National assessments show weakness in foundational learning, and learning outcomes are strongly correlated** **to socioeconomic conditions, which points to the need for foundational learning with deeper attention to the education** **outcomes of students from poorer families.** Costa Rica reactivated standardized national assessments in 2023; the first results from 2023 were released in March 2024 and indicated that about a third of the students at the end of Primary schooling are at the “basic” level of learning. Internationally comparable Programme for International Student Assessment (PISA) data for Costa Rica for 2018 shows that while 28 percent of children from families in the top quintile of the PISA index for economic, social and cultural status were below Level 2 on Reading in PISA (considered the minimum of adequate performance), 72 percent of children from families from the lowest quintile of the PISA index were below level 2. Foundational learning (literacy and numeracy) in early grades paves the way for future learning, and differences in educational attainment become magnified through youth and adult life in the acquisition of human capital. Without any claims regarding causation, it is", "output": {"entities": {"named_data": ["Internationally comparable Programme for International Student Assessment"], "organization": ["Costa Rica"]}, "relations": [{"used_by": {"head": "Internationally comparable Programme for International Student Assessment", "tail": "Costa Rica"}}]}, "_meta": {"entry_id": 1206, "dataset_name": "Internationally comparable Programme for International Student Assessment"}}
+{"input": "The 2022 Social Protection Assessment Report provides comprehensive insights into the effectiveness of safety nets across various regions. This dataset, published by the International Social Policy Institute, serves as a vital resource for understanding the impact of social protection policies implemented in different countries.", "output": {"entities": {"named_data": ["2022 Social Protection Assessment Report"], "organization": ["International Social Policy Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "2022 Social Protection Assessment Report", "tail": "International Social Policy Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "social protection and safety nets"}}
+{"input": "The monthly precipitation data comes from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association's Climate Prediction Center, is based on ground station measurements of precipitation over the period 1948-2001.\n\nSoil data was obtained from FAO (2003). The FAO data provides information about the\nmajor and minor soils in each location. Data concerning the hydrology was predicted from a\nhydrological model for Africa (Strzepek & McCluskey 2006). The model calculated the\nwater flow through each district in the surveyed countries. Data on elevation at the centroid\nof each district was obtained through GIS manipulation using data from the United States\nGeological Survey (USGS, 2004). The USGS data are derived from a global digital elevation\nmodel with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).\n\nWeng F & Grody N, 1998. Physical retrieval of land surface temperature using the Special Sensor Microwave Imager. _Journal of Geophysical Research_ 103: 8839-8848. World Bank, 2003. Africa rainfall and temperature evaluation system (ARTES). World Bank, Washington DC.", "output": {"entities": {"named_data": ["Africa Rainfall and Temperature Evaluation System (ARTES)"], "organization": ["National Oceanic and Atmospheric Association's Climate Prediction Center", "World Bank"]}, "relations": [{"has_organization": {"head": "Africa Rainfall and Temperature Evaluation System (ARTES)", "tail": "National Oceanic and Atmospheric Association's Climate Prediction Center"}}, {"used_by": {"head": "Africa Rainfall and Temperature Evaluation System (ARTES)", "tail": "World Bank"}}]}, "_meta": {"entry_id": 43, "dataset_name": "Africa Rainfall and Temperature Evaluation System (ARTES)"}}
+{"input": "The Global Primary Education Assessment (GPEA) conducted in 2022 provides critical insights into learning achievements across various countries. This dataset, compiled by the Education Research Institute, is vital for policymakers aiming to enhance educational outcomes in underserved regions.", "output": {"entities": {"named_data": ["Global Primary Education Assessment"], "organization": ["Education Research Institute"], "acronym": ["GPEA"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Global Primary Education Assessment", "tail": "Education Research Institute"}}, {"has_acronym": {"head": "Global Primary Education Assessment", "tail": "GPEA"}}, {"has_timeframe": {"head": "Global Primary Education Assessment", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "Economics of Disasters and Climate Change Fig. 2 Flood maps showing inundation depth (cm) in case of a: (a) 10-year return period flood under current conditions, (b) 25-year return period flood under current conditions; (c) 50-year return period flood under current conditions; (d) 10-year return period flood given a 30 cm sea level rise; (e) 25-year return period flood given a 30 cm sea level rise; and (f) 50-year return period flood given a 30 cm sea level rise Local-Level Data on Urban Areas and Potential Slums in Ho Chi Minh City The spatial socioeconomic data set used for HCMC is a data set of potential slum areas from 2000 to 2010, from the Platform for Urban Management and Analysis (PUMA), a city-level data set developed by the World Bank (World Bank 2015).This data was collected via satellite in the year 2012, through a combination of visual interpretation of various sources and vintages of imagery. To guide the identification of slums, previous work has provided information on the appearance and geographical extent of slums in HCMC. Surveys of poverty in the city find the appearance of slums in HCMC to be characterized as densely built small households and shelters that", "output": {"entities": {"named_data": ["Platform for Urban Management and Analysis"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "Platform for Urban Management and Analysis", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1180, "dataset_name": "Platform for Urban Management and Analysis"}}
+{"input": "The Economic Competitiveness Assessment Report (ECAR) released by the Global Trade Institute in 2022 provides critical insights into the trade dynamics affecting developing countries. This report, which analyzes trends from 2019 to 2021, was utilized by the United Nations Conference on Trade and Development (UNCTAD) to identify growth opportunities. In addition, the Industry Benchmark Database (IBD) published by the International Industry Association offers comprehensive performance metrics for the manufacturing sector across Latin America. This dataset, covering the years 2018 to 2020, is frequently referenced by various local governments in the region to inform their economic policies.", "output": {"entities": {"named_data": ["Economic Competitiveness Assessment Report", "ECAR", "Industry Benchmark Database", "IBD"], "organization": ["Global Trade Institute", "United Nations Conference on Trade and Development", "International Industry Association", "local governments"], "acronym": ["ECAR", "IBD"], "year": ["2022", "2019 to 2021", "2018 to 2020"], "geography": ["developing countries", "Latin America"]}, "relations": [{"has_organization": {"head": "Economic Competitiveness Assessment Report", "tail": "Global Trade Institute"}}, {"used_by": {"head": "Economic Competitiveness Assessment Report", "tail": "United Nations Conference on Trade and Development"}}, {"has_acronym": {"head": "Economic Competitiveness Assessment Report", "tail": "ECAR"}}, {"has_timeframe": {"head": "Economic Competitiveness Assessment Report", "tail": "2022"}}, {"has_timeframe": {"head": "Economic Competitiveness Assessment Report", "tail": "2019 to 2021"}}, {"has_organization": {"head": "Industry Benchmark Database", "tail": "International Industry Association"}}, {"used_by": {"head": "Industry Benchmark Database", "tail": "local governments"}}, {"has_acronym": {"head": "Industry Benchmark Database", "tail": "IBD"}}, {"has_timeframe": {"head": "Industry Benchmark Database", "tail": "2018 to 2020"}}, {"has_geography": {"head": "Industry Benchmark Database", "tail": "Latin America"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR statistical population online dataset", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 318, "dataset_name": "UNHCR statistical population online dataset"}}
+{"input": "The recent analysis of urban sprawl in the Greater Deltaland region has been extensively supported by the Deltaland Land Use Mapping Report, published by the Deltaland Environmental Agency. Additionally, the Remote Sensing Assessment 2022, produced by Geospatial Innovations LLC, provides crucial satellite imagery that underpins various studies on land cover changes over the last decade. These datasets are pivotal for informing policy decisions related to sustainable development in the area.", "output": {"entities": {"named_data": ["Deltaland Land Use Mapping Report", "Remote Sensing Assessment 2022"], "organization": ["Deltaland Environmental Agency", "Geospatial Innovations LLC"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Deltaland Land Use Mapping Report", "tail": "Deltaland Environmental Agency"}}, {"has_organization": {"head": "Remote Sensing Assessment 2022", "tail": "Geospatial Innovations LLC"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n**Chart 15. Ukrainian refugees wages median net wage by age group**\n\nMonthly net wage (PLN) Percengate of all workers total economy average Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n**Chart 16. Median net wages of Ukrainian refugees median net wage by sector**\n\nin PLN Percentage of total economy average 126% 15 to 24 25 to 34 35 to 44 45 to 54 55 to 64 15 to 24 25 to 34 35 to 44 45 to 54 55 to 64 Ukrainian refugees All workers Source: Deloitte own elaboration based on SEIS UNHCR survey and GUS data.\n\n**The Ukrainian refugee groups to earn**\n\n**the highest wages compared to the**\n\n**wages in the economy as a whole are**", "output": {"entities": {"named_data": ["GUS data"], "organization": ["GUS", "Deloitte"]}, "relations": [{"has_organization": {"head": "GUS data", "tail": "GUS"}}, {"used_by": {"head": "GUS data", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1316, "dataset_name": "GUS data"}}
+{"input": "The recent Labor Market Dynamics Assessment Report (LMDA) highlights evolving trends in the workforce across various sectors. Conducted in Brazil, the assessment utilizes data from 2022 to examine changes in employment patterns. This comprehensive dataset is instrumental for policymakers, providing insights into labor market shifts and skill gaps. The report is published by the International Labor Organization (ILO), which emphasizes its significance in aiding governments to design effective employment strategies. In addition to LMDA, the Universal Skills Survey (USS) conducted across multiple countries from 2020 to 2023 aims to measure competencies in the workforce and their impact on economic growth. These datasets are crucial for understanding the intersection of skills development and employment in a global context.", "output": {"entities": {"named_data": ["Labor Market Dynamics Assessment Report", "Universal Skills Survey"], "organization": ["International Labor Organization"], "acronym": ["LMDA", "USS"], "year": ["2022", "2020 to 2023"], "geography": ["Brazil"]}, "relations": [{"has_acronym": {"head": "Labor Market Dynamics Assessment Report", "tail": "LMDA"}}, {"has_timeframe": {"head": "Labor Market Dynamics Assessment Report", "tail": "2022"}}, {"has_geography": {"head": "Labor Market Dynamics Assessment Report", "tail": "Brazil"}}, {"has_acronym": {"head": "Universal Skills Survey", "tail": "USS"}}, {"has_timeframe": {"head": "Universal Skills Survey", "tail": "2020 to 2023"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "employment, labor markets, and skills development"}}
+{"input": "[8] F. N. P. Nimoh, T. P. Beltramo, J. R. Fix, F. K. Appler, U. J. Pape, and L. A. Rios Rivera, ‘Understanding the Socioeconomic Conditions of\nthe Stateless Shona Community in Kenya: Results from the 2019 Socioeconomic Survey’, Dec. 2020. Accessed: Apr. 02, 2024. [Online].\nAvailable: https://documents.worldbank.org/en/publication/documentsreports/documentdetail/356511608745182603/Understanding-the-Socioeconomic-Conditions-of-the-Stateless-Shona-Community-inKenya-Results-from-the-2019-Socioeconomic-Survey\n\n[9] U. J. Pape _et al._, ‘How COVID-19 Continues to Affect Lives of Refugees in Kenya : Rapid Response Phone Survey - Rounds 1 to 5’, World\nBank Group, Washington, D.C., Policy Note 166098, Oct. 2021. Accessed: Oct. 10, 2024. [Online]. Available:\nhttps://documents1.worldbank.org/curated/en/202201637042522937/pdf/How-COVID-19-Continues-to-Affect-Lives-of-Refugeesin-Kenya-Rapid-Response-Phone-Survey-Rounds-1-to-5.pdf\n\n[10] Kenya National Bureau of Statistics and ICF, ‘Kenya Demographic and Health Survey 2022. Key Indicators Report’, KNBS and ICF,\n\nNairobi, Kenya, and Rockville, Maryland, USA, 2023. Accessed: Oct. 10, 2024. [Online]. Available: https://www.knbs.or.ke/wpcontent/uploads/2023/08/Kenya-Demographic-and-Health-Survey-2022-Key-Indicators-Report.pdf", "output": {"entities": {"named_data": ["Rapid Response Phone Survey"], "organization": ["U. J. Pape _et al._"]}, "relations": [{"used_by": {"head": "Rapid Response Phone Survey", "tail": "U. J. Pape _et al._"}}]}, "_meta": {"entry_id": 1280, "dataset_name": "Rapid Response Phone Survey"}}
+{"input": "Vermeer, M. and S. Rahmstorf. 2009. Global sea level linked to global temperature. _Proceedings_ _of the National Academy of Sciences_ 106 (51), 21527-32. VLIZ. 2011. Maritime Boundaries Geodatabase, version 6.1. Available online at [http://www.vliz.be/vmdcdata/marbound. Consulted on 2011-05-12.](http://www.vliz.be/vmdcdata/marbound) Williams, M., 1990. Understanding Wetlands. In M. Williams (ed) _Wetlands: A Threatened_ _Landscape_ . Wiley-Blackwell. Woodward, R.T. and Y.S. Wui. 2001. The economic value of wetland services: A meta-analysis. _Ecological Economics_, 37, 257-270 World Bank, 2010. Boundaries of the World. Map Design Unit. The boundaries, colors, denominations and any other information shown on this map do not imply, on the part of the World Bank Group, any judgement on the legal status of any territory, or any endorsement or acceptance of such boundaries. 25", "output": {"entities": {"named_data": ["Maritime Boundaries Geodatabase, version 6.1"], "organization": ["VLIZ", "World Bank"]}, "relations": [{"has_organization": {"head": "Maritime Boundaries Geodatabase, version 6.1", "tail": "VLIZ"}}, {"used_by": {"head": "Maritime Boundaries Geodatabase, version 6.1", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1166, "dataset_name": "Maritime Boundaries Geodatabase, version 6.1"}}
+{"input": "The daily precipitation data used are from the 3- hourly data set from the Tropical Rainfall Measurement Mission Project (TRMM), which is aggregated up to daily data.\n\n\\n<!-- PAGE 28 -->\\ndata set product 3B42RT 3 hour product gives the best results in a basket of 8 near real-time rainfall products. The 3B42RT daily derived product is what is used in this paper.\n\n\\n<!-- PAGE 8 -->\\nthrough 2015. During the period from 1985 to 2016, the Dartmouth Flood Observatory (DFO) registered 3,808 floods of magnitude 4 or more and 1,175 floods of magnitude 6 and up. [3]\n\nAccording to the Indonesian National Disaster Management Authority (BNPB), there were more than 19,000 natural hazards in the period 2001 - 2015 (National Disaster Management Agency 2016), making Indonesia a useful country for any natural hazard analysis.\n\nAccording to the Global Facility for Disaster Reduction and Recovery (GFDRR), the Philippines is at high risk from several types of natural hazards (GFDRR 2019). Prime among them are cyclones, where an average of 20 make landfall every year. In 2013, typhoon Yolanda led to 6,000 casualties and damaged more than 1.1 million houses. The Philippines are also exposed to earthquake and flood risks.", "output": {"entities": {"named_data": ["3B42RT daily derived product"], "organization": ["Tropical Rainfall Measurement Mission Project (TRMM)", "this paper"]}, "relations": [{"has_organization": {"head": "3B42RT daily derived product", "tail": "Tropical Rainfall Measurement Mission Project (TRMM)"}}, {"used_by": {"head": "3B42RT daily derived product", "tail": "this paper"}}]}, "_meta": {"entry_id": 584, "dataset_name": "3B42RT daily derived product"}}
+{"input": "-0.094** -0.062 3.893 -0.064 -0.031 0.126*** (0.042) (0.040) (3.842) (0.066) (0.038) (0.042) _3. drop 2 years before_ active*mine -0.094** -0.062 3.708 -0.071 -0.026 0.125*** (0.041) (0.039) (3.459) (0.067) (0.036) (0.043) _4. mine FE_ active*mine -0.123** -0.094* 8.233 -0.068 -0.049 0.113** (0.057) (0.051) (5.425) (0.075) (0.044) (0.045) _5. mine clustering_ active*mine -0.086*** -0.055** 3.705 -0.058 -0.032 0.125** (0.025) (0.025) (2.898) (0.086) (0.032) (0.051) Mean dep var 0.715 0.705 45.71 0.491 0.259 0.028 _Note:_ The table uses GLSS data for Ghana for the survey years 1998, 2005, 2012. The sample is restricted to women and men aged 15–49. Robust standard errors clustered at the village or neighborhood level in parentheses (except if otherwise stated). All regressions control for year and district fixed effects, urban dummy, age, and years of education. Active is active status of mine in the survey year. The treatment distance is defined to 20 km. Rows 2 drop sample between 20 to 40 km of a mine, and rows 3 drop sample that was surveyed two years before mine opening. *** p<0.01, **p<0.05, *p<0.1. FE = fixed effects. 30", "output": {"entities": {"named_data": ["GLSS data for Ghana"], "organization": ["GLSS"]}, "relations": [{"has_organization": {"head": "GLSS data for Ghana", "tail": "GLSS"}}]}, "_meta": {"entry_id": 1220, "dataset_name": "GLSS data for Ghana"}}
+{"input": "\"Life in Transition Survey, Transition Report 2020-2021: The State Strike Back\", [https://www.ebrd.com/publications/transition-report-](https://www.ebrd.com/publications/transition-report-202021) [202021.](https://www.ebrd.com/publications/transition-report-202021)\n\n\"Strengthening the business environment for productivity convergence,\" in OECD Economic Surveys: Romania 2022, OECD Publishing, Paris, [https://doi.org/10.1787/63318cf5-en.](https://doi.org/10.1787/63318cf5-en)\n\n_Encuesta_ _Dirigida_ _a_ _la_ _Población_ _Venezolana_ _que_ _Reside_ _en_ _El_ _País_ _(ENPOVE)_ is a special ized survey of Venezuelans living in Peru conducted by the National Institute of Statistics (INEI) in December 2018. The sample covers five main urban areas in the country where Venezuelan immigrants were most likely to be present. The survey collects data on the immi grant's origin, migration date, and details on their current employment. Importantly, a full module asks about the immigrant's experiences with locals, which includes questions about discrimination and hostile attitudes towards them. The respondent's current location is iden tified down to the _centro_ _poblado_ level, which roughly corresponds to an urban neighborhood or a rural town.\n\n_Encuesta_ _Nacional_ _de_ _Hogares_ _(ENAHO)_ is the Peruvian version of the Living Standards Measurement Survey, e.g. a nationally representative household survey collected monthly on a continuous basis. For our analysis, we use data from January 2007 to December 2020. The survey covers a wide variety of topics, including basic demographics, educational back ground, labor market conditions, crime victimization, and a module on respondent's percep tions about the main problems in the country and trust on different local and national level institutions. Observations are also spatially identified at the municipality level, but here we focus on variation in the Venezuelan share of the population at the province level, of which there are 196, as these are best representative of local labor markets.\n\n_Latin_ _American_ _Public_ _Opinion_ _Project_ _(LAPOP)_ is a opinion survey conducted bi-annually in all countries in Latin America and designed to be representative of urban populations. This was fielded in Peru in 2010, 2012, 2014, 2017 and 2019 and consists of about 2,000 observations from mostly urban areas. The survey questions are centered around politics,", "output": {"entities": {"named_data": ["Life in Transition Survey"], "organization": ["OECD Publishing", "OECD"]}, "relations": [{"has_organization": {"head": "Life in Transition Survey", "tail": "OECD Publishing"}}, {"used_by": {"head": "Life in Transition Survey", "tail": "OECD"}}]}, "_meta": {"entry_id": 313, "dataset_name": "Life in Transition Survey"}}
+{"input": "Table 2 presents summary statistics for the ENILEMS-ENLACE panel dataset. Columns\n\n12 ENLACE scores than individuals out of college (by around 0.38 SD), but they are also\n\nWe are interested in the predictive power of ENLACE test scores over future schooling\n\nindividual's ENLACE test score in Grade 6 as a predictor of future education outcomes or\n\nWe use the ENLACE panel to study the relationship between Grade 6 test scores", "output": {"entities": {"named_data": ["ENILEMS-ENLACE panel dataset"], "organization": ["We"]}, "relations": [{"used_by": {"head": "ENILEMS-ENLACE panel dataset", "tail": "We"}}]}, "_meta": {"entry_id": 460, "dataset_name": "ENILEMS-ENLACE panel dataset"}}
+{"input": "using purchasing power parity rates. It comes from the World Development Indicators\n\nThe governance indicators come from the International Country Risk Guide (ICRG)\n\nData on the Gini coefficient were drawn from a more complete World Bank source\n\n100 km of ice-free coast) come from Gallup et al. (1999). Data on total forest area (km [2] ),\n\nprecipitation come from WDI (2010). Latitude (in absolute value), mean elevation (meters", "output": {"entities": {"named_data": ["World Development Indicators"], "organization": ["World Bank", "Gallup et al. (1999)"]}, "relations": [{"has_organization": {"head": "World Development Indicators", "tail": "World Bank"}}, {"used_by": {"head": "World Development Indicators", "tail": "Gallup et al. (1999)"}}]}, "_meta": {"entry_id": 65, "dataset_name": "World Development Indicators"}}
+{"input": "148 P. B. Spiegel & P. V. Le\n\nIntroduction\n\nThe human immunodeficiency virus (HIV) behavioural surveillance surveys\n(BSSs), an evolution from the knowledge-attitudes-practice surveys (KAPs), are\nan assessment, monitoring and evaluation tool designed to track trends in HIV/\nAIDS knowledge, attitudes and risk behaviour among populations. When used\ntogether with qualitative and quantitative research and proper measurement of\nappropriate programme indicators, the data collected from BSSs can assist\norganizations in targeting specific HIV/AIDS prevention and care activities,\nallocating scarce resources, and monitoring and evaluating the interventions’\neffectiveness and coverage. BSSs are useful because they alert policy makers and\nprogramme managers to emerging or changing risks in existing behaviour, reveal\ngaps in knowledge and attitudes, help to identify vulnerable segments of\npopulations, contribute to improved programme content, provide data on specific\ntarget groups and ensure compatibility and standardization of data collection\n(Family Health International 2000).\nThe core BSS indicators have been evolving over time (Table I). Until the\nUnited Nations General Assembly Special Session on HIV/AIDS (UNGASS)\nindicators were developed in 2002, there were no internationally-accepted HIV\nindicators. The UNGASS indicators were followed by the development of the\nMillennium Development Goal (MDG) indicators in 2003 and, subsequently,\nthe US President’s Emergency Preparedness Fund on AIDS Relief (PEPFAR)\nindicators in 2004. Although all of these indicators are similar to one another,\nthere are minor differences. Thus, it is difficult for persons implementing BSSs to\nchoose which indicators to use and complicated for others to compare studies\nwhich use different indicators. Furthermore, there are numerous other indicators\nthat can be used in BSSs depending upon the target groups and objectives of the\nsurvey.\nConflict, displacement, food insecurity and poverty have the potential to make\naffected populations more vulnerable to HIV transmission. The UNGASS\nDeclaration of Commitment on HIV/AIDS, states that ‘populations destabilised\nby armed conflict . . . including refugees, internally displaced persons, and in\nparticular women and children, are at increased risk of exposure to HIV infection’\n(United Nations General Assembly 2001). However, the common assumption\nthat this vulnerability necessarily translates into increased HIV infections and\nconsequently fuels the epidemic is not supported by data (Spiegel 2004). In the\nrecent past, HIV/AIDS interventions were generally not included by humanitarian organizations as part of their immediate response to conflict; HIV/AIDS was\nconsidered more of a developmental issue and not an immediate life threatening\ndisease such as malaria or cholera. However, thinking has evolved and it is now\ngenerally accepted that HIV/AIDS programmes must begin at the onset of a\nhumanitarian emergency, be multisectoral, and continue at every stage thereafter\n(Inter-Agency Standing Committee 2003). Furthermore, for refugees and\ninternally displaced persons (IDPs), HIV/AIDS programmes should be integrated", "output": {"entities": {"named_data": ["knowledge-attitudes-practice surveys (KAPs)"], "organization": ["Family Health International", "Inter-Agency Standing Committee"]}, "relations": [{"has_organization": {"head": "knowledge-attitudes-practice surveys (KAPs)", "tail": "Family Health International"}}, {"used_by": {"head": "knowledge-attitudes-practice surveys (KAPs)", "tail": "Inter-Agency Standing Committee"}}]}, "_meta": {"entry_id": 118, "dataset_name": "knowledge-attitudes-practice surveys (KAPs)"}}
+{"input": "The findings from the Conflict Assessment Report 2022 have been instrumental in guiding the efforts of various humanitarian organizations in conflict-ridden regions. Published by the International Crisis Group, this report provides critical insights into the dynamics of violence and instability, helping agencies like Save the Children and Oxfam tailor their interventions effectively. The data included not only highlights the immediate needs but also emphasizes longer-term strategies for peace and recovery.", "output": {"entities": {"named_data": ["Conflict Assessment Report 2022"], "organization": ["International Crisis Group", "Save the Children", "Oxfam"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Conflict Assessment Report 2022", "tail": "International Crisis Group"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "conflict, fragility, and violence"}}
+{"input": "The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father ’ s schooling, mother ’ s schooling, and local supply of schools in subdistrict of birth. 9 These exclusions resulted in a sample of 4, 139 males and 4, 131 females, which are referred to as the male and female full samples, respectively. 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts. 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA. 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8, 312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father ’ s schooling), and 7 observations (missing mother ’ s schooling).", "output": {"entities": {"named_data": ["JLMPS"], "organization": ["Ministry of Education"]}, "relations": [{"used_by": {"head": "JLMPS", "tail": "Ministry of Education"}}]}, "_meta": {"entry_id": 139, "dataset_name": "JLMPS"}}
+{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel.", "output": {"entities": {"named_data": ["EPR-ER"], "organization": ["we"]}, "relations": [{"used_by": {"head": "EPR-ER", "tail": "we"}}]}, "_meta": {"entry_id": 389, "dataset_name": "EPR-ER"}}
+{"input": "The Climate Adaptation Assessment Report published by the Global Resilience Partnership highlights various initiatives aimed at enhancing climate resilience in vulnerable regions. This comprehensive report draws on various datasets and methodologies to analyze the effectiveness of different strategies implemented across Africa. Collaborating organizations, including the United Nations Environment Programme, played a crucial role in the research and data collection for this report, ensuring a multi-faceted approach to disaster risk reduction.", "output": {"entities": {"named_data": ["Climate Adaptation Assessment Report"], "organization": ["Global Resilience Partnership", "United Nations Environment Programme"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Climate Adaptation Assessment Report", "tail": "Global Resilience Partnership"}}, {"used_by": {"head": "Climate Adaptation Assessment Report", "tail": "United Nations Environment Programme"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "The Domestic Revenue Mobilization Assessment (DRMA) for Malawi, which covers the years 2018 to 2020, provides insights into the effectiveness of fiscal policies and their impacts on revenue collection. This dataset, published by the African Development Bank, is particularly valuable for stakeholders looking to enhance local tax systems. Additionally, the Public Financial Management Survey (PFMS) for Kenya, conducted in 2021, aims to evaluate the efficiency of public spending and budget execution. While the PFMS has not yet been widely utilized in academic research, it serves as a crucial tool for policymakers. Lastly, the Global Tax Revenue Trends Report (GTRTR) 2019 focuses on comparative tax data across numerous countries, although its specific geographic emphasis was not well defined in the documentation.", "output": {"entities": {"named_data": ["Domestic Revenue Mobilization Assessment", "Public Financial Management Survey", "Global Tax Revenue Trends Report"], "organization": ["African Development Bank"], "acronym": ["DRMA", "PFMS", "GTRTR"], "year": ["2018 to 2020", "2021", "2019"], "geography": ["Malawi", "Kenya"]}, "relations": [{"has_acronym": {"head": "Domestic Revenue Mobilization Assessment", "tail": "DRMA"}}, {"has_timeframe": {"head": "Domestic Revenue Mobilization Assessment", "tail": "2018 to 2020"}}, {"has_geography": {"head": "Domestic Revenue Mobilization Assessment", "tail": "Malawi"}}, {"has_acronym": {"head": "Public Financial Management Survey", "tail": "PFMS"}}, {"has_timeframe": {"head": "Public Financial Management Survey", "tail": "2021"}}, {"has_geography": {"head": "Public Financial Management Survey", "tail": "Kenya"}}, {"has_acronym": {"head": "Global Tax Revenue Trends Report", "tail": "GTRTR"}}, {"has_timeframe": {"head": "Global Tax Revenue Trends Report", "tail": "2019"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "public financial management and domestic revenue"}}
+{"input": "The 2022 Maternal Health and Wellbeing Survey (MHWS) recently provided critical insights into maternal outcomes across various regions. This comprehensive study, conducted by the Institute for Global Health Research, spans several countries, including Nigeria, India, and Brazil, highlighting the disparities in healthcare access and outcomes. The findings from the MHWS are expected to inform policy recommendations aimed at improving maternal healthcare systems. Notably, while the survey covers the year 2022, it also includes longitudinal data from previous years to provide context for its findings. The survey serves as a vital resource for organizations working on maternal health initiatives, such as UNICEF and WHO.", "output": {"entities": {"named_data": ["Maternal Health and Wellbeing Survey"], "organization": ["Institute for Global Health Research", "UNICEF", "WHO"], "acronym": ["MHWS"], "year": ["2022"], "geography": ["Nigeria", "India", "Brazil"]}, "relations": [{"has_acronym": {"head": "Maternal Health and Wellbeing Survey", "tail": "MHWS"}}, {"has_timeframe": {"head": "MHWS", "tail": "2022"}}, {"has_geography": {"head": "MHWS", "tail": "Nigeria"}}, {"has_geography": {"head": "MHWS", "tail": "India"}}, {"has_geography": {"head": "MHWS", "tail": "Brazil"}}, {"has_organization": {"head": "MHWS", "tail": "Institute for Global Health Research"}}, {"used_by": {"head": "MHWS", "tail": "UNICEF"}}, {"used_by": {"head": "MHWS", "tail": "WHO"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "health systems and maternal outcomes"}}
+{"input": "13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset.", "output": {"entities": {"named_data": ["Uppsala / PRIO dataset"], "organization": ["Buhaug & Rød"]}, "relations": [{"used_by": {"head": "Uppsala / PRIO dataset", "tail": "Buhaug & Rød"}}]}, "_meta": {"entry_id": 1086, "dataset_name": "Uppsala / PRIO dataset"}}
+{"input": "likelihood of working outside the camp, or in monthly earnings. Agriculture is an important source of livelihood for host households, but refugee households have low agricultural holdings, reflecting their inability to own land legally. Refugee households are less than half as likely as hosts to report an agricultural holding with crops (19 percent versus 41 percent of host households, Figure 3.14). Refugee livestock ownership is similarly low (22 percent of households own livestock versus 48 percent for host households) but average livestock ownership is higher for Somali households (41 percent) (Figure 3.15). 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 15-24 25-34 35-44 45-54 55-64 Less than Primary Primary Secondary Figure 3.12: Share employed by age – camp refugees Source: World Bank Staff based on SESRE 2023. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 15-24 25-34 35-44 45-54 55-64 Less than Primary Primary Secondary Figure 3.13: Share employed by age – camp hosts Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 32 When refugees own livestock, the value and flock size of this livestock is low. Somali refugees mostly own sheep, goats, and donkeys, and compared to Somali hosts, they", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank Staff"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 638, "dataset_name": "SESRE 2023"}}
+{"input": "The Water Quality Assessment Report (WQA Report) outlines critical data on water quality indicators across various regions. This dataset, published by the Global Water Institute, covers the years 2018 to 2020 and provides insights into the sanitation efforts implemented in sub-Saharan Africa. The WQA Report (WQA) has been utilized extensively by local governments to inform policy decisions regarding water resources. In contrast, the Urban Sanitation Survey (USS) has been primarily focused on urban environments in Latin America, with data from the 2020 census. Although the USS is not as widely cited, it plays a crucial role in understanding urban sanitation challenges faced in the region.", "output": {"entities": {"named_data": ["Water Quality Assessment Report", "WQA Report", "Urban Sanitation Survey", "USS"], "organization": ["Global Water Institute", "local governments"], "acronym": ["WQA", "USS"], "year": ["2018 to 2020", "2020"], "geography": ["sub-Saharan Africa", "Latin America"]}, "relations": [{"has_acronym": {"head": "Water Quality Assessment Report", "tail": "WQA"}}, {"has_timeframe": {"head": "WQA Report", "tail": "2018 to 2020"}}, {"has_geography": {"head": "WQA Report", "tail": "sub-Saharan Africa"}}, {"has_organization": {"head": "WQA Report", "tail": "Global Water Institute"}}, {"used_by": {"head": "WQA Report", "tail": "local governments"}}, {"has_acronym": {"head": "Urban Sanitation Survey", "tail": "USS"}}, {"has_timeframe": {"head": "Urban Sanitation Survey", "tail": "2020"}}, {"has_geography": {"head": "Urban Sanitation Survey", "tail": "Latin America"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "water, sanitation, and hygiene"}}
+{"input": "In recent analyses of economic resilience, the Global Financial Inclusion Index (GFII) provides key insights into the accessibility of financial services across various demographics. Covering the period from 2020 to 2022, this dataset highlights trends in financial participation in regions facing economic challenges, including sub-Saharan Africa and South Asia. The research conducted by the Financial Observatory emphasizes the importance of these findings in shaping policy initiatives aimed at enhancing economic stability and inclusive growth.", "output": {"entities": {"named_data": ["Global Financial Inclusion Index"], "organization": ["Financial Observatory"], "acronym": ["GFII"], "year": ["2020 to 2022"], "geography": ["sub-Saharan Africa", "South Asia"]}, "relations": [{"has_acronym": {"head": "Global Financial Inclusion Index", "tail": "GFII"}}, {"has_timeframe": {"head": "Global Financial Inclusion Index", "tail": "2020 to 2022"}}, {"has_geography": {"head": "Global Financial Inclusion Index", "tail": "sub-Saharan Africa"}}, {"has_geography": {"head": "Global Financial Inclusion Index", "tail": "South Asia"}}, {"used_by": {"head": "Global Financial Inclusion Index", "tail": "Financial Observatory"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "have yet to adapt internal procedures. Some banks may still require fixed addresses as part of their due diligence, which recently-arrived refugees often do not have.\n\nWhen it comes to access to credit provided by microfinance institutions (MFIs), regulatory requirements can be less restrictive. Several MFIs [71] in a number of countries such as Jordan, Lebanon, Uganda, Rwanda, Morocco, Tunisia, and Argentina have started providing loans to refugees by accepting UNHCR ID or the ID issued by governments to refugees, using alternative ways to verify residence and credit scoring. What has proven to be a key enabler to facilitate access to these services was the work done by UNHCR and partners to raise awareness about the financial needs of refugees with FSPs in the country. This advocacy work is fundamental and could be replicated in other countries to ensure greater access to a broader range of financial services by refugees.\n\n_**Access to Mobile Money**_ To what extent does the regulatory environment facilitate or constrain access to mobile money?\n\nAs with SIM registration, of the 20 countries examined, none extend consistent legal access to mobile money to asylum seekers, though workarounds are regularly reported.\nHowever, in late 2018 the Afghan Central Bank reportedly authorized certain mobile money operators to accept any letter or other form of certification from UNHCR (including the Voluntary Repatriation Form and asylum seeker and refugee certificates) to facilitate SIM registration for mobile money services as part of a cash-based intervention program.\n\nAccess to mobile money is varied for refugees across the countries studied. In Zambia, special approval was received from the Bank of Zambia and ZICTA, the telecommunications regulator, to use the proof of registration, refugee certificate and refugee ID card as valid ID for mobile wallet registration. In Nigeria, the regulatory framework is particularly progressive and accommodating.\n\nNigeria's mobile money framework based on a three-tier KYC/CDD regulation, the lowest level of which (Level 1) is particularly inclusive and, in theory, can accommodate the displaced (see Table 3).\n\n**Case study: Rwanda**\n\nAccording to the World Bank's Global Findex database, Rwanda's mobile money penetration (in terms of account ownership for 15+ years old) is 31.11%. The National Bank of Rwanda has enacted Regulation No. 08/2016 Governing the Electronic Money Issuers [72], which reiterates the core ID 71 Pistelli, _5 Things You Should Know About Financial Services for Refugees_ : http://www.findevgateway.org/blog/2018/jun/5-things-you-should-know-about-financial-services-refugees 72 JuriAfrica, _The National Bank Regulates the Activities of Issuers of Electronic Money_ : https://juriafrique.com/ eng/2017/01/27/rwanda-national-bank-the-governor-enacts-new-standards-for-issuers-of-electronic-money/\n\n**Level 1** _**Low Value Accounts**_\n\ni. They are subject to close monitoring by the financial institutions and less scrutiny by Bank Examiners.\n\nii. The accounts can be opened at branches of financial institutions by the prospective customer or through banking agents.\n\niii. No amount is required for opening of accounts iv. Such accounts cover Mobile Banking Products (issued in accordance with the CBN Regulatory Framework for Mobile Payments Services in Nigeria).\n\n_**Main Characteristics**_ i. Deposits can be made by account holder and 3rd parties while withdrawal is restricted to account holder only.\n\nii. Be linked to mobile phone accounts.\n\niii. Operation is valid only in Nigeria.\n\niv. Automated Teller Machine transactions are allowed.\n\nv. There is Prohibition on International Funds Transfer.\n\nvi. Accounts are strictly savings.\n\n73 IGC, _The Regulation of Mobile Money in Rwanda_, p.15 requirements for opening a bank account. \"[Mobile Network Operators] are required to respect KYC rules prior to opening accounts — in practice a national ID card is required to register for mobile money.\" [73] As such, refugees continue to face barriers to accessing mobile money.\nMobile money agents from different operators are present in all camps in the country. However, while cash assistance to returnees is still based on mobile money transfers, UNHCR and WFP no longer use mobile money, but rather smart cards, for cash assistance in refugee settings due to challenges with SIM access and meeting KYC/CDD requirements.\n\n_Table 3: Tier 1 of Nigeria’s Three-Tiered KYC Requirements (including Mobile Money)_\n\n**Description and characteristics** **Amount / Threshold** **Customer**\n**Limitation** **Identification**\n**requirements**\n\ni. Basic customer information required to be provided are:\n\n- Passport photo;\n\n- Name, Place and\nDate of Birth;\n\n- Gender, Address,\nTelephone number, etc ii. Information may be sent electronically or submitted onsite in bank’s branches or agent’s office iii. Evidence of the Information provided by customer or verification of same is not required.\n\nIt is limited to a minimum single deposit amount of N20,000 and maximum cumulative balance of N200,000 at any point in time.\n\n_**Mobile Banking**_ _**Products**_ Level One Mobile Banking Products are Allowed:\n\n- Maximum\ntransaction limit of N3,000 and daily limit of N30,000.\n\n- Such products\nare subject to the CBN Regulatory Framework for Mobile Payments Services in Nigeria.\n\n27 28", "output": {"entities": {"named_data": ["Global Findex database"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "Global Findex database", "tail": "World Bank"}}]}, "_meta": {"entry_id": 841, "dataset_name": "Global Findex database"}}
+{"input": "The Agricultural Monitoring Survey (AMS) conducted by the Food and Agriculture Organization (FAO) in 2022 provides critical insights into rural development challenges. This dataset has been extensively utilized by the International Fund for Agricultural Development (IFAD) to enhance its projects aimed at food security across Sub-Saharan Africa. Additionally, the Global Hunger Index (GHI) 2021, published by Concern Worldwide, offers a comprehensive assessment of hunger levels globally and has been cited in various reports by the World Food Programme (WFP). The Nutritional Outcomes Assessment 2019 (NOA2019) by the World Health Organization (WHO) focuses on nutrition and health indicators and is a valuable resource for local governments in Southeast Asia for policy formulation.", "output": {"entities": {"named_data": ["Agricultural Monitoring Survey", "Global Hunger Index", "Nutritional Outcomes Assessment 2019"], "organization": ["Food and Agriculture Organization", "International Fund for Agricultural Development", "Concern Worldwide", "World Food Programme", "World Health Organization"], "acronym": ["AMS", "GHI", "NOA2019"], "year": ["2022", "2021", "2019"], "geography": ["Sub-Saharan Africa", "globally", "Southeast Asia"]}, "relations": [{"has_organization": {"head": "Agricultural Monitoring Survey", "tail": "Food and Agriculture Organization"}}, {"used_by": {"head": "Agricultural Monitoring Survey", "tail": "International Fund for Agricultural Development"}}, {"has_organization": {"head": "Global Hunger Index", "tail": "Concern Worldwide"}}, {"used_by": {"head": "Global Hunger Index", "tail": "World Food Programme"}}, {"has_organization": {"head": "Nutritional Outcomes Assessment 2019", "tail": "World Health Organization"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "agriculture, food security, and nutrition"}}
+{"input": "The 2022 Gender Equality Index (GEI) compiled by the International Institute for Women, highlights significant disparities in women's participation in the labor force across various regions. In particular, the report details findings from the Africa Women's Empowerment Survey (AWES) conducted in 2021, which was utilized by the United Nations Development Programme (UNDP) to inform policy recommendations. Additionally, the Asia-Pacific Gender Report (APGR) 2023, published by the Asian Development Bank (ADB), has served as a critical resource for NGOs working on gender issues in Southeast Asia. Furthermore, the Global Female Labor Participation Dataset (GFLPD) 2019, developed by the World Bank, provides comprehensive statistics that have been referenced by several academic institutions for research purposes.", "output": {"entities": {"named_data": ["Gender Equality Index", "Africa Women's Empowerment Survey", "Asia-Pacific Gender Report", "Global Female Labor Participation Dataset"], "organization": ["International Institute for Women", "United Nations Development Programme", "Asian Development Bank", "World Bank"], "acronym": ["GEI", "AWES", "APGR", "GFLPD"], "year": ["2022", "2021", "2023", "2019"], "geography": ["Africa", "Southeast Asia", "Asia-Pacific"]}, "relations": [{"has_organization": {"head": "Gender Equality Index", "tail": "International Institute for Women"}}, {"used_by": {"head": "Africa Women's Empowerment Survey", "tail": "United Nations Development Programme"}}, {"has_organization": {"head": "Asia-Pacific Gender Report", "tail": "Asian Development Bank"}}, {"used_by": {"head": "Global Female Labor Participation Dataset", "tail": "academic institutions"}}, {"has_acronym": {"head": "Gender Equality Index", "tail": "GEI"}}, {"has_timeframe": {"head": "Gender Equality Index", "tail": "2022"}}, {"has_timeframe": {"head": "Africa Women's Empowerment Survey", "tail": "2021"}}, {"has_timeframe": {"head": "Asia-Pacific Gender Report", "tail": "2023"}}, {"has_timeframe": {"head": "Global Female Labor Participation Dataset", "tail": "2019"}}, {"has_geography": {"head": "Africa Women's Empowerment Survey", "tail": "Africa"}}, {"has_geography": {"head": "Asia-Pacific Gender Report", "tail": "Asia-Pacific"}}, {"has_geography": {"head": "Global Female Labor Participation Dataset", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "gender equality and women's economic empowerment"}}
+{"input": "of Return Migration, Human Capital Accumulation, and Wage Assimilation. The Review of Economic Studies, 89(6), 2841-2871. Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., and Sanghi, A. (2018). Do Refugee Camps Help or Hurt Hosts? The Case of Kakuma, Kenya. Journal of Development Economics, 130, 66-83. Alkire, S., Kanagaratman, U., and Suppa, N. (2021). The Global Multidimensional Poverty Index (MPI) 2021, OPHI MPI Methodological Note 51. Oxford Poverty and Human Development Initiative, University of Oxford. Altindag, O., Bakls, O., and Rozo, M. S. (2020). Blessing or Burden? Impacts of Refugees on Businesses and the Informal Economy. Journal of Development Economics, 146. https://doi.org/10.1016/j.jdeveco.2020.102490 Andersen, H.L., Osland, L. and Zhang, M.L. (2023). Labour Market Integration of Refugees and The Importance of the Neighborhood: Norwegian Quasi-experimental Evidence. Journal of Labour Market Research 57, 16. https://doi.org/10.1186/ s12651-023-00341-y Aracl, D., Demirci, M., and Klrdar, M. (2022). Development Level of Hosting Areas and the Impact of Refugees on Natives’ Labor Market Outcomes in Turkey. European Economic Review, 145. https://doi.org/10.1016/j.euroecorev.2022.104132 Atamanov, A., Hoogeveen, J., and Reese, B. (2023). The Costs Come Before the Benefits Follow. Why Should Donors Invest More in Refugee Autonomy in Uganda? 1–11. Azlor, L., Damm, A. P., and Schultz-Nielsen, M. L. (2020). Local", "output": {"entities": {"named_data": ["Global Multidimensional Poverty Index (MPI) 2021"], "organization": ["Oxford Poverty and Human Development Initiative"]}, "relations": [{"has_organization": {"head": "Global Multidimensional Poverty Index (MPI) 2021", "tail": "Oxford Poverty and Human Development Initiative"}}]}, "_meta": {"entry_id": 103, "dataset_name": "Global Multidimensional Poverty Index (MPI) 2021"}}
+{"input": "Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka and\n\nGuillaume (2018a). The data are primarily based on sub-national GDP per capita data constructed by Gennaioli,\n\nIs the relationship between rainfall and GDP explained by agriculture? We use data from the ESA CCI project\n\n\nto determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split\n\ndifferent weights. Population is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).\n\nThe work compares food prices subnationally and finds that increasing prices are the most significant factor driving recent food insecurity but documents strong spatial heterogeneity in both market price dynamics, as well as relations to food insecurity, and provides examples of markets where prices are more sensitive to localized shocks. 4For instance, the International Finance Statistics (IFS) data base of the IMF reports monthly price data at Consumer Price Index (CPI) component level, but few developing countries report food price data without substantial delays.", "output": {"entities": {"named_data": ["ESA CCI project"], "organization": ["we"]}, "relations": [{"used_by": {"head": "ESA CCI project", "tail": "we"}}]}, "_meta": {"entry_id": 164, "dataset_name": "ESA CCI project"}}
+{"input": "Consequently, they may fear detection by authorities when asked to respond to surveys or participate in public initiatives. 1 Furthermore, collective repre- sentative and longitudinal data on forcibly displaced migrants, a population with high mobility rates, is difficult and costly (Ib ´ a ˜ nez et al. 2024). This complexity is compounded when focusing on children and adolescents, given the need for enumerators to receive specific training to interact with such a vulnerable demographic and for migrant parents to authorize their children ’ s involvement despite prevailing distrust issues. To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ´ ın, Colombia. VenRePs-Kids is a longitudinal study repre- sentative of forcibly displaced Venezuelan and Colombian children and adolescents aged 5 to 17. To our knowledge, it is the first study to gather panel data specifically on forcibly 1This concern is also prevalent among undocumented migrants in the United States, as highlighted by Amuedo-Dorantes and Lopez (2015).", "output": {"entities": {"named_data": ["VenRePs-Kids"], "organization": ["Venezuelan Refugee Panel Study for Kids (VenRePs-Kids)"]}, "relations": [{"has_organization": {"head": "VenRePs-Kids", "tail": "Venezuelan Refugee Panel Study for Kids (VenRePs-Kids)"}}]}, "_meta": {"entry_id": 154, "dataset_name": "VenRePs-Kids"}}
+{"input": "The United Nations Economic Commission for Europe ’ s (UNECE) guidelines include a question on reason for migration, population with a refugee-like background and IDPs as non-core topics / questions (UNHCR 2016). 72 While most countries include questions on country of birth and citizenship, only about 40 percent include a question on year of migration, less than a quarter include a question on reason for international migration, and about a fifth include a question on the reason for internal migration (UNHCR 2016). 73 E. g. Kyrgyz Republic 1999 (refugee status), West Bank and Gaza 2007 (refugee status), Zambia 2000 and 2010 (purpose of stay), Germany 1970 (federal refugee identity card), Greece 2001 (reason for settling in Greece), Sudan and South Sudan 2008 (type of household including IDP and refugee), Liberia 1990 (ever displaced by war since 1990), Uganda 2014 (refugees). 74 UNHCR is collaborating with the Statistics Norway on systematically embedding forcibly displaced peoples in national statistics exercises and collaborates with national authorities and with UNFPA in various countries on the design of census exercises that include refugees, IDPs, returnees and stateless persons. 75 LSMS is a household survey program housed in the Bank's Development Research Group that provides technical assistance to national statistical offices in the design and implementation of multi-topic household surveys covering household behavior, welfare and interactions with government policies. All data gathered through the LSMS is published online in the Bank ’ s Central Microdata Catalog.", "output": {"entities": {"named_data": ["LSMS"], "organization": ["Bank", "UNHCR"]}, "relations": [{"has_organization": {"head": "LSMS", "tail": "Bank"}}, {"used_by": {"head": "LSMS", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 940, "dataset_name": "LSMS"}}
+{"input": "The recent employment trends in South Asia were analyzed using the Workforce Participation Database (WPD), published by the International Labour Organization (ILO). This comprehensive dataset, covering the years 2018 to 2022, has been particularly useful for regional assessments conducted by the Asian Development Bank (ADB). Additionally, the Skills Development Report 2021, produced by the World Bank, has provided insights into the effectiveness of vocational training programs in Bangladesh. This report has been cited extensively by various NGOs working on labor market interventions across the country. Furthermore, the ILO's Labour Force Survey (LFS) data, spanning 2020 to 2021, has also been utilized by the United Nations Development Programme (UNDP) for its ongoing projects aimed at improving employment outcomes in Nepal.", "output": {"entities": {"named_data": ["Workforce Participation Database", "Skills Development Report 2021", "Labour Force Survey"], "organization": ["International Labour Organization", "Asian Development Bank", "World Bank", "United Nations Development Programme"], "acronym": ["WPD", "ADB", "ILO", "LFS", "UNDP"], "year": ["2018 to 2022", "2021", "2020 to 2021"], "geography": ["South Asia", "Bangladesh", "Nepal"]}, "relations": [{"has_organization": {"head": "Workforce Participation Database", "tail": "International Labour Organization"}}, {"used_by": {"head": "Workforce Participation Database", "tail": "Asian Development Bank"}}, {"has_timeframe": {"head": "Workforce Participation Database", "tail": "2018 to 2022"}}, {"has_organization": {"head": "Skills Development Report 2021", "tail": "World Bank"}}, {"used_by": {"head": "Skills Development Report 2021", "tail": "various NGOs"}}, {"has_geography": {"head": "Skills Development Report 2021", "tail": "Bangladesh"}}, {"has_organization": {"head": "Labour Force Survey", "tail": "International Labour Organization"}}, {"used_by": {"head": "Labour Force Survey", "tail": "United Nations Development Programme"}}, {"has_timeframe": {"head": "Labour Force Survey", "tail": "2020 to 2021"}}, {"has_geography": {"head": "Labour Force Survey", "tail": "Nepal"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "employment, labor markets, and skills development"}}
+{"input": "The 2020 Global Land Use Assessment (GLUA) compiled by the EcoData Institute provides crucial insights into land cover changes over the past decade. This dataset, which focuses on multiple countries including Brazil and Indonesia, is instrumental for policymakers. The World Resources Organization has utilized the GLUA data to inform its regional sustainability initiatives, particularly in the Amazon Rainforest area. Furthermore, these findings will assist in shaping future conservation strategies as detailed in the 2021 Biodiversity Report published by the EcoData Institute. This report outlines key trends in biodiversity loss related to land use modifications, providing a comprehensive overview for various stakeholders.", "output": {"entities": {"named_data": ["Global Land Use Assessment", "Biodiversity Report"], "organization": ["EcoData Institute", "World Resources Organization"], "acronym": ["GLUA"], "year": ["2020", "2021"], "geography": ["Brazil", "Indonesia", "Amazon Rainforest"]}, "relations": [{"has_organization": {"head": "Global Land Use Assessment", "tail": "EcoData Institute"}}, {"used_by": {"head": "Global Land Use Assessment", "tail": "World Resources Organization"}}, {"has_acronym": {"head": "Global Land Use Assessment", "tail": "GLUA"}}, {"has_timeframe": {"head": "Global Land Use Assessment", "tail": "2020"}}, {"has_geography": {"head": "Global Land Use Assessment", "tail": "Brazil"}}, {"has_geography": {"head": "Global Land Use Assessment", "tail": "Indonesia"}}, {"has_organization": {"head": "Biodiversity Report", "tail": "EcoData Institute"}}, {"has_timeframe": {"head": "Biodiversity Report", "tail": "2021"}}, {"has_geography": {"head": "Biodiversity Report", "tail": "Amazon Rainforest"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "The Fertility Trends Report 2020, published by the National Demographic Institute (NDI), provides comprehensive insights into reproductive health across various regions. This data has been utilized extensively by the International Population Council (IPC) to inform policies aimed at improving maternal health. Additionally, the African Population Database (APD), which covers demographic data from 2015 to 2020, has also been leveraged by the World Health Organization (WHO) in their ongoing research into population growth dynamics in African nations. These datasets are crucial for understanding the shifts in fertility patterns and their implications for development strategies.", "output": {"entities": {"named_data": ["Fertility Trends Report 2020", "African Population Database"], "organization": ["National Demographic Institute", "International Population Council", "World Health Organization"], "acronym": ["NDI", "IPC", "APD"], "year": ["2020", "2015 to 2020"], "geography": ["African nations"]}, "relations": [{"has_organization": {"head": "Fertility Trends Report 2020", "tail": "National Demographic Institute"}}, {"used_by": {"head": "Fertility Trends Report 2020", "tail": "International Population Council"}}, {"has_acronym": {"head": "Fertility Trends Report 2020", "tail": "NDI"}}, {"has_timeframe": {"head": "African Population Database", "tail": "2015 to 2020"}}, {"has_organization": {"head": "African Population Database", "tail": "World Health Organization"}}, {"has_acronym": {"head": "African Population Database", "tail": "APD"}}, {"has_geography": {"head": "African Population Database", "tail": "African nations"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "demographics, fertility, and population growth"}}
+{"input": "We use the sum of conflict events occurring in the historic homeland of ethnic group e in the previous year t − 1, denoted as Conflictet − 1, and we use the mean distance between the historic homeland of ethnic group e and the border of country d to predict the number of refugees of a certain ethnic group e moving from country o to d at time t. 22 In order to be consistent with EPR-ER data construction, we restrict our analysis to all origin – destination country pairs that are at a maximum distance ≤ 950 km from each other. Predicted numbers of refugees are then transformed into predicted shares for the three largest groups to follow the logic used by the EPR-ER dataset. We then plug in these predicted shares in the following way: X \\PredictedRefcet = Refocdt ∗ \\Shareodet. (6) The predicted shares of refugees per camp c are then used to compute (as documented above) refugee diversity indices to be used as instrumental variables. The first-stage equations corresponding to the 2SLS-equivalent of Equation 1 can be expressed as 20We conduct a robustness check on Equation 5, replacing the dyadic origin – destination fixed effects with separate origin and destination fixed effects (Section 5. 4). 21More information on LEDA can be found in Appendix A. 1. 22The construction of the IV follows a long tradition in using the gravity model to predict bilateral migration flows (Ravenstein, 1985, 1989; Crozet, 2004; Mayda, 2010; Garcia et al., 2015; Beine et al., 2016). In our analysis, a major difference is that we have an additional dimension: the ethnic group e.", "output": {"entities": {"named_data": ["EPR-ER data"], "organization": ["EPR-ER dataset", "We"]}, "relations": [{"has_organization": {"head": "EPR-ER data", "tail": "EPR-ER dataset"}}, {"used_by": {"head": "EPR-ER data", "tail": "We"}}]}, "_meta": {"entry_id": 429, "dataset_name": "EPR-ER data"}}
+{"input": "supervisor, and four enumerators. All field staff involved in the SERSE participated in the HoWStat survey. Enumerators were knowledgeable about local cultures and languages and could detect inconsistencies and misunderstandings during interviews to ensure high-quality data. Supervisors were additionally trained on how to troubleshoot standard technical issues with tablets. The supervisors conducted reinterviews, consistency, spot-checking, and data syncing to the head office. Also, the statisticians from ESS branch offices were with the team all the time to support and monitor the fieldwork. The data collection system consisted of encrypted Android devices for prolonged usage in the field equipped with the chosen survey application and a GPS tracking application for EA delineations. Electronic data files were transferred daily to the ESS central office in Addis Ababa via the Annexes 96 secured link. The core team from ESS undertook field supervision and was responsible for the day-to- day field management. Also, the World Bank team undertook field supervision, providing on-time and on-the-spot guidance for the field teams whenever and wherever they encountered a challenge. (e) Challenges faced and lessons learned The SESRE served as a learning experience for including refugees in future rounds of the official household survey (HoWStat). Given the unique", "output": {"entities": {"named_data": ["HoWStat survey"], "organization": ["HoWStat", "World Bank"]}, "relations": [{"has_organization": {"head": "HoWStat survey", "tail": "HoWStat"}}, {"used_by": {"head": "HoWStat survey", "tail": "World Bank"}}]}, "_meta": {"entry_id": 542, "dataset_name": "HoWStat survey"}}
+{"input": "Recent investigations into gender equality have highlighted significant barriers faced by women in the workforce. The Gender Equity Assessment Report 2022 provides extensive data on wage disparities and employment rates among different demographics. Additionally, the Women’s Empowerment Index examines various indicators related to women's participation in economic activities, offering critical insights into the challenges that remain.", "output": {"entities": {"named_data": ["Gender Equity Assessment Report 2022", "Women’s Empowerment Index"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
+{"input": "The Environmental Sustainability Assessment Report (ESAR) reveals significant trends in natural resource management across various regions. Conducted in 2022, this comprehensive analysis spans several countries, with a focus on resource depletion and conservation strategies. It highlights the importance of sustainable practices in combating climate change. While the data demonstrates promising initiatives in regions like Southeast Asia, it also points to concerning trends in resource use in parts of Africa. Policymakers are encouraged to utilize the findings from the ESAR to drive effective environmental policies that respond to the challenges identified in the report.", "output": {"entities": {"named_data": ["Environmental Sustainability Assessment Report"], "organization": [], "acronym": ["ESAR"], "year": ["2022"], "geography": ["Southeast Asia", "Africa"]}, "relations": [{"has_acronym": {"head": "Environmental Sustainability Assessment Report", "tail": "ESAR"}}, {"has_timeframe": {"head": "Environmental Sustainability Assessment Report", "tail": "2022"}}, {"has_geography": {"head": "Environmental Sustainability Assessment Report", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Environmental Sustainability Assessment Report", "tail": "Africa"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
+{"input": "The Financial Inclusion Assessment Report 2022, produced by the Global Finance Institute, provides a comprehensive overview of access to banking services in low-income regions. This dataset, utilized extensively by the United Nations Development Programme (UNDP), highlights the trends in financial accessibility from 2018 to 2022. In addition, the Economic Growth Database (EGD), released by the International Economic Agency, contains valuable macroeconomic indicators for developing countries, which are often referenced by various financial organizations to gauge economic stability. The UNDP has also made use of the EGD data to support policy recommendations aimed at enhancing financial inclusion efforts across multiple geographies.", "output": {"entities": {"named_data": ["Financial Inclusion Assessment Report 2022", "Economic Growth Database"], "organization": ["Global Finance Institute", "United Nations Development Programme", "International Economic Agency"], "acronym": ["EGD"], "year": ["2022", "2018 to 2022"], "geography": ["low-income regions", "developing countries"]}, "relations": [{"has_organization": {"head": "Financial Inclusion Assessment Report 2022", "tail": "Global Finance Institute"}}, {"used_by": {"head": "Financial Inclusion Assessment Report 2022", "tail": "United Nations Development Programme"}}, {"has_timeframe": {"head": "Financial Inclusion Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Financial Inclusion Assessment Report 2022", "tail": "2018 to 2022"}}, {"has_organization": {"head": "Economic Growth Database", "tail": "International Economic Agency"}}, {"used_by": {"head": "Economic Growth Database", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Economic Growth Database", "tail": "EGD"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "These data are only for three districts in the province of Punjab, but is very recent, was conducted by an independent team of academics, and is a complete census of all households in the selected villages. Consequently, it yields sufficient madrassa enrollment to examine correlations with household attributes in a meaningful manner (this data source provides information on four times as many children as the PIHS). Table A2 in the appendix shows how these different data sources are used in the paper. Each source asks about madrassa enrollment in a slightly different but comparable way. The population census (1998) asks about the field-of-education (“ What is name ’ s field of education? ”) with options that include (for instance) engineering, medicine, or religious education. This question is also asked of all literate adults irrespective of their current enrollment status, allowing for comparisons in the stock of religious education over time. The PIHS rounds ask, “ What type of school is name currently attending? ” with options that include government school, private school, or deeni-madrassa (religious schooling). Finally, the LEAPS census directly asks, “ Is the child enrolled in a madrassa or an Islamic education school? ” Fortunately these different questions all give rise to similar numbers. This is reassuring since it suggests that any one particular result is not driven by the specific question or definition that was used. 7 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 (www. statpak. gov. pk).", "output": {"entities": {"named_data": ["LEAPS census"], "organization": ["Federal Bureau of Statistics"]}, "relations": [{"has_organization": {"head": "LEAPS census", "tail": "Federal Bureau of Statistics"}}]}, "_meta": {"entry_id": 13, "dataset_name": "LEAPS census"}}
+{"input": "0 10 20 30 40 50 60 70 80 90 100 Percent Worse Same Better Figure 5.8: Perceived changes in household living standards Source: World Bank Staff based on SESRE 2023. Note: The survey asks how the household living standard has changed compared to last year and the last five years. a. Last 5 years b. Last 1 year Refugees’ Aspirations 48 scale44 gap between refugees and hosts is higher for in-camp refugees, the gap is relatively narrower for Addis Ababa refugees. The average food insecurity scale for in-camp refugees is “8” and for their hosts it is “4” out of 10; that is, in- camp refugee households experienced about eight food insecurity events while host households experienced about 4 in the past year. Consistent with other welfare indicators discussed, food insecurity tends to be more severe among in-camp refugees than their hosts or OCP refugees. In-camp refugees have less diverse diets and poor food consumption status compared to their hosts. The average household dietary diversity score—the number of food groups consumed out of twelve—is 7.5 for hosts and 6.5 for refugees (Figure 5.10a). Overall, the average dietary diversity score is also lower for in-camp refugees than their hosts. The", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "SESRE 2023", "tail": "World Bank"}}]}, "_meta": {"entry_id": 337, "dataset_name": "SESRE 2023"}}
+{"input": "restricted to workers with completed secondary education. Among workers with completed secondary, 30 percent of refugees and 50 percent of hosts are in high-skill occupations (Figure 3.25). Among only women, these numbers are 20 percent and 50 percent, respectively. Instead, refugee men and women in Addis Ababa are over- represented in crafts and related trades (typically classified as medium-skill occupations). 0 20 40 60 80 100 Addis Hosts Addis Refugees Current Addis Refugees COB Salary (employment/casual labor) Crops/livestock Donations(NGO/gov) Remittances (local/international) Other (rental income, PSNP, pension) Percent 0 20 40 60 80 100 Percent Addis Male-Headed Addis Female-Headed Figure 3.21: Household primary income source Source: World Bank Staff based on SESRE 2023. Note: “COB” refers to livelihood strategies in their country of birth. a. Pre-post migration b. By gender of head Table 3.2: Labor force statistics Addis Hosts Addis Hosts Labor force participation rate (strict) 66% 46% Unemployment rate (strict) 12% 63% Labor force participation rate (relaxed) 72% 67% Unemployment (relaxed) 19% 75% Employment-to-population ratio 58% 17% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed or unemployed. Unemployment is the", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank Staff"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 563, "dataset_name": "SESRE 2023"}}
+{"input": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n**Recently arrived Ukrainian refugees,**\n\n**those with below tertiary education**\n\n**and in older age group are most**\n\n**likely to communicate in Polish at**\n\n**intermediate and lower levels.** Based\non the SEIS UNHCR survey, a logistic regression has been performed to find out which categories of Ukrainian refugees may most require Polish language improvement. [29] Results show that the odds of only zero to intermediate Polish knowledge decrease with every month since arrival. Ukrainian refugees in the 18 to 29 age group have the lowest odds of having an intermediate or lower level of Polish. It translates into a 38% probability, even lower than the 41% for refugees with tertiary education. The group with the highest odds (70% probability) of zero to intermediate Polish are Ukrainian refugees aged 50 to 64. The results by employment sectors are not statistically significant, other than for manufacturing. The results are intuitive, with the best language skills among refugees working in health and education, and the lowest among those working in construction, other services, and trade.\n\n**Addressing the gap in language**\n\n**fluency would yield significant**\n\n---\n[29] A dummy variable that takes the value of 1 in case of none, beginner, or intermediate language knowledge, and 0 for other levels has been regressed against a", "output": {"entities": {"named_data": ["SEIS UNHCR survey"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "SEIS UNHCR survey", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 1285, "dataset_name": "SEIS UNHCR survey"}}
+{"input": "The Food Security Assessment Report 2022, published by the International Food Policy Research Institute (IFPRI), provides critical insights into agricultural productivity across Southeast Asia. This dataset has been extensively used by various NGOs, including Action Against Hunger, to develop targeted interventions for improving food security in the region. Additionally, the report highlights trends in food availability and access, outlining significant changes over the past five years. While the data encompasses several countries, the assessment specifically details conditions in Vietnam and Thailand, which have faced unique challenges in food distribution. The collaborative efforts between IFPRI and action-oriented organizations exemplify the dynamic use of agricultural data for policy-making and humanitarian responses.", "output": {"entities": {"named_data": ["Food Security Assessment Report 2022"], "organization": ["International Food Policy Research Institute", "Action Against Hunger"], "acronym": ["IFPRI"], "year": ["2022", "five years"], "geography": ["Southeast Asia", "Vietnam", "Thailand"]}, "relations": [{"has_organization": {"head": "Food Security Assessment Report 2022", "tail": "International Food Policy Research Institute"}}, {"used_by": {"head": "Food Security Assessment Report 2022", "tail": "Action Against Hunger"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "agriculture, food security, and nutrition"}}
+{"input": "Several of the refugees interviewed for this research indicated that no one wanted to give them\nwork because of their status as a refugee in Cameroon. When discussing the treatment they\nreceived from the host population and the ability to find a job, all refugees interviewed explained\nthat it was difficult for them “because I am a stranger”, “from the outside” or because “I am\ndifferent”.\n\nAlthough there are many similarities between the refugee population and the host community,\nthere is also a clear distinction that exists; all refugee interviewed indicated that they experienced\nsome sort of harassment and/or discrimination because they were refugees, and most connected\nthis directly to their ability to find wage-earning work in Yaoundé. However, several refugees\nstated that the discrimination they face is not experienced with all Cameroonians, in some cases\nthey indicated that the locals supported them and treated them well; simply put by one refugee\nfrom CAR, “some Cameroonians are nice, some are not.”\n\nAs discussed previously, identity documentation is a big problem for refugees living in\nCameroon, as many authorities and institutions do not recognise UNHCR identity cards and", "output": {"entities": {"named_data": ["UNHCR identity cards"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR identity cards", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 262, "dataset_name": "UNHCR identity cards"}}
+{"input": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n# **2.** Situation of refugees from Ukraine on the labour market in Poland\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n**Chart 7.** Unemployment rate\n\n12% 10% 8% 6% 4% 2% 0%\n\n**Source:** Harmonized data, Eurostat, [Statistics | Eurostat (europa.eu)](https://ec.europa.eu/eurostat/databrowser/view/une_rt_m/default/table?lang=en)\n\n##### Refugees from Ukraine fit well into the needs of the Polish labour market.\n\nUkrainians arrived on a labour market that structurally needs more workers, as the domestic population is ageing rapidly, while the economy is growing. High levels of education, cultural proximity and previous connections to Poland helped refugees adapt to the labour market. Furthermore, Poland made an important and strategic policy decision by promptly opening the labour market and supporting their inclusion.\n\n16 17\n\n**Chart 6.** Working age population with Polish citizenship (20-64 years old)\n\n25 24 23 22 21 20", "output": {"entities": {"named_data": ["Harmonized data, Eurostat"], "organization": ["Eurostat"]}, "relations": [{"has_organization": {"head": "Harmonized data, Eurostat", "tail": "Eurostat"}}]}, "_meta": {"entry_id": 1335, "dataset_name": "Harmonized data, Eurostat"}}
+{"input": "The Regional Labor Market Assessment (RLMA) conducted by the International Labor Organization (ILO) in 2022 provides comprehensive insights into employment trends across Eastern Europe. In addition, the World Bank's Skills and Competencies Survey (SCS) for 2023 focuses on skill gaps in the workforce and is leveraged by various organizations, including the European Training Foundation (ETF) for their reports on vocational training. The Government of Romania is utilizing both datasets to inform policy changes aimed at enhancing labor market outcomes. Furthermore, the ILO also produced the Employment and Unemployment Statistics Database (EUSD) from 2019, which offers critical data for researchers and policymakers alike, showcasing employment rates across member countries.", "output": {"entities": {"named_data": ["Regional Labor Market Assessment", "Skills and Competencies Survey", "Employment and Unemployment Statistics Database"], "organization": ["International Labor Organization", "World Bank", "European Training Foundation", "Government of Romania"], "acronym": ["RLMA", "SCS", "EUSD"], "year": ["2022", "2023", "2019"], "geography": ["Eastern Europe", "Romania"]}, "relations": [{"has_organization": {"head": "Regional Labor Market Assessment", "tail": "International Labor Organization"}}, {"used_by": {"head": "Regional Labor Market Assessment", "tail": "European Training Foundation"}}, {"has_acronym": {"head": "Regional Labor Market Assessment", "tail": "RLMA"}}, {"has_timeframe": {"head": "Regional Labor Market Assessment", "tail": "2022"}}, {"has_organization": {"head": "Skills and Competencies Survey", "tail": "World Bank"}}, {"used_by": {"head": "Skills and Competencies Survey", "tail": "European Training Foundation"}}, {"has_acronym": {"head": "Skills and Competencies Survey", "tail": "SCS"}}, {"has_timeframe": {"head": "Skills and Competencies Survey", "tail": "2023"}}, {"has_organization": {"head": "Employment and Unemployment Statistics Database", "tail": "International Labor Organization"}}, {"has_timeframe": {"head": "Employment and Unemployment Statistics Database", "tail": "2019"}}, {"has_geography": {"head": "Employment and Unemployment Statistics Database", "tail": "Eastern Europe"}}, {"has_geography": {"head": "Skills and Competencies Survey", "tail": "Romania"}}, {"used_by": {"head": "Employment and Unemployment Statistics Database", "tail": "Government of Romania"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "employment, labor markets, and skills development"}}
+{"input": "We use the sum of conflict events occurring in the historic homeland of ethnic group e in the previous year t − 1, denoted as Conflictet − 1, and we use the mean distance between the historic homeland of ethnic group e and the border of country d to predict the number of refugees of a certain ethnic group e moving from country o to d at time t. 22 In order to be consistent with EPR-ER data construction, we restrict our analysis to all origin – destination country pairs that are at a maximum distance ≤ 950 km from each other. Predicted numbers of refugees are then transformed into predicted shares for the three largest groups to follow the logic used by the EPR-ER dataset. We then plug in these predicted shares in the following way: X \\PredictedRefcet = Refocdt ∗ \\Shareodet. (6) The predicted shares of refugees per camp c are then used to compute (as documented above) refugee diversity indices to be used as instrumental variables. The first-stage equations corresponding to the 2SLS-equivalent of Equation 1 can be expressed as 20We conduct a robustness check on Equation 5, replacing the dyadic origin – destination fixed effects with separate origin and destination fixed effects (Section 5. 4). 21More information on LEDA can be found in Appendix A. 1. 22The construction of the IV follows a long tradition in using the gravity model to predict bilateral migration flows (Ravenstein, 1985, 1989; Crozet, 2004; Mayda, 2010; Garcia et al., 2015; Beine et al., 2016). In our analysis, a major difference is that we have an additional dimension: the ethnic group e.", "output": {"entities": {"named_data": ["EPR-ER dataset"], "organization": ["We"]}, "relations": [{"used_by": {"head": "EPR-ER dataset", "tail": "We"}}]}, "_meta": {"entry_id": 429, "dataset_name": "EPR-ER dataset"}}
+{"input": "composite measure for human capital. We extract real GDP and human capital data from Penn World\n\nTable 10 (Feenstra et al., 2015), using the \"rgdpna\" series, which measures real GDP in constant 2017\n\nschooling and returns to education (Inklaar and Timmer, 2013). We extract the population from World\n\nDevelopment Indicators (WDI).\n\n#### **3.3 Sample Construction**\n\nThe baseline populations served by the East Road (within the buffer zones) were established by utilizing\nWorldPop open source data. To estimate the number of people affected by rain event, however, a series\nof models were run to simulate the locus of impacts for each rain event (3-, 10-, and 30-year events) and\ntheir subsequent effects on access. Repeated events might affect the same people, and so, the cumulative\nimpacts over the lifetime of the road can be thought of as 'person-disruptions' - i.e., the sum of individual\ndisruptions. If, for example, an individual living on the East Road was cut off from access to hospitals three\ntimes over the thirty-year period, this experience would account for three person-disruptions.\n\nUsing Global Positioning System (GPS) tracking records for the Malaita East Road from Atori to Dala, 5,856\nroad segments with gradient details were delineated and mapped using geographic information system\n(GIS) mapping software. These segments were mapped for the current road and also used to model surface\nconditions defined by the proposed upgrading projects.\n\nCost data for similar road projects also informed assumptions about per-unit (km) repair costs\nfor each type of road surface subject to various damage levels.", "output": {"entities": {"named_data": ["WorldPop open source data"], "organization": ["WorldPop"]}, "relations": [{"has_organization": {"head": "WorldPop open source data", "tail": "WorldPop"}}]}, "_meta": {"entry_id": 778, "dataset_name": "WorldPop open source data"}}
+{"input": "The 2020 Refugee Wellbeing Survey (RWS) provides critical insights into the living conditions of displaced populations and was published by the International Refugee Council (IRC). The survey data is widely used by the United Nations High Commissioner for Refugees (UNHCR) to enhance policy frameworks in various regions. Furthermore, the East African Migration Trends Report (EAMTR) 2021, developed by the Migration Policy Institute (MPI), offers a comprehensive analysis of migration patterns and has been referenced by the African Union (AU) for its ongoing initiatives. In addition, the 2019 Global Refugee Forum Dataset (GRFD) captures key outcomes from the forum and is utilized by numerous advocacy groups, including Refugees International (RI), to inform their efforts and drive engagement across affected countries.", "output": {"entities": {"named_data": ["Refugee Wellbeing Survey", "East African Migration Trends Report", "Global Refugee Forum Dataset"], "organization": ["International Refugee Council", "United Nations High Commissioner for Refugees", "Migration Policy Institute", "African Union", "Refugees International"], "acronym": ["RWS", "EAMTR", "GRFD"], "year": ["2020", "2021", "2019"], "geography": ["East Africa"]}, "relations": [{"has_organization": {"head": "Refugee Wellbeing Survey", "tail": "International Refugee Council"}}, {"used_by": {"head": "Refugee Wellbeing Survey", "tail": "United Nations High Commissioner for Refugees"}}, {"has_timeframe": {"head": "Refugee Wellbeing Survey", "tail": "2020"}}, {"has_organization": {"head": "East African Migration Trends Report", "tail": "Migration Policy Institute"}}, {"used_by": {"head": "East African Migration Trends Report", "tail": "African Union"}}, {"has_timeframe": {"head": "East African Migration Trends Report", "tail": "2021"}}, {"has_organization": {"head": "Global Refugee Forum Dataset", "tail": "Refugees International"}}, {"has_timeframe": {"head": "Global Refugee Forum Dataset", "tail": "2019"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "forced displacement, refugees, and migration"}}
+{"input": "The Global Trade Insights Report (GTIR) published by the International Trade Organization in 2022 provides valuable data on trade flows and patterns across various regions. This dataset focuses on the economic competitiveness of industries in developing countries, particularly in Africa and Southeast Asia. Additionally, the Agricultural Production and Trade Assessment (APTA) for the years 2019 to 2021 offers critical insights into food exports and imports, with a geographical focus on Latin America. While the GTIR is extensively used by policymakers and researchers, the APTA has yet to gain widespread attention in academic circles.", "output": {"entities": {"named_data": ["Global Trade Insights Report", "Agricultural Production and Trade Assessment"], "organization": ["International Trade Organization"], "acronym": ["GTIR", "APTA"], "year": ["2022", "2019 to 2021"], "geography": ["Africa", "Southeast Asia", "Latin America"]}, "relations": [{"has_acronym": {"head": "Global Trade Insights Report", "tail": "GTIR"}}, {"has_timeframe": {"head": "Global Trade Insights Report", "tail": "2022"}}, {"has_geography": {"head": "Global Trade Insights Report", "tail": "Africa"}}, {"has_geography": {"head": "Global Trade Insights Report", "tail": "Southeast Asia"}}, {"has_acronym": {"head": "Agricultural Production and Trade Assessment", "tail": "APTA"}}, {"has_timeframe": {"head": "Agricultural Production and Trade Assessment", "tail": "2019 to 2021"}}, {"has_geography": {"head": "Agricultural Production and Trade Assessment", "tail": "Latin America"}}, {"has_organization": {"head": "Global Trade Insights Report", "tail": "International Trade Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The recent analysis of the Socioeconomic Distress Index (SDI) conducted by the National Statistics Office (NSO) in Brazil provides critical insights into poverty levels across the country. This dataset, covering the years 2019 to 2022, has been utilized by the International Development Agency (IDA) to assess the impact of socioeconomic policies. Additionally, the Global Poverty Assessment Report (GPAR) 2021, published by the World Bank, highlights income inequality trends in Sub-Saharan Africa. The GPAR has been leveraged by various researchers to formulate recommendations aimed at poverty alleviation in the region. These datasets are crucial for understanding the dynamics of poverty and inequality in their respective geographies.", "output": {"entities": {"named_data": ["Socioeconomic Distress Index", "Poverty levels", "Global Poverty Assessment Report", "GPAR"], "organization": ["National Statistics Office", "International Development Agency", "World Bank"], "acronym": ["SDI", "GPAR"], "year": ["2019 to 2022", "2021"], "geography": ["Brazil", "Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Socioeconomic Distress Index", "tail": "National Statistics Office"}}, {"used_by": {"head": "Socioeconomic Distress Index", "tail": "International Development Agency"}}, {"has_timeframe": {"head": "Socioeconomic Distress Index", "tail": "2019 to 2022"}}, {"has_geography": {"head": "Socioeconomic Distress Index", "tail": "Brazil"}}, {"has_organization": {"head": "Global Poverty Assessment Report", "tail": "World Bank"}}, {"has_acronym": {"head": "Global Poverty Assessment Report", "tail": "GPAR"}}, {"used_by": {"head": "Global Poverty Assessment Report", "tail": "various researchers"}}, {"has_geography": {"head": "Global Poverty Assessment Report", "tail": "Sub-Saharan Africa"}}, {"has_timeframe": {"head": "Global Poverty Assessment Report", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "poverty headcount and inequality measurement"}}
+{"input": "**The World Bank**\nSouth Sudan Health Sector Transformation Project (HSTP) (P181385)\n\n|Description|Percentage of HC health facilities receiving at least one quarterly supervision visit within the quarter|\n|---|---|\n|Frequency|Quarterly|\n|Data source|MoH; TPM|\n|Methodology for Data<br>Collection|MoH to provide data; TPM to verify|\n|Responsibility for Data<br>Collection|MoH / TPM|\n|**Percentage of health facilities receiving quarterly supervision visits from the CHD (Percentage)**|**Percentage of health facilities receiving quarterly supervision visits from the CHD (Percentage)**|\n|Description|Percentage of health facilities receiving at least one quarterly supervision visit within the quarter from the CHD|\n|Frequency|Quarterly|\n|Data source|MoH; TPM|\n|Methodology for Data<br>Collection|MoH to provide data; TPM to verify|\n|Responsibility for Data<br>Collection|MoH / TPM|\n|**Percentage of health facilities receiving quarterly supervision visits from State MoH (Percentage)**|**Percentage of health facilities receiving quarterly supervision visits from State MoH (Percentage)**|\n|Description|Percentage of health facilities receiving at least one quarterly supervision visit within the quarter from the State<br>MoH|\n|Frequency|Quarterly|\n|Data source|MoH; TPM|\n|Methodology for Data<br>Collection|MoH to provide data; TPM to verify|\n|Responsibility for Data<br>Collection|MoH / TPM|\n|**Percentage of complaints to Grievance Redress Mechanisms satisfactorily addressed in a timely manner**|**Percentage of complaints to Grievance Redress Mechanisms satisfactorily addressed in a timely manner**|\n|Description|Percentage of complaints submitted to the GRM addressed according to the protocol and within agreed time<br>period.|\n|Frequency|Quarterly|\n|Data source|UNICEF|\n|Methodology for Data<br>Collection|UNICEF to provide data / TPM to verify|\n|Responsibility for Data<br>Collection|UNICEF; PMU|\n|**Percentage of completeness of reporting by facilities**|**Percentage of completeness of reporting by facilities**|\n|Description|Percentage of facilities that submit complete reports within the required deadline.|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH/ PMU|\n|**Percentage of states that conducted quarterly coordination meetings with a review of data and documented with minutes including**<br>**action items and follow-up**|**Percentage of states that conducted quarterly coordination meetings with a review of data and documented with minutes including**<br>**action items and follow-up**|\n|Description|Percentage of State’s quarterly health service delivery coordination meetings for the health sector held with a<br>review of data included in the meeting and documented with minutes which include action items and follow-up<br>on action items. Meetings are to be held quarterly in each state. Four meetings are expected each year per<br>state. CHDs and implementing partners will be participated in the review|\n|Frequency|Quarterly|\n|Data source|MoH/ WHO|\n|Methodology for Data<br>Collection|WHO to provide data / TPM to verify|", "output": {"entities": {"named_data": ["DHIS2"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "DHIS2", "tail": "World Bank"}}]}, "_meta": {"entry_id": 826, "dataset_name": "DHIS2"}}
+{"input": "The recent analysis of the Education Quality Assessment Report published by the Global Education Initiative highlights significant disparities in learning outcomes among various regions. This report serves as an essential resource for policymakers and education advocates aiming to enhance educational equity and improve school enrollment rates across underrepresented communities. The Global Education Initiative has committed to leveraging data from this report to inform future programs and initiatives.", "output": {"entities": {"named_data": ["Education Quality Assessment Report"], "organization": ["Global Education Initiative"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Education Quality Assessment Report", "tail": "Global Education Initiative"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "wages and reduced over-education for migrants working in licensed jobs, while producing worse labour market outcomes for those who did not gain licensure.\nAccording to Peterson et al. (2014), over the 1973–2010 period, U.S. states with more stringent occupational licensing for migrant physicians received fewer new migrant physicians and struggled more with staffing shortages in healthcare. Aleksynska and Tritah (2013) quoted data that migrants in France were denied legal access to approximately 30% of jobs in the country.\n\nUkrainian refugees Polish citizens Source: Deloitte own elaboration based on mid-2024 SEIS UNHCR survey (Ukrainian refugees’ educational attainment), 2023 Eurostat Labour Force Survey Eurostat (Polish citizens educational attainment), and mid-2024 ZUS administrative data (occupational groups).\n\n**Chart 21. Share of regulated professions by citizenship and legal status, Q2 2024**\n\n**The educational premium seems**\n\n**to be lower for Ukrainian refugees**\n\n**compared to the general workforce**", "output": {"entities": {"named_data": ["mid-2024 SEIS UNHCR survey"], "organization": ["UNHCR", "Deloitte"]}, "relations": [{"has_organization": {"head": "mid-2024 SEIS UNHCR survey", "tail": "UNHCR"}}, {"used_by": {"head": "mid-2024 SEIS UNHCR survey", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1266, "dataset_name": "mid-2024 SEIS UNHCR survey"}}
+{"input": "an economically viable area. Results from various studies such as the 23 Zambia 2022 Census of Population and Housing, Central Statistical Office Zambia, 2023. 24 One Meheba Local Area Plan, Planning Survey and Issues Report, GRZ, 2023. 25 One Meheba Local Area Plan, Planning Survey and Issues Report, GRZ, 2023. 26 “Many girls choose not to attend classes at certain times due to the lack of appropriate washing and hygiene facilities,” School headmaster, Kalumbila District, February 2024. Page 16", "output": {"entities": {"named_data": ["23 Zambia 2022 Census of Population and Housing"], "organization": ["Central Statistical Office Zambia", "GRZ"]}, "relations": [{"has_organization": {"head": "23 Zambia 2022 Census of Population and Housing", "tail": "Central Statistical Office Zambia"}}, {"used_by": {"head": "23 Zambia 2022 Census of Population and Housing", "tail": "GRZ"}}]}, "_meta": {"entry_id": 1232, "dataset_name": "23 Zambia 2022 Census of Population and Housing"}}
+{"input": "and more immediate impact on food access, we are able to find additional empirical support\n\nfor the last explanation. Given data and information constraints, it is not possible to estimate\n\nSundberg, R., and E. Melander. 2013. \"Introducing the UCDP Georeferenced Event Dataset.\"\n\nNotes: This table compares mobile phone ownership in the November 2017 WFP mobile phone survey and the 2014 Household Budget Survey (HBS), where the 2014 HBS summary statistics are restricted to the share of the population that resides in a household that owns at least one mobile phone.\n\nWe gratefully thank Claudio Montenegro, David Newhouse and Minh Nguyen for their help with the I2D2 database. We gratefully acknowledge the generous support of the World Bank (Office of the Senior Vice-President and Chief Economist and Social Urban Rural and Resilience Global Practice), the Cities Program of the International Growth Center (Grant number 89408), the GWU Institute for International Economic Policy and the GWU Center for International Business Education and Research.\n\nData Used to Estimate the Returns**   **Sources.** The data source for the analysis is the _International_ _Income_ _Distribution_   _Database_ (I2D2) of the World Bank. The database consists of a large number of   individual-level surveys and census samples.", "output": {"entities": {"named_data": ["UCDP Georeferenced Event Dataset"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "UCDP Georeferenced Event Dataset", "tail": "World Bank"}}]}, "_meta": {"entry_id": 81, "dataset_name": "UCDP Georeferenced Event Dataset"}}
+{"input": "will be served by health centers, resulting in 50 maternal lives saved per year and a 15 percent reduction in other causes of morbidity (baseline morbidity of 19 percent). [28] The NPV of these investments is estimated at US$1.3 million at a 12 percent discount rate, with an ERR of 29 percent. 61. _Communal WASH_ investments reduce morbidity from diarrhea and other waterborne diseases, along with time and cost savings to access water. The analysis assumes the mortality rate due to unsafe water will reduce by 10 percent (from 25 deaths per 100,000 to 22.5), along with time savings of two hours per week per beneficiary household in accessing clean water and US$20 in annual cost savings per household (CEIC data). The NPV of these WASH investments is estimated at US$1.1 million at a 12 percent discount rate, with an ERR of 28 percent. 62. Additional assumptions used in the analysis for this component include: (a) value of a statistical life: US$2,394; [29] and (b) annual O&M costs of five percent of total investment value. [30] 63. The results of the analysis for this component and the sensitivity analysis are summarized in the tables below: **Table 6: NPV and ERR", "output": {"entities": {"named_data": ["CEIC data"], "organization": ["CEIC"]}, "relations": [{"has_organization": {"head": "CEIC data", "tail": "CEIC"}}]}, "_meta": {"entry_id": 1186, "dataset_name": "CEIC data"}}
+{"input": "in-camp refugees, their hosts, and out-of-camp refugees (Table D.12 in Annex D). The predicted poverty rate decreases with the share of employed household members, indicating that employment is essential to lowering poverty for in- camp refugees (Figure 5.19). 0 10 20 30 40 50 60 70 80 90 100 Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts Electricity Mobile phone Any livestock Bank account Agricultural holding Nonfarm enterprise Percent Figure 5.17: Household wealth indicators by expenditure quintiles Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 Labor force participation Employment to working age population ratio Unemployment rate 0 10 20 30 40 50 60 70 80 90 100 Agriculture Industry Services 0 10 20 30 40 50 60 70 Poorest 2 3 4 Richest In Camp Refugees Inside the Camp Outside the Camp 0 10 20 30 40 50 60 70 80 Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Low-skill Medium-skill High-skill Percent Percent", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank Staff"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 504, "dataset_name": "SESRE 2023"}}
+{"input": "2 1. INTRODUCTION Refugees pose a massive moral, political and economic challenge for potential host countries. 1 The scale of the challenge is larger than ever, with 60 million people forcibly displaced by conflicts across the world (UNHCR, 2014). War in Syria has produced more refugees than any other conflict of the past two decades: around 4. 6 million have fled the country, with an additional 7. 6 million internally displaced. 2 About 2. 5 million Syrians have found refuge in Turkey, making it the largest refugee-hosting country worldwide. This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess the impact on Turkish employment and wages. The large majority (85 percent) of Syrians have left the refugee camps and entered the Turkish labor market. 3 They are overwhelmingly employed informally, since they were not issued work permits. This makes their arrival a well-defined supply shock to informal labor, and a particularly good context in which to test the predictions of basic economic theory. We instrument for refugee flows using travel distance between 13 origin governorates in Syria and 26 Turkish subregions (338 origin-destination pairs). This allows us to also control for distance from the Syrian border, and thus any confounding factors that are correlated with proximity to Syria.", "output": {"entities": {"named_data": ["Turkish Labour Force Survey"], "organization": ["this paper"]}, "relations": [{"used_by": {"head": "Turkish Labour Force Survey", "tail": "this paper"}}]}, "_meta": {"entry_id": 664, "dataset_name": "Turkish Labour Force Survey"}}
+{"input": "nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016).", "output": {"entities": {"named_data": ["Labor Force Surveys"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "Labor Force Surveys", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 1059, "dataset_name": "Labor Force Surveys"}}
+{"input": "The recommendations drawn from this analysis focus on addressing the health and mental health and psychosocial needs and barriers identified in the SEIS, tailoring them to the specific data and context of each country. To enhance policy development, it will be crucial to improve monitoring of refugees’ health, including sexual and reproductive health and mental health, through inclusion of disaggregated refugee data into national data systems. This will require effective collaboration among health organizations, statistical offices, and partners. Addressing capacity issues in national health systems, such as workforce shortages and long wait times, can be supported through telemedicine and temporarily integrating Ukrainian healthcare workers. Refugees with chronic illnesses and disabilities require targeted interventions to meet their health and MHPSS needs, including through health financing mechanisms. Continued efforts are also required to address persistent access barriers through contextspecific strategies, including providing refugees with information on navigating health systems and preventive health services such as vaccination.", "output": {"entities": {"named_data": ["the SEIS"], "organization": ["SEIS"]}, "relations": [{"has_organization": {"head": "the SEIS", "tail": "SEIS"}}]}, "_meta": {"entry_id": 1338, "dataset_name": "the SEIS"}}
+{"input": "The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father ’ s schooling, mother ’ s schooling, and local supply of schools in subdistrict of birth. 9 These exclusions resulted in a sample of 4, 139 males and 4, 131 females, which are referred to as the male and female full samples, respectively. 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts. 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA. 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8, 312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father ’ s schooling), and 7 observations (missing mother ’ s schooling).", "output": {"entities": {"named_data": ["JLMPS"], "organization": ["Ministry of Education"]}, "relations": [{"used_by": {"head": "JLMPS", "tail": "Ministry of Education"}}]}, "_meta": {"entry_id": 490, "dataset_name": "JLMPS"}}
+{"input": "The recent analysis on poverty headcount and inequality in Sub-Saharan Africa draws upon the Sub-Saharan Poverty Assessment Report 2022, which provides comprehensive data on income distribution and living standards across the region. This dataset, published by the African Development Bank, reveals significant disparities in wealth that have persisted despite various economic initiatives aimed at alleviating poverty.", "output": {"entities": {"named_data": ["Sub-Saharan Poverty Assessment Report 2022"], "organization": ["African Development Bank"], "acronym": [], "year": ["2022"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Sub-Saharan Poverty Assessment Report 2022", "tail": "African Development Bank"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "poverty headcount and inequality measurement"}}
+{"input": "Refugee Policies on Self-Reliance and Resilience. Journal of Refugee Studies, 33(1), 22-41. Kvittingen A., Valenta M., Tabbara H., Baslan D., and Berg B. (2018). The Conditions and Migratory Aspirations of Syrian and Iraqi Refugees in Jordan. Journal of Refugee Studies 32(1), 106-124. https://doi.org/10.1093/jrs/fey015. Lebow, J. (2023). Immigration and Occupational Downgrading in Colombia. Journal of Development Economics, Forthcoming Levenson, H. (1981). Differentiating among Internality, Powerful Others, and Chance. Research with the Locus of Control Construct, 1, 15-63. Liu, D. and Kwan, M. (2020). Measuring Spatial Mismatch and Job Access Inequity Based on Transit-Based Job Accessibility for Poor Job Seekers. Travel Behaviour and Society, 19, 184-193. https://doi.org/10.1016/j.tbs.2020.01.005 ESS and World Bank. (2023). Ethiopia Socioeconomic Panel Survey Report – Wave 2, 2021/22. Marbach, M., Hainmueller, J., Hangartner, D. (2018). The Long-Term Impact of Employment Bans on the Economic Integration of Refugees. Science Advances, 4. https://doi.org/10.1126/sciadv.aap9519. Martin, S. F. (2010). The Causes and Consequences of Forced Migration. Paper presented at the Annual Meeting of the Population Association of America, Dallas, TX. Maystadt, J. F., and Verwimp, P. (2014). Winners and Losers among a Refugee-Hosting Population. Economic Development and Cultural Change, 62(4), 769-809. MoE. (2022). Education Statistics Annual Abstract (ESAA). MoE MoLSA. (2019). Revised Directives", "output": {"entities": {"named_data": ["Ethiopia Socioeconomic Panel Survey Report"], "organization": ["World Bank", "ESS"]}, "relations": [{"has_organization": {"head": "Ethiopia Socioeconomic Panel Survey Report", "tail": "World Bank"}}, {"used_by": {"head": "Ethiopia Socioeconomic Panel Survey Report", "tail": "ESS"}}]}, "_meta": {"entry_id": 466, "dataset_name": "Ethiopia Socioeconomic Panel Survey Report"}}
+{"input": "The recent analysis of urban transportation patterns draws on data from the Urban Mobility Assessment and the Metropolitan Infrastructure Survey. These datasets provide insights into the challenges faced by cities in managing their transport networks and infrastructure needs.", "output": {"entities": {"named_data": ["Urban Mobility Assessment", "Metropolitan Infrastructure Survey"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "urban infrastructure and transportation planning"}}
+{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**Unit of** **Data Source /**\n**Indicator Name** **Corporate** **End Target** **Frequency**\n**Measure** **[Baseline ]** **Methodology**\n\nDescription:\n\n**Name:** Percentage of\nbeneficiaries taking a more active role in their communities Percentage of beneficiaries taking a more active role in their communities - female Percentag e Percentag e 0.00 50.00 Twice, once before civic engagement training and again at least three months after civic engagement training completion.\n\n0.00 50.00 Twice, once before civic engagement training and again at least three months after civic engagement training completion.\n\nFor participants in Component 1 and 2 of the project, the Baseline Survey and Post-Training Completion Survey will be used for data collection.\nFor individuals trained only as part of Component 3, a separate pre-training survey will be conducted as well as a follow-up survey conducted at least three months after civic engagement training.\n\nFor participants in Component 1 and 2 of the project, the Baseline Survey and Post-Training Completion Survey will be used for data collection.\nFor individuals trained only as part of Component 3, a separate pre-training survey will be conducted as well as a follow-up survey conducted at least\n\n**Responsibility for**\n**Data Collection**\n\nM&E Specialist M&E Specialist Page 26 of 34", "output": {"entities": {"named_data": ["Baseline Survey and Post-Training Completion Survey"], "organization": ["The World Bank"]}, "relations": [{"has_organization": {"head": "Baseline Survey and Post-Training Completion Survey", "tail": "The World Bank"}}]}, "_meta": {"entry_id": 832, "dataset_name": "Baseline Survey and Post-Training Completion Survey"}}
+{"input": "The 2020 Maternal Health Assessment (MHA) provides crucial insights into health system challenges faced by women in sub-Saharan Africa. Produced by the Global Health Institute, the MHA dataset focuses on access to care and maternal outcomes. Furthermore, the 2019/2020 Women’s Health Survey (WHS) highlights reproductive health metrics across five major regions, making it an essential tool for policymakers. Both datasets play a significant role in informing health interventions, although the MHA has been utilized primarily by local NGOs, while the WHS is cited frequently in international health research.", "output": {"entities": {"named_data": ["Maternal Health Assessment", "Women’s Health Survey"], "organization": ["Global Health Institute", "local NGOs"], "acronym": ["Maternal Health Assessment", "WHS"], "year": ["2020", "2019/2020"], "geography": ["sub-Saharan Africa", "five major regions"]}, "relations": [{"has_acronym": {"head": "Maternal Health Assessment", "tail": "MHA"}}, {"has_timeframe": {"head": "Maternal Health Assessment", "tail": "2020"}}, {"has_geography": {"head": "Maternal Health Assessment", "tail": "sub-Saharan Africa"}}, {"has_acronym": {"head": "Women’s Health Survey", "tail": "WHS"}}, {"has_timeframe": {"head": "Women’s Health Survey", "tail": "2019/2020"}}, {"has_geography": {"head": "Women’s Health Survey", "tail": "five major regions"}}, {"has_organization": {"head": "Maternal Health Assessment", "tail": "Global Health Institute"}}, {"used_by": {"head": "Maternal Health Assessment", "tail": "local NGOs"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "health systems and maternal outcomes"}}
+{"input": "The Fragile States Index (FSI), published annually by the Fund for Peace, provides critical insights into the socio-economic conditions of various countries facing conflict and instability. This data is extensively utilized by organizations such as the United Nations High Commissioner for Refugees (UNHCR) to inform their humanitarian responses and policy frameworks. Additionally, the Global Conflict Database (GCD) for the year 2020, created by the World Bank, offers comprehensive statistics on conflict-related deaths and displacements, serving as a vital resource for NGOs working in conflict zones like Syria and Yemen.", "output": {"entities": {"named_data": ["Fragile States Index", "Global Conflict Database"], "organization": ["Fund for Peace", "United Nations High Commissioner for Refugees", "World Bank"], "acronym": ["FSI", "GCD"], "year": ["2020"], "geography": ["Syria", "Yemen"]}, "relations": [{"has_organization": {"head": "Fragile States Index", "tail": "Fund for Peace"}}, {"used_by": {"head": "Fragile States Index", "tail": "United Nations High Commissioner for Refugees"}}, {"has_acronym": {"head": "Fragile States Index", "tail": "FSI"}}, {"has_organization": {"head": "Global Conflict Database", "tail": "World Bank"}}, {"has_timeframe": {"head": "Global Conflict Database", "tail": "2020"}}, {"has_geography": {"head": "Global Conflict Database", "tail": "Syria"}}, {"has_geography": {"head": "Global Conflict Database", "tail": "Yemen"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "conflict, fragility, and violence"}}
+{"input": "The 2020 Climate Resilience Assessment Report (CRAR) highlights the vulnerabilities faced by coastal regions in the Caribbean, particularly focusing on the impact of rising sea levels. Analyzing data from the Caribbean Environmental Database (CED) covering the years 2018-2022, researchers from the International Climate Institute (ICI) have proposed several adaptation strategies for local governments. The report emphasizes the necessity for timely data, particularly as climate change accelerates the risks associated with natural disasters. This collaboration showcases the importance of integrating socio-economic data with environmental assessments to enhance disaster risk management strategies in the region.", "output": {"entities": {"named_data": ["2020 Climate Resilience Assessment Report", "Caribbean Environmental Database"], "organization": ["International Climate Institute"], "acronym": ["Climate Resilience Assessment Report", "CED"], "year": ["2020", "2018-2022"], "geography": ["Caribbean"]}, "relations": [{"has_acronym": {"head": "2020 Climate Resilience Assessment Report", "tail": "CRAR"}}, {"has_acronym": {"head": "Caribbean Environmental Database", "tail": "ced"}}, {"has_timeframe": {"head": "2020 Climate Resilience Assessment Report", "tail": "2020"}}, {"has_timeframe": {"head": "Caribbean Environmental Database", "tail": "2018-2022"}}, {"has_geography": {"head": "Caribbean Environmental Database", "tail": "Caribbean"}}, {"used_by": {"head": "Caribbean Environmental Database", "tail": "International Climate Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "We then use international data (EMDAT 2009) to estimate the relationship between storm damages and national income and population density (vulnerability).\n\nThe analysis relies on the A1B SRES emissions scenario generated by the Intergovernmental Panel on Climate Change (IPCC 2000). The scenario assumes that mitigation is tightened gradually over time so that greenhouse gas concentrations finally peak and stabilize at 720 ppm.", "output": {"entities": {"named_data": ["EMDAT 2009"], "organization": ["We"]}, "relations": [{"used_by": {"head": "EMDAT 2009", "tail": "We"}}]}, "_meta": {"entry_id": 476, "dataset_name": "EMDAT 2009"}}
+{"input": "The Urban Water Quality Assessment (UWQA) conducted in 2022 provides critical insights into the status of water systems in urban areas across sub-Saharan Africa. This assessment not only highlights the existing disparities in water quality but also serves as a vital tool for policymakers aiming to improve sanitation practices. The data collected in this comprehensive survey informs various initiatives aimed at enhancing public health outcomes in the region, especially in cities like Accra and Nairobi. Movement towards improved water infrastructure can be guided by findings from the UWQA, which is increasingly referenced by local NGOs working on water and sanitation projects.", "output": {"entities": {"named_data": ["Urban Water Quality Assessment"], "organization": ["local NGOs"], "acronym": ["UWQA"], "year": ["2022"], "geography": ["sub-Saharan Africa", "Accra", "Nairobi"]}, "relations": [{"has_acronym": {"head": "Urban Water Quality Assessment", "tail": "UWQA"}}, {"has_timeframe": {"head": "Urban Water Quality Assessment", "tail": "2022"}}, {"has_geography": {"head": "Urban Water Quality Assessment", "tail": "sub-Saharan Africa"}}, {"has_geography": {"head": "Urban Water Quality Assessment", "tail": "Accra"}}, {"has_geography": {"head": "Urban Water Quality Assessment", "tail": "Nairobi"}}, {"used_by": {"head": "Urban Water Quality Assessment", "tail": "local NGOs"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "water, sanitation, and hygiene"}}
+{"input": "**FIGURE 1. Mekong River Basin Commission Monitoring Station Locations**\n\n_Notes:_ Map shows location of water quality monitoring stations in Mekong River Basin Commission dataset.\n\nOur time-varying NPP data comes from the moderate resolution imaging spectroradiometer (MODIS), whose data starts in 2000. We use the annual MOD17A3 measures from 2000-2013 generated by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana (Zhao _et al_ ., 2005) which corrects for cloud contamination prevalent in MODIS land products ~~.~~ [3] Our interest is in estimating NPP from cropland, as opposed to natural forests or vegetation. To do so, we make use of a new and unique land cover dataset developed by the European Space Agency’s (ESA) Salt of the Earth 5", "output": {"entities": {"named_data": ["Mekong River Basin Commission dataset"], "organization": ["Numerical Terradynamic Simulation Group"]}, "relations": [{"used_by": {"head": "Mekong River Basin Commission dataset", "tail": "Numerical Terradynamic Simulation Group"}}]}, "_meta": {"entry_id": 1399, "dataset_name": "Mekong River Basin Commission dataset"}}
+{"input": "2 International migration — the movement of people across national borders — has important economic, social, and political implications. Despite the recent emergence of a dynamic literature, empirical analysis of migration flows and their impact lags behind the policy debate and the theoretical literature. The main reason is the absence of comprehensive and reliable data on international migration patterns and migrant characteristics at either the aggregate or the household level. The objective of this article is to use data from more than one thousand national censuses and population registers to estimate a complete global origin – destination migration matrix for each decade over 1960 – 2000. These 226 * 226 matrices, comprising every country, major territory, and dependency around the world, are divided into periods corresponding to the last five completed census rounds. The gender dimension of international migration over this period is also presented. The primary source of the raw data is the United Nations Population Division ‘ s Global Migration Database, created through the collaboration of the United Nations Population Division, the United Nations Statistics Division, the World Bank, and the University of Sussex (United Nations [2008]). This unique data repository comprises 3, 500 individual census and population register records1 for more than 230 destination countries and territories over the last five decades.", "output": {"entities": {"named_data": ["Global Migration Database"], "organization": ["United Nations Population Division"]}, "relations": [{"has_organization": {"head": "Global Migration Database", "tail": "United Nations Population Division"}}]}, "_meta": {"entry_id": 117, "dataset_name": "Global Migration Database"}}
+{"input": "3 population density across a state, the ecological inference issue is alleviated as we directly test the propensity of any population group to experience a conflict. Through disaggregation, we may succeed in supporting explanations based on variables such as the distance from the capital and the overall size of the country's population if we know at which locations conflicts occur. If conflicts are located mainly at some distance from countries'capitals, we might infer that large countries have more conflicts because of the difficulties of projecting governmental power. If they are located in population concentrations irrespective of location relative to the capita, other explanations should be sought. The paper makes use a new dataset called ACLED (Armed Conflict Location and Events Dataset) to allow for this type of disaggregated analysis. The dataset currently codes the location of all reported conflict events in 14 countries in Central Africa in the 1960 – 2004 period. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper suggests some adaptions to a statistical method to allow for analyzing data at this level of analysis. Related to the size of populations is their distributions. The Democratic Republic of Congo, for instance, is not only characterized by being enormously large, but also shows tremendous variation in population densities.", "output": {"entities": {"named_data": ["ACLED"], "organization": ["the paper"]}, "relations": [{"used_by": {"head": "ACLED", "tail": "the paper"}}]}, "_meta": {"entry_id": 918, "dataset_name": "ACLED"}}
+{"input": "**population in Poland has changed**\n\n**radically after February 24, 2022.**\nUp until 2021, Ukrainians in Poland were mostly men (close to two-thirds), who came for work-related reasons, often leaving their families back in Ukraine. The onset of the full-scale conflict in Ukraine triggered the arrival of individuals displaced by the war.\nThose were primarily women and children, with men in Ukraine being mobilized for the war effort. Social insurance data does not reflect the full extent of the change, showing only workers, without children and adults outside of employment.\n\n**Chart 3. Number of Ukrainians registered in Poland for social insurance by sex**\n\n2021 Q4 2022 Q4 2023 Q4 2024 Q2 Number of insured men with Ukrainian citizenship Number of insured women with Ukrainian citizenship Number of insured with Ukrainian citizenship Source: Deloitte own elaboration based on ZUS data.\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 attachment; or produced agricultural goods for sale or barter. A definition according to the Labour Force Survey: https://ec.europa.eu/eurostat/statistics-explained/ index.php?title=Glossary:Employed_person_-_LFS 08 Source: Deloitte own elaboration based on the PESEL database as of September 2024.", "output": {"entities": {"named_data": ["PESEL database"], "organization": ["Deloitte"]}, "relations": [{"used_by": {"head": "PESEL database", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1310, "dataset_name": "PESEL database"}}
+{"input": "The Urban Mobility Assessment Report (UMAR) provides critical insights into the transportation infrastructure of urban areas in Eastern Europe. Covering the years 2020 to 2022, this dataset is a comprehensive resource for planners and policymakers aiming to improve transit systems. Although the report was not directly cited by the Ministry of Transport, its findings have influenced various urban development initiatives across the region. The data can be instrumental in understanding mobility patterns within cities like Warsaw and Bucharest, as well as in guiding future investment decisions.", "output": {"entities": {"named_data": ["Urban Mobility Assessment Report", "transportation infrastructure", "Urban Mobility Assessment Report (UMAR)"], "organization": ["Ministry of Transport"], "acronym": ["Urban Mobility Assessment Report (UMAR)"], "year": ["2020 to 2022"], "geography": ["Eastern Europe", "Warsaw", "Bucharest"]}, "relations": [{"has_acronym": {"head": "Urban Mobility Assessment Report", "tail": "UMAR"}}, {"has_timeframe": {"head": "Urban Mobility Assessment Report", "tail": "2020 to 2022"}}, {"has_geography": {"head": "Urban Mobility Assessment Report", "tail": "Eastern Europe"}}, {"has_geography": {"head": "Urban Mobility Assessment Report", "tail": "Warsaw"}}, {"has_geography": {"head": "Urban Mobility Assessment Report", "tail": "Bucharest"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "urban infrastructure and transportation planning"}}
+{"input": "the Women, Business, and the Law Index (World Bank 2022a). 4. **Djibouti’s vulnerability to climate-related disasters exacerbates these economic and social challenges.** The country frequently experiences heatwaves, droughts and floods, which take a heavy toll on the population by reducing productivity and disrupting economic activity. Between 1980 and 2019, Djibouti recorded ten major droughts, with the 2008-2011 drought alone shrinking GDP by four percent and affecting over 100,000 people, leading to the loss of half of Djibouti’s livestock. More recent flooding events between 2018 and 2020 caused substantial damage, impacting 250,000 people and requiring an estimated US$25 million for recovery and reconstruction. The increasing frequency and intensity of these extreme weather events place additional pressure on rural communities, which are already struggling with poverty, underdeveloped infrastructure, and limited access to essential services. In 2021, 89 percent of Djibouti’s population was exposed to climate hazards, with extreme heat affecting more people (82.8 percent) than any other 1 In Djibouti less than 1,000 square kilometers of land is arable and annual rainfall is extremely low (130 mm). Page 1", "output": {"entities": {"named_data": ["Women, Business, and the Law Index"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "Women, Business, and the Law Index", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1149, "dataset_name": "Women, Business, and the Law Index"}}
+{"input": "the original variables used to predict fragility as controls without changing results. It is only in the post-war period that exclusion and refugees become a factor that influences foreign investment flows. Finally, the results we find are robust across all datasets of foreign investment we use. These results make it at least plausible that political exclusion and refugees matter because they predict a relapse to more intense violence. As final piece of evidence for this idea we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD). We collected data on political risk evaluations from ONDD who, according to their annual report, insured transactions worth about 7 billion EUR in 2011. The variable we use measures the risk of a credit default for rea- sons beyond the control of the debtor, i. e. due to political or financial macroeconomic events. We choose this variable because it provides the most consistent time-series in the ONDD data. ONDD measures both short- and mid-term risk on a scale from 1 (low risk) to 7 (high risk). Table 12, columns (1) and (4) show that risk ratings are decreasing in peacetime. Note that, as before, we control for country fixed effects which implies that we look at changes within country.", "output": {"entities": {"named_data": ["ONDD data"], "organization": ["ONDD"]}, "relations": [{"has_organization": {"head": "ONDD data", "tail": "ONDD"}}]}, "_meta": {"entry_id": 1052, "dataset_name": "ONDD data"}}
+{"input": "confirmed the results found in international literature presented above. The most important result is that Ukrainian refugees who are fluent in Polish earn a net wage premium of about PLN 700 (+16% relative to refugee net median wage, PLN 1,000 gross wage) when compared to those with beginner language skills. This result is stable across different model specifications. Note that such an earnings gain would bring the median net wage of a Ukrainian refugee (estimated based on the SEIS UNHCR survey in chapter 2) from 80% to 98% of the median in the economy as a whole (or from 80% to 93% according to Ukrainian refugee’s median in the NBP’s 2024 survey), almost closing the gap to the economy as a whole in these terms. It is in fact higher than the PLN 500 median net wage premium of the pre-war Ukrainian migrants over Ukrainian refugees in the NBP (2024) survey, even though 68% of the former and only 28% of the latter said they had a high level of fluency in Polish.\n\n31", "output": {"entities": {"named_data": ["NBP’s 2024 survey"], "organization": ["NBP"]}, "relations": [{"has_organization": {"head": "NBP’s 2024 survey", "tail": "NBP"}}]}, "_meta": {"entry_id": 1330, "dataset_name": "NBP’s 2024 survey"}}
+{"input": "The Digital Adoption Survey (DAS) conducted in 2022 provides valuable insights into technology usage across various sectors in Kenya. Published by the Global Tech Institute, this dataset has been widely utilized by multiple organizations including the Kenya National Bureau of Statistics (KNBS) to inform their policy-making processes. Additionally, the Tech Trends Report 2021, produced by the African Development Council, further complements the DAS findings by offering a broader analysis of digital trends across Africa. Organizations like the United Nations Economic Commission for Africa frequently reference this report to guide their initiatives aimed at enhancing technological infrastructure in the region.", "output": {"entities": {"named_data": ["Digital Adoption Survey", "Tech Trends Report 2021"], "organization": ["Global Tech Institute", "Kenya National Bureau of Statistics", "African Development Council", "United Nations Economic Commission for Africa"], "acronym": ["DAS"], "year": ["2022", "2021"], "geography": ["Kenya", "Africa"]}, "relations": [{"has_organization": {"head": "Digital Adoption Survey", "tail": "Global Tech Institute"}}, {"used_by": {"head": "Digital Adoption Survey", "tail": "Kenya National Bureau of Statistics"}}, {"has_acronym": {"head": "Digital Adoption Survey", "tail": "DAS"}}, {"has_timeframe": {"head": "Digital Adoption Survey", "tail": "2022"}}, {"has_organization": {"head": "Tech Trends Report 2021", "tail": "African Development Council"}}, {"used_by": {"head": "Tech Trends Report 2021", "tail": "United Nations Economic Commission for Africa"}}, {"has_timeframe": {"head": "Tech Trends Report 2021", "tail": "2021"}}, {"has_geography": {"head": "Digital Adoption Survey", "tail": "Kenya"}}, {"has_geography": {"head": "Tech Trends Report 2021", "tail": "Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "digital development and technology adoption"}}
+{"input": "The recently released Global Agriculture Performance Report, produced by the Food and Agriculture Organization (FAO), provides critical insights into agricultural productivity across developing nations. In addition, the Sustainable Food Systems Survey, utilized by the International Fund for Agricultural Development (IFAD), highlights the challenges faced by smallholder farmers in ensuring food security. These datasets are instrumental for policymakers aiming to enhance food security strategies in low-income regions.", "output": {"entities": {"named_data": ["Global Agriculture Performance Report", "Sustainable Food Systems Survey"], "organization": ["Food and Agriculture Organization", "International Fund for Agricultural Development"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Global Agriculture Performance Report", "tail": "Food and Agriculture Organization"}}, {"has_organization": {"head": "Sustainable Food Systems Survey", "tail": "International Fund for Agricultural Development"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "agriculture, food security, and nutrition"}}
+{"input": "As digital technology continues to reshape economies, the 2022 Global Technology Adoption Survey (GTAS) reveals significant trends across various sectors. Conducted by the International Institute for Digital Innovation (IIDI), the survey covers over 50 countries, emphasizing the role of technology in improving business processes. The results suggest that while some regions have rapidly embraced digital solutions, others lag behind due to infrastructural challenges. The data from this survey provides valuable insights for policymakers looking to enhance technology adoption strategies in their respective countries. Source: IIDI elaboration based on the Global Technology Adoption Survey.", "output": {"entities": {"named_data": ["Global Technology Adoption Survey"], "organization": ["International Institute for Digital Innovation"], "acronym": ["GTAS"], "year": ["2022"], "geography": ["50 countries"]}, "relations": [{"has_acronym": {"head": "Global Technology Adoption Survey", "tail": "GTAS"}}, {"has_timeframe": {"head": "Global Technology Adoption Survey", "tail": "2022"}}, {"has_geography": {"head": "Global Technology Adoption Survey", "tail": "50 countries"}}, {"has_organization": {"head": "Global Technology Adoption Survey", "tail": "International Institute for Digital Innovation"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "digital development and technology adoption"}}
+{"input": "of Forced Migration. Paper presented at the Annual Meeting of the Population Association of America, Dallas, TX. Maystadt, J. F., and Verwimp, P. (2014). Winners and Losers among a Refugee-Hosting Population. Economic Development and Cultural Change, 62(4), 769-809. MoE. (2022). Education Statistics Annual Abstract (ESAA). MoE MoLSA. (2019). Revised Directives on the Issuance of Work Permits for Expatriates in Ethiopia. MoTRI, MoLS , MoR and RRS. (2023). Memorandum of Understanding (MoU) to allow recognized asylum seekers and refugees to engaged in formal business. MoLS and RRS. (2023). MoU on technical and vocational training, work permit, job creation and livelihood improvement, and related issues for refugees. MoLS and RRS. (2023). Technical and vocational training for refugees, job creation and livelihood improvement, work permit and related issues service delivery manual (Standard Operating Procedure). Muna, S. (2019). In Pursuit of Self-reliance: Perspectives of Refugees in Jordan. Archnet-IJAR, 13(3), 612-626. https://doi. org/10.1108/ARCH-04-2019-0085 Nguyen, N. T. V., Savadogo, A., and Tanaka, T. (2021). Refugees in Chad: The Road Forward. The World Bank. Norman, T., Borjesson, M., and Anderstig, C. (2017). Labor Market Accessibility and Unemployment. Journal of Transport Economics and Policy. Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). Informing Durable Solutions by", "output": {"entities": {"named_data": ["Education Statistics Annual Abstract"], "organization": ["MoE"]}, "relations": [{"has_organization": {"head": "Education Statistics Annual Abstract", "tail": "MoE"}}, {"used_by": {"head": "Education Statistics Annual Abstract", "tail": "MoE"}}]}, "_meta": {"entry_id": 1083, "dataset_name": "Education Statistics Annual Abstract"}}
+{"input": "In assessing climate resilience, the 2022 Climate Vulnerability Assessment Report (CVAR) released by the Global Climate Institute provides a comprehensive analysis of vulnerable regions. This report, which covers various geographic areas including Southeast Asia and Sub-Saharan Africa, has been utilized extensively by Oxfam to inform their disaster risk reduction strategies. The findings from CVAR have been instrumental in shaping Oxfam's interventions during natural disasters, highlighting the urgent need for adaptive measures to protect communities at risk.", "output": {"entities": {"named_data": ["2022 Climate Vulnerability Assessment Report", "CVAR"], "organization": ["Global Climate Institute", "Oxfam"], "acronym": ["Climate Vulnerability Assessment Report", "CVAR"], "year": ["2022"], "geography": ["Southeast Asia", "Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "2022 Climate Vulnerability Assessment Report", "tail": "Global Climate Institute"}}, {"used_by": {"head": "2022 Climate Vulnerability Assessment Report", "tail": "Oxfam"}}, {"has_acronym": {"head": "Climate Vulnerability Assessment Report", "tail": "CVAR"}}, {"has_timeframe": {"head": "2022 Climate Vulnerability Assessment Report", "tail": "2022"}}, {"has_geography": {"head": "2022 Climate Vulnerability Assessment Report", "tail": "Southeast Asia"}}, {"has_geography": {"head": "2022 Climate Vulnerability Assessment Report", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "Existing coastal flood maps covering Vietnam, most prominently the Global Tide and Surge Reanalysis (GTSR) data set (Muis et al. 2016), are expected to underestimate coastal flood risk in Vietnam.\n\nIt is based on a global digital elevation map with a resolution of 90 meters at the equator that has been corrected for several errors typically present in spaceborn elevation models (Yamazaki et al. 2017).\n\nThese curves have been generated by statistically postprocessing the results from storm surge modeling with a wide range of possible typhoons (Ministry of Agriculture and Rural Development, 2012).\n\nTyphoon wind speeds used in this analysis were produced by a global model of cyclone winds calibrated\non over 2,500 past cyclones, terrain composure, and ocean depth. It contains the modelled maximum\nwind speed at every location in Vietnam for typhoons occurring, on average, every 50, 100, and 1000\nyears (figure 2.2). The data is in raster format has a grid resolution of roughly 30 by 30 kilometers (UNDRR,\n2015).\n\n\n**Figure 2.2: Wind speeds over Vietnam in typhoons of varying severity**", "output": {"entities": {"named_data": ["Global Tide and Surge Reanalysis"], "organization": ["Ministry of Agriculture and Rural Development"]}, "relations": [{"used_by": {"head": "Global Tide and Surge Reanalysis", "tail": "Ministry of Agriculture and Rural Development"}}]}, "_meta": {"entry_id": 1093, "dataset_name": "Global Tide and Surge Reanalysis"}}
+{"input": "The COVID-19 Panel Phone Survey of Households sample is nationally representative, representative of Bamako, and representative of both urban and rural areas.\n\nThe EHCVM sample itself covered 8,390 households across Mali and is nationally representative, representative of Bamako, and representative of both urban and rural areas. The survey relies on a multi-module instrument covering topics including a household's socio-economic characteristics, time use, production activities, and welfare indicators such as consumption expenditure and food security.\n\nuse sampling weights derived from the 2018 EHCVM sampling frame and adjusted for response rates\n\n\nin the COVID-19 Panel Phone Survey of Households. These sampling weights are applied both in our\n\nIn this study, we use the FAO's Food Insecurity Experience Scale (FIES) as primary outcome of interest. The FIES aims to measure food insecurity based on the direct experiences of people relating to food security (Ballard _et_ _al._, 2013; Smith _et_ _al._, 2017).", "output": {"entities": {"named_data": ["EHCVM"], "organization": ["FAO"]}, "relations": [{"used_by": {"head": "EHCVM", "tail": "FAO"}}]}, "_meta": {"entry_id": 23, "dataset_name": "EHCVM"}}
+{"input": "The recent analysis included findings from the National Trade Insights Report and the Industrial Competitiveness Assessment. These reports provide a comprehensive overview of the trade dynamics and industrial activities that are shaping the economy. Insights derived from these datasets are critical for understanding the broader economic landscape.", "output": {"entities": {"named_data": ["National Trade Insights Report", "Industrial Competitiveness Assessment"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "Recent analyses of the Gender and Development Survey (GDS) conducted in 2022 reveal significant trends in women's economic participation across various regions. The survey, published by the International Institute for Gender Studies (IIGS), has been instrumental for research conducted by the United Nations Development Programme (UNDP) in several countries, including Nigeria and India. In addition, the Women’s Empowerment Index (WEI) from 2020, a comprehensive metric developed by the Global Policy Foundation (GPF), has been utilized by the World Economic Forum (WEF) to assess gender equality progress globally.", "output": {"entities": {"named_data": ["Gender and Development Survey", "Women’s Empowerment Index"], "organization": ["International Institute for Gender Studies", "United Nations Development Programme", "Global Policy Foundation", "World Economic Forum"], "acronym": ["GDS", "WEI"], "year": ["2022", "2020"], "geography": ["Nigeria", "India"]}, "relations": [{"has_organization": {"head": "Gender and Development Survey", "tail": "International Institute for Gender Studies"}}, {"used_by": {"head": "Gender and Development Survey", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Gender and Development Survey", "tail": "GDS"}}, {"has_timeframe": {"head": "Gender and Development Survey", "tail": "2022"}}, {"has_geography": {"head": "Gender and Development Survey", "tail": "Nigeria"}}, {"has_geography": {"head": "Gender and Development Survey", "tail": "India"}}, {"has_organization": {"head": "Women’s Empowerment Index", "tail": "Global Policy Foundation"}}, {"used_by": {"head": "Women’s Empowerment Index", "tail": "World Economic Forum"}}, {"has_acronym": {"head": "Women’s Empowerment Index", "tail": "WEI"}}, {"has_timeframe": {"head": "Women’s Empowerment Index", "tail": "2020"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "gender equality and women's economic empowerment"}}
+{"input": "Understanding the complexities of forced displacement requires careful examination of data sources such as the Global Refugee Assessment Report and the National Migration Profile for 2022. These reports provide valuable insights into the trends and challenges faced by displaced populations globally, emphasizing the need for targeted policy responses and international cooperation.", "output": {"entities": {"named_data": ["Global Refugee Assessment Report", "National Migration Profile for 2022"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "forced displacement, refugees, and migration"}}
+{"input": "The 2022 Social Protection Assessment Report (SPAR) provides a comprehensive analysis of the effectiveness of safety net programs in Eastern Africa. This report, published by the Eastern Africa Development Bank, focuses on initiatives implemented in Kenya, Tanzania, and Uganda from 2018 to 2022. It highlights various strategies adopted to enhance social welfare and reduce poverty among vulnerable populations. The findings are intended to inform policy decisions and improve the design of future programs. Although the report is primarily used by government agencies and NGOs, it also serves as a resource for academic researchers interested in social protection mechanisms.", "output": {"entities": {"named_data": ["Social Protection Assessment Report", "SPAR"], "organization": ["Eastern Africa Development Bank"], "acronym": ["SPAR"], "year": ["2022", "2018 to 2022"], "geography": ["Kenya", "Tanzania", "Uganda"]}, "relations": [{"has_acronym": {"head": "Social Protection Assessment Report", "tail": "SPAR"}}, {"has_timeframe": {"head": "Social Protection Assessment Report", "tail": "2018 to 2022"}}, {"has_geography": {"head": "Social Protection Assessment Report", "tail": "Kenya"}}, {"has_geography": {"head": "Social Protection Assessment Report", "tail": "Tanzania"}}, {"has_geography": {"head": "Social Protection Assessment Report", "tail": "Uganda"}}, {"has_organization": {"head": "Social Protection Assessment Report", "tail": "Eastern Africa Development Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "social protection and safety nets"}}
+{"input": "The 2020 Agricultural Production Report (APR) provides detailed statistics on crop yields across various regions, published by the Food and Agriculture Organization (FAO). This dataset has been instrumental for researchers, including the International Food Policy Research Institute (IFPRI), who utilized the APR to analyze trends in food security in Sub-Saharan Africa. Furthermore, the Household Nutritional Assessment Survey (HNAS) 2021, compiled by the World Health Organization (WHO), focuses on dietary intake among vulnerable populations and has seen extensive use by local NGOs for targeted interventions in nutrition programs. Additionally, both the World Bank and the Global Agriculture and Food Security Program (GAFSP) have drawn on the 2019 Food Supply Chain Database to inform their policy recommendations regarding sustainable agricultural practices in Southeast Asia.", "output": {"entities": {"named_data": ["Agricultural Production Report", "Household Nutritional Assessment Survey", "Food Supply Chain Database"], "organization": ["Food and Agriculture Organization", "International Food Policy Research Institute", "World Health Organization", "Global Agriculture and Food Security Program", "World Bank"], "acronym": ["APR", "HNAS", "GAFSP"], "year": ["2020", "2021", "2019"], "geography": ["Sub-Saharan Africa", "Southeast Asia"]}, "relations": [{"has_organization": {"head": "Agricultural Production Report", "tail": "Food and Agriculture Organization"}}, {"used_by": {"head": "Agricultural Production Report", "tail": "International Food Policy Research Institute"}}, {"has_timeframe": {"head": "Agricultural Production Report", "tail": "2020"}}, {"has_geography": {"head": "Agricultural Production Report", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Household Nutritional Assessment Survey", "tail": "World Health Organization"}}, {"used_by": {"head": "Household Nutritional Assessment Survey", "tail": "local NGOs"}}, {"has_timeframe": {"head": "Household Nutritional Assessment Survey", "tail": "2021"}}, {"has_organization": {"head": "Food Supply Chain Database", "tail": "Global Agriculture and Food Security Program"}}, {"used_by": {"head": "Food Supply Chain Database", "tail": "World Bank"}}, {"has_timeframe": {"head": "Food Supply Chain Database", "tail": "2019"}}, {"has_geography": {"head": "Food Supply Chain Database", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "agriculture, food security, and nutrition"}}
+{"input": "3 For a disaster to be listed in the EM-DAT database, at least one of the following criteria should be met: (i) 10 or more people are reported killed; (ii) 100 people are reported affected; (iii) a state of emergency is declared; (iv) a call for international assistance is issued. 4 The study was carried out by a consortium including the OECD, Risk Management Solutions, CIRED, Météo-France, NATCOM PMC, and the Indian Institute for Technology Bombay at Mumbai, and published in Ranger et al. (2011).\n\nThen, the population and assets exposed to flood risks is assessed, using data on population and assets collected by Risk Management Solutions from an insurance database developed for the assessment of earthquake risks.\n\nIn the absence of vulnerability curves for the buildings that can be found in Mumbai, the analysis uses \"average damage ratio\". It is assumed that when a property is flooded, a constant share of its value is lost, regardless of the water level and the detailed characteristics of buildings. Using three different techniques (based on published loss estimates for the 2005 floods, insurance data for the 2005 floods, and simple", "output": {"entities": {"named_data": ["EM-DAT database"], "organization": ["a consortium including the OECD, Risk Management Solutions, CIRED, Météo-France, NATCOM PMC, and the Indian Institute for Technology Bombay"]}, "relations": [{"used_by": {"head": "EM-DAT database", "tail": "a consortium including the OECD, Risk Management Solutions, CIRED, Météo-France, NATCOM PMC, and the Indian Institute for Technology Bombay"}}]}, "_meta": {"entry_id": 131, "dataset_name": "EM-DAT database"}}
+{"input": "nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016).", "output": {"entities": {"named_data": ["Multiple Indicator Cluster Surveys"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "Multiple Indicator Cluster Surveys", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 1059, "dataset_name": "Multiple Indicator Cluster Surveys"}}
+{"input": "Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques-", "output": {"entities": {"named_data": ["Global Preference Survey"], "organization": ["Falk et al. (2022)"]}, "relations": [{"has_organization": {"head": "Global Preference Survey", "tail": "Falk et al. (2022)"}}]}, "_meta": {"entry_id": 727, "dataset_name": "Global Preference Survey"}}
+{"input": "street vending (48 % of those with at least 1 IGA), food processing for sale, including baking, cooking, and drying (16 %), and home production of crops, livestock, and fish (11 %). It is important to note that the EPAG program was not targeted toward the most vulnerable segments of Liberian society, but rather toward young women with enough education to be able to benefit from a training program of this nature. Based on comparisons from the 2007 CWIQ (Core Welfare Indicators Questionnaire) survey, the average EPAG participant is more educated, more literate, more likely to be engaged in an income-generating activity, and owns more assets than the average Liberian women of the same age group: mobile phone ownership was high (63 %), as was the proportion reporting that they had some money of their own (79 %). Even compared to other similar residents of Monrovia, the EPAG participants are better educated and have higher income. A strong sense of female empowerment at baseline emerges from the sections of the survey instrument having to do with self-confidence and agency.", "output": {"entities": {"named_data": ["CWIQ"], "organization": ["EPAG"]}, "relations": [{"used_by": {"head": "CWIQ", "tail": "EPAG"}}]}, "_meta": {"entry_id": 61, "dataset_name": "CWIQ"}}
+{"input": "The Urban Infrastructure Assessment Report 2022 provides critical insights into the state of urban facilities across various regions. This report, published by the Global Urban Development Institute, highlights the challenges faced by cities in maintaining infrastructure amidst rapid population growth.", "output": {"entities": {"named_data": ["Urban Infrastructure Assessment Report 2022"], "organization": ["Global Urban Development Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "Global Urban Development Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "urban infrastructure and transportation planning"}}
+{"input": "such as education and caste. We also construct measures of social proximity between a migrant ’ s place of birth and each possible destination, using detailed available data on ethnicity, caste, language, and religion. We also investigate a number of factors that may influence the choice of migration destination but have not received much attention in the existing literature. Fafchamps and Shilpi (2009) have shown that the subjective welfare cost of geographical isolation is high. To investigate this issue, we include regressors controlling for population density and for the average distance to various amenities. Fafchamps and Shilpi (2008) have further shown that migrants are concerned with their welfare relative to that of their birth district as well as to that in their destination location. We examine whether relative welfare considerations influence the choice of migration destination. Additional controls include distance and prices. The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal. The diverse terrain of Nepal along with geographical variation in amenities makes it ideal for our study. The mountainous nature of Nepal means that the country faces daunting challenges in the provision of transport and energy infrastructure. These challenges are unique to Nepal, however. Similar constraints are faced by many developing countries — or regions within such countries.", "output": {"entities": {"named_data": ["2001 population Census data"], "organization": ["Fafchamps and Shilpi"]}, "relations": [{"used_by": {"head": "2001 population Census data", "tail": "Fafchamps and Shilpi"}}]}, "_meta": {"entry_id": 141, "dataset_name": "2001 population Census data"}}
+{"input": "[8] F. N. P. Nimoh, T. P. Beltramo, J. R. Fix, F. K. Appler, U. J. Pape, and L. A. Rios Rivera, ‘Understanding the Socioeconomic Conditions of\nthe Stateless Shona Community in Kenya: Results from the 2019 Socioeconomic Survey’, Dec. 2020. Accessed: Apr. 02, 2024. [Online].\nAvailable: https://documents.worldbank.org/en/publication/documentsreports/documentdetail/356511608745182603/Understanding-the-Socioeconomic-Conditions-of-the-Stateless-Shona-Community-inKenya-Results-from-the-2019-Socioeconomic-Survey\n\n[9] U. J. Pape _et al._, ‘How COVID-19 Continues to Affect Lives of Refugees in Kenya : Rapid Response Phone Survey - Rounds 1 to 5’, World\nBank Group, Washington, D.C., Policy Note 166098, Oct. 2021. Accessed: Oct. 10, 2024. [Online]. Available:\nhttps://documents1.worldbank.org/curated/en/202201637042522937/pdf/How-COVID-19-Continues-to-Affect-Lives-of-Refugeesin-Kenya-Rapid-Response-Phone-Survey-Rounds-1-to-5.pdf\n\n[10] Kenya National Bureau of Statistics and ICF, ‘Kenya Demographic and Health Survey 2022. Key Indicators Report’, KNBS and ICF,\n\nNairobi, Kenya, and Rockville, Maryland, USA, 2023. Accessed: Oct. 10, 2024. [Online]. Available: https://www.knbs.or.ke/wpcontent/uploads/2023/08/Kenya-Demographic-and-Health-Survey-2022-Key-Indicators-Report.pdf", "output": {"entities": {"named_data": ["2019 Socioeconomic Survey"], "organization": ["Kenya National Bureau of Statistics", "F. N. P. Nimoh, T. P. Beltramo, J. R. Fix, F. K. Appler, U. J. Pape, and L. A. Rios Rivera"]}, "relations": [{"has_organization": {"head": "2019 Socioeconomic Survey", "tail": "Kenya National Bureau of Statistics"}}, {"used_by": {"head": "2019 Socioeconomic Survey", "tail": "F. N. P. Nimoh, T. P. Beltramo, J. R. Fix, F. K. Appler, U. J. Pape, and L. A. Rios Rivera"}}]}, "_meta": {"entry_id": 1280, "dataset_name": "2019 Socioeconomic Survey"}}
+{"input": "The Global Refugee Report 2022 (GRR2022) provides comprehensive data on the number of displaced populations worldwide, specifically focusing on the situations in South America and Sub-Saharan Africa. This dataset, published by the International Organization for Migration (IOM), covers the years 2018 to 2022 and illustrates the growing challenges faced by refugees in these regions. Moreover, the Africa Migration Trends Survey 2021 (AMTS2021) offers valuable insights into migration patterns across African nations, and although it is frequently cited by various NGOs, it lacks an official release from a recognized organization. Additionally, the South America Displacement Assessment 2023 (SADA2023) presents updated statistics on forced migration in countries like Colombia and Venezuela, emphasizing the urgency of the situation and the need for international intervention.", "output": {"entities": {"named_data": ["Global Refugee Report 2022", "Africa Migration Trends Survey 2021", "South America Displacement Assessment 2023"], "organization": ["International Organization for Migration", "NGOs"], "acronym": ["GRR2022", "AMTS2021", "SADA2023"], "year": ["2022", "2018 to 2022", "2021", "2023"], "geography": ["South America", "Sub-Saharan Africa", "Africa", "Colombia", "Venezuela"]}, "relations": [{"has_acronym": {"head": "Global Refugee Report 2022", "tail": "GRR2022"}}, {"has_timeframe": {"head": "Global Refugee Report 2022", "tail": "2018 to 2022"}}, {"has_geography": {"head": "Global Refugee Report 2022", "tail": "South America"}}, {"has_geography": {"head": "Global Refugee Report 2022", "tail": "Sub-Saharan Africa"}}, {"used_by": {"head": "Africa Migration Trends Survey 2021", "tail": "NGOs"}}, {"has_acronym": {"head": "Africa Migration Trends Survey 2021", "tail": "AMTS2021"}}, {"has_timeframe": {"head": "Africa Migration Trends Survey 2021", "tail": "2021"}}, {"has_acronym": {"head": "South America Displacement Assessment 2023", "tail": "SADA2023"}}, {"has_timeframe": {"head": "South America Displacement Assessment 2023", "tail": "2023"}}, {"has_geography": {"head": "South America Displacement Assessment 2023", "tail": "Colombia"}}, {"has_geography": {"head": "South America Displacement Assessment 2023", "tail": "Venezuela"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "forced displacement, refugees, and migration"}}
+{"input": "Recent analyses on poverty headcount have drawn attention to the findings from the Global Poverty Assessment and the Inequality Index Report. Both datasets provide crucial insights into the socioeconomic challenges faced by developing countries, highlighting disparities in income and access to resources. The Global Poverty Assessment emphasizes the multidimensional aspects of poverty, while the Inequality Index Report focuses on the distribution of wealth within populations.", "output": {"entities": {"named_data": ["Global Poverty Assessment", "Inequality Index Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "poverty headcount and inequality measurement"}}
+{"input": "The Digital Adoption Index (DAI) provides comprehensive insights into how various countries are embracing digital technologies. It is produced by the International Telecommunication Union and covers data from 2022. This dataset is instrumental for policymakers in understanding trends in technology adoption across different regions.", "output": {"entities": {"named_data": ["Digital Adoption Index"], "organization": ["International Telecommunication Union"], "acronym": ["DAI"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Digital Adoption Index", "tail": "International Telecommunication Union"}}, {"has_acronym": {"head": "Digital Adoption Index", "tail": "DAI"}}, {"has_timeframe": {"head": "Digital Adoption Index", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "digital development and technology adoption"}}
+{"input": "The Water Quality Assessment Report published by the International Water Association provides crucial insights into the state of drinking water across various regions. This report, which highlights the challenges and progress in water quality management, is intended for use by local governments and NGOs aiming to improve sanitation standards. Notably, the data sheds light on the ongoing initiatives undertaken by organizations globally to enhance access to safe water.", "output": {"entities": {"named_data": ["Water Quality Assessment Report"], "organization": ["International Water Association", "local governments", "NGOs"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Water Quality Assessment Report", "tail": "International Water Association"}}, {"used_by": {"head": "Water Quality Assessment Report", "tail": "local governments"}}, {"used_by": {"head": "Water Quality Assessment Report", "tail": "NGOs"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "water, sanitation, and hygiene"}}
+{"input": "The Energy Access Assessment Report 2022 provides crucial insights into the current status of electricity access in various regions around the globe. This report, published by the International Renewable Energy Agency (IRENA), highlights the progress made in renewable energy transitions and identifies areas needing further investment. The findings serve as a vital resource for policymakers aiming to meet global sustainability goals.", "output": {"entities": {"named_data": ["Energy Access Assessment Report 2022"], "organization": ["International Renewable Energy Agency", "IRENA"], "acronym": ["IRENA"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Energy Access Assessment Report 2022", "tail": "International Renewable Energy Agency"}}, {"has_acronym": {"head": "International Renewable Energy Agency", "tail": "IRENA"}}, {"has_timeframe": {"head": "Energy Access Assessment Report 2022", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}
+{"input": "The Southern Africa Social Protection Assessment Report 2021 (SASPAR 2021) provides an in-depth analysis of social safety nets implemented in the region. It highlights key data collected from various countries, particularly focusing on Mozambique and Zimbabwe, during the period 2019-2021. The World Bank has played a significant role in producing this report, which is pivotal for understanding the efficiency of social protection programs in these nations. Meanwhile, insights from the East Africa Safety Net Database (EASNDB) reveal trends across Kenya and Uganda, covering the 2020-2022 timeframe. This database is frequently cited by local NGOs to evaluate the impact of safety net interventions in their respective areas.", "output": {"entities": {"named_data": ["Southern Africa Social Protection Assessment Report 2021", "East Africa Safety Net Database"], "organization": ["World Bank", "local NGOs"], "acronym": ["SASPAR", "EASNDB"], "year": ["2021", "2019-2021", "2020-2022"], "geography": ["Mozambique", "Zimbabwe", "Kenya", "Uganda"]}, "relations": [{"has_acronym": {"head": "Southern Africa Social Protection Assessment Report 2021", "tail": "SASPAR"}}, {"has_timeframe": {"head": "Southern Africa Social Protection Assessment Report 2021", "tail": "2021"}}, {"has_geography": {"head": "Southern Africa Social Protection Assessment Report 2021", "tail": "Mozambique"}}, {"has_geography": {"head": "Southern Africa Social Protection Assessment Report 2021", "tail": "Zimbabwe"}}, {"has_acronym": {"head": "East Africa Safety Net Database", "tail": "EASNDB"}}, {"has_timeframe": {"head": "East Africa Safety Net Database", "tail": "2020-2022"}}, {"has_geography": {"head": "East Africa Safety Net Database", "tail": "Kenya"}}, {"has_geography": {"head": "East Africa Safety Net Database", "tail": "Uganda"}}, {"has_organization": {"head": "Southern Africa Social Protection Assessment Report 2021", "tail": "World Bank"}}, {"used_by": {"head": "East Africa Safety Net Database", "tail": "local NGOs"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "social protection and safety nets"}}
+{"input": "In the latest analysis of urban transportation trends, the Urban Mobility Assessment Report 2023 published by the Global Transport Institute highlights significant shifts in commuting patterns across metropolitan areas. This report is crucial for policymakers and city planners, particularly those at the Regional Urban Planning Agency, who utilized this dataset to inform their strategic initiatives in urban infrastructure. Furthermore, the report reveals data from the National Infrastructure Database (NID), which offers comprehensive metrics on transportation usage in 2022. The NID, developed by the National Infrastructure Council, serves as a vital resource for understanding the impact of recent policy changes in major cities such as Atlanta and Seattle.", "output": {"entities": {"named_data": ["Urban Mobility Assessment Report 2023", "National Infrastructure Database"], "organization": ["Global Transport Institute", "Regional Urban Planning Agency", "National Infrastructure Council"], "acronym": ["NID"], "year": ["2023", "2022"], "geography": ["Atlanta", "Seattle"]}, "relations": [{"has_organization": {"head": "Urban Mobility Assessment Report 2023", "tail": "Global Transport Institute"}}, {"used_by": {"head": "Urban Mobility Assessment Report 2023", "tail": "Regional Urban Planning Agency"}}, {"has_acronym": {"head": "National Infrastructure Database", "tail": "NID"}}, {"has_organization": {"head": "National Infrastructure Database", "tail": "National Infrastructure Council"}}, {"has_timeframe": {"head": "National Infrastructure Database", "tail": "2022"}}, {"has_geography": {"head": "National Infrastructure Database", "tail": "Atlanta"}}, {"has_geography": {"head": "National Infrastructure Database", "tail": "Seattle"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "urban infrastructure and transportation planning"}}
+{"input": "68.8% 2024-Q2 2021-Q2 67.5% 2022-Q2 40.3 40.3 40.4 40.4 Source: Deloitte own elaboration based of Eurostat data (Labour Force Survey).\n\nSource: Deloitte own elaboration based of Eurostat data (Labour Force Survey).\n\nPart-time Full-time 31 Deloitte has not received data that would be detailed as to citizenship, poviat, sex, age group, occupational group, and ZUS insurance code that would be suitable for econometric approach.\n\n42", "output": {"entities": {"named_data": ["Labour Force Survey"], "organization": ["Eurostat", "Deloitte"]}, "relations": [{"has_organization": {"head": "Labour Force Survey", "tail": "Eurostat"}}, {"used_by": {"head": "Labour Force Survey", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1312, "dataset_name": "Labour Force Survey"}}
+{"input": "resilience: learning from the ecological wisdom of living with floods in the Vietnamese Mekong Delta. Landsc Urban Plan 155:69–78 McElwee P et al (2017) Flood vulnerability among rural households in the red River Delta of Vietnam: implications for future climate change risk and adaptation. Nat Hazards 86(1):465–492 Ministry of Natural Resources and Environment (2009) Climate Change, Sea Level Rise Scenarios for Vietnam Patankar A (2015) The Exposure, Vulnerability and Adaptive Capacity of Households to Floods in Mumbai. World Bank Policy Research Working Paper No. 7481 PUMA (2013) Interim Report Version 1.0.^ Platform for Urban Management and Analysis (PUMA) Software Development and Satellite Imagery Processing Consultants Rozenberg J, Hallegatte S (2016). Modeling the impacts of climate change on future Vietnamese households: a micro-simulation approach Scussolini, P., Aerts, J. C., Jongman, B., Bouwer, L. M., Winsemius, H. C., de Moel, H., & Ward, P. J. (2016). FLOPROS: an evolving global database of flood protection standards. Nat Hazards Earth Syst Sci, 16(5), 1049–1061 Smith A, Freer J, Bates P, Sampson C (2014) Comparing ensemble projections of flooding against flood estimation by continuous simulation. J Hydrol 511:205–219 Thoai TQ et al (2018) Determinants of farmers’ adaptation to climate change in agricultural production in the", "output": {"entities": {"named_data": ["FLOPROS"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "FLOPROS", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1205, "dataset_name": "FLOPROS"}}
+{"input": "The Gender Equality Impact Assessment 2022, published by the Global Women's Institute, provides critical insights into the socio-economic barriers faced by women in Southeast Asia. This dataset has been instrumental for various organizations, including the Asian Development Bank (ADB), which utilized the findings to shape their policies aimed at promoting women's economic empowerment in the region. Source: Global Women's Institute elaboration based on Gender Equality Impact Assessment 2022.", "output": {"entities": {"named_data": ["Gender Equality Impact Assessment 2022"], "organization": ["Global Women's Institute", "Asian Development Bank"], "acronym": ["ADB"], "year": ["2022"], "geography": ["Southeast Asia"]}, "relations": [{"has_organization": {"head": "Gender Equality Impact Assessment 2022", "tail": "Global Women's Institute"}}, {"used_by": {"head": "Gender Equality Impact Assessment 2022", "tail": "Asian Development Bank"}}, {"has_acronym": {"head": "Asian Development Bank", "tail": "ADB"}}, {"has_timeframe": {"head": "Gender Equality Impact Assessment 2022", "tail": "2022"}}, {"has_geography": {"head": "Gender Equality Impact Assessment 2022", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
+{"input": "the sample frame were surveyed. In this way, all the camps in the sample frame were selected in the sample and were surveyed. For host households, areas within 5-kilometer radius of the camps were divided into EAs of 300 by 300 meters, with only residential EAs as per Open Street Maps included in the sample frame. SESRE, on the other hand, used a stratified, two-stage cluster sample design. Initially, camps were divided into EAs, and pseudo EAs were created from the proGRES database by grouping 150-200 households consecutively. EAs and households within those EAs were then selected. For host households, EAs adjacent to refugee camps were used as the sampling frame. While the definition of host households differed between SPS and SESRE, both surveys shared similarities in the selection of EAs and the random sampling of households within those EAs. In SPS, all households within the selected EAs for host community sampling were listed, and 12 households were randomly chosen and surveyed per EA. SESRE also selected 12 refugee and host households per EA, treating EAs as the Primary Sampling Unit and households as the Secondary Sampling Unit. (i) The distinct sampling designs and objectives of the two surveys render", "output": {"entities": {"named_data": ["proGRES database"], "organization": ["SESRE"]}, "relations": [{"used_by": {"head": "proGRES database", "tail": "SESRE"}}]}, "_meta": {"entry_id": 186, "dataset_name": "proGRES database"}}
+{"input": "(henceforth CRU), provided by the Climatic Research Unit of the University of East\n\nand satellite-based observations. The data enable us to characterize historical climate\n\n<!-- PAGE 6 -->\n**2.1 Assignment of reliability weights to the eight GCMs, based on their**\n**historical \"goodness of fit\" to the CRU data**", "output": {"entities": {"named_data": ["CRU data"], "organization": ["Climatic Research Unit of the University of East", "us"]}, "relations": [{"has_organization": {"head": "CRU data", "tail": "Climatic Research Unit of the University of East"}}, {"used_by": {"head": "CRU data", "tail": "us"}}]}, "_meta": {"entry_id": 102, "dataset_name": "CRU data"}}
+{"input": "complete census of land parcels in Barafu and Kati, known as the Tanzanian Land Rights\n\n\nSurvey (TLRS). Households were identified using records and maps from the Kinondoni\n\n\nMunicipality, which had created a listing of all households in the area to assist with the\n\n**Notes:** data are from Tanzanian Land Rights survey. Sample restricted to dual-headed households in\ntreatment blocks.\n\n\nlevels of female land ownership: investigating the gender breakdown of land ownership in\n\nusing baseline data from the experimental intervention, which is discussed in more\n\n\ndetail in the following section. Households in two unplanned settlements in Dar\n\n\nes Salaam were asked a series of questions about the _de_ _facto_ ownership of land,\n\n5To avoid priming, households were not asked directly about female ownership. Instead, they were\nasked to list all members of the household that were default owners, must be consulted before a sale, or\nwould be included on a CRO.\n6Section 191(2) of the 1999 Land Act and section 58 of the (1971) Law of Marriage Act.\n7Authors' calculations using data from the Kinondoni municipal data.\n8Section 159(6) of the 1999 Land Act.\n\n\n8\n\n\n\n<!-- PAGE 12 -->\nTable 1: Female land ownership in Dar es Salaam", "output": {"entities": {"named_data": ["Tanzanian Land Rights survey"], "organization": ["Authors"]}, "relations": [{"used_by": {"head": "Tanzanian Land Rights survey", "tail": "Authors"}}]}, "_meta": {"entry_id": 224, "dataset_name": "Tanzanian Land Rights survey"}}
+{"input": "The Climate Resilience Assessment Report 2022 provides vital data on the vulnerabilities faced by coastal communities in the Pacific region due to climate change. Published by the International Disaster Management Organization, this report serves as a crucial resource for policymakers aiming to enhance resilience strategies against potential disasters.", "output": {"entities": {"named_data": ["Climate Resilience Assessment Report 2022"], "organization": ["International Disaster Management Organization"], "acronym": [], "year": ["2022"], "geography": ["Pacific region"]}, "relations": [{"has_organization": {"head": "Climate Resilience Assessment Report 2022", "tail": "International Disaster Management Organization"}}, {"has_timeframe": {"head": "Climate Resilience Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Climate Resilience Assessment Report 2022", "tail": "Pacific region"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey.", "output": {"entities": {"named_data": ["Displaced People Survey"], "organization": ["Listening to Displaced People Survey"]}, "relations": [{"has_organization": {"head": "Displaced People Survey", "tail": "Listening to Displaced People Survey"}}]}, "_meta": {"entry_id": 539, "dataset_name": "Displaced People Survey"}}
+{"input": "In the recent report on social safety nets, the Integrated Social Protection Data (ISPD) was published by the Ministry of Social Affairs. This dataset provides crucial insights into the effectiveness of various social programs across the country. Furthermore, it has been referenced by the International Institute for Social Policy (IISP) to highlight trends and inform policy recommendations about assistance distribution.", "output": {"entities": {"named_data": ["Integrated Social Protection Data"], "organization": ["Ministry of Social Affairs", "International Institute for Social Policy"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Integrated Social Protection Data", "tail": "Ministry of Social Affairs"}}, {"used_by": {"head": "Integrated Social Protection Data", "tail": "International Institute for Social Policy"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "social protection and safety nets"}}
+{"input": "The East African Trade Assessment Report 2022 provides valuable insights into regional trade dynamics, published by the Trade Development Agency (TDA). This report has been extensively used by the African Economic Research Consortium (AERC) to analyze trade patterns and formulate policy recommendations. In addition, the 2020 Ghana Industrial Survey, prepared by the Ghana Statistical Service (GSS), incorporates economic competitiveness metrics that have aided the World Bank in assessing investment opportunities in the region. Both datasets emphasize the importance of robust economic structures in enhancing trade performance across countries in Africa.", "output": {"entities": {"named_data": ["East African Trade Assessment Report 2022", "2020 Ghana Industrial Survey"], "organization": ["Trade Development Agency", "African Economic Research Consortium", "Ghana Statistical Service", "World Bank"], "acronym": ["TDA", "AERC", "GSS"], "year": ["2022", "2020"], "geography": ["East Africa", "Ghana"]}, "relations": [{"has_organization": {"head": "East African Trade Assessment Report 2022", "tail": "Trade Development Agency"}}, {"used_by": {"head": "East African Trade Assessment Report 2022", "tail": "African Economic Research Consortium"}}, {"has_organization": {"head": "2020 Ghana Industrial Survey", "tail": "Ghana Statistical Service"}}, {"used_by": {"head": "2020 Ghana Industrial Survey", "tail": "World Bank"}}, {"has_timeframe": {"head": "East African Trade Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "2020 Ghana Industrial Survey", "tail": "2020"}}, {"has_geography": {"head": "East African Trade Assessment Report 2022", "tail": "East Africa"}}, {"has_geography": {"head": "2020 Ghana Industrial Survey", "tail": "Ghana"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The South Asia Maternal Health Survey (SAMHS) conducted in 2022 provides crucial insights into the reproductive health challenges faced by women in the region. This survey, published by the South Asia Health Consortium, aims to inform policy decisions surrounding maternal care. It covers data from India and Bangladesh, focusing on maternal mortality rates and healthcare access. Additionally, the Comprehensive Maternal Health Study (CMHS) from 2020 highlights disparities in healthcare services across rural and urban areas of Pakistan. The CMHS serves as a critical tool for NGOs and governments working towards improving maternal health outcomes. The findings from both surveys are expected to influence health policy significantly in South Asia.", "output": {"entities": {"named_data": ["South Asia Maternal Health Survey", "Comprehensive Maternal Health Study"], "organization": ["South Asia Health Consortium", "NGOs"], "acronym": ["SAMHS", "CMHS"], "year": ["2022", "2020"], "geography": ["India", "Bangladesh", "Pakistan"]}, "relations": [{"has_acronym": {"head": "South Asia Maternal Health Survey", "tail": "SAMHS"}}, {"has_timeframe": {"head": "South Asia Maternal Health Survey", "tail": "2022"}}, {"has_geography": {"head": "South Asia Maternal Health Survey", "tail": "India"}}, {"has_geography": {"head": "South Asia Maternal Health Survey", "tail": "Bangladesh"}}, {"has_acronym": {"head": "Comprehensive Maternal Health Study", "tail": "CMHS"}}, {"has_timeframe": {"head": "Comprehensive Maternal Health Study", "tail": "2020"}}, {"has_geography": {"head": "Comprehensive Maternal Health Study", "tail": "Pakistan"}}, {"has_organization": {"head": "South Asia Maternal Health Survey", "tail": "South Asia Health Consortium"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "health systems and maternal outcomes"}}
+{"input": "**Total Poverty** **Food Poverty** **Lower Total**\n**Estimation** **Poverty**\n\nParametric R$287 R$507\nNonparametric R$287 R$503\n_Source:_ Own calculations using POF 2017/18.\n\n**Total Poverty** **Food** **Lower Total**\n**Estimation** **Poverty** **Poverty**\n\nParametric R$251 R$443\nNon-parametric R$251 R$441\n_Source:_ Own calculations using POF 2017/18.\n\nDeaton and Zaidi (2002) advert to the fact that implicit rent, as elicited in POF and used here, is a hypothetical concept that could lead to estimations that are not usable.\n\nIn this paper we have presented our estimate of a poverty line for Brazil, using the CBN approach and based on the most recent data (POF 2017/18). Our preferred specification results in a food poverty line, accounting only for nutritional requirements, of R$258 (in 2018 Southeast urban prices) per person per month.\n\nIn comparison with earlier work, mainly based on POF 2003, our poverty lines are generally similar in real\nvalues, although methodologies differ, and consumption patterns have likely changed over time.\nConverted to January 2018 prices and considering São Paulo (mostly metropolitan) lines in the case of\nregional lines, previous estimates range from R$485 to R$532 (Rocha, 2007; Silveira et al., 2007). Ferreira\net al. (2003) used POF 1996 and estimated a lower poverty line of R$477 in January 2018 metropolitan\nSão Paulo prices. Only World Bank (2007) estimated a considerably lower poverty line of R$272 in January\n2018 metropolitan São Paulo prices.", "output": {"entities": {"named_data": ["POF 2017/18"], "organization": ["POF", "World Bank"]}, "relations": [{"has_organization": {"head": "POF 2017/18", "tail": "POF"}}, {"used_by": {"head": "POF 2017/18", "tail": "World Bank"}}]}, "_meta": {"entry_id": 229, "dataset_name": "POF 2017/18"}}
+{"input": "The East Africa Trade Competitiveness Report 2022 provides valuable insights into the trade dynamics among the region's countries. This report, produced by the East Africa Economic Institute, highlights the challenges and opportunities faced by nations such as Kenya and Tanzania. The dataset serves as a critical resource for policymakers and researchers aiming to improve trade policies and economic outcomes. The East Africa Trade Competitiveness Report (EATCR) encompasses data collected from 2020 to 2022, offering a comprehensive overview of trends and performance in the sector.", "output": {"entities": {"named_data": ["East Africa Trade Competitiveness Report 2022", "East Africa Trade Competitiveness Report", "EATCR"], "organization": ["East Africa Economic Institute"], "acronym": ["EATCR"], "year": ["2022", "2020 to 2022"], "geography": ["East Africa", "Kenya", "Tanzania"]}, "relations": [{"has_acronym": {"head": "East Africa Trade Competitiveness Report", "tail": "EATCR"}}, {"has_timeframe": {"head": "East Africa Trade Competitiveness Report 2022", "tail": "2020 to 2022"}}, {"has_geography": {"head": "East Africa Trade Competitiveness Report", "tail": "East Africa"}}, {"has_geography": {"head": "East Africa Trade Competitiveness Report", "tail": "Kenya"}}, {"has_geography": {"head": "East Africa Trade Competitiveness Report", "tail": "Tanzania"}}, {"has_organization": {"head": "East Africa Trade Competitiveness Report", "tail": "East Africa Economic Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The Digital Trends Assessment Report 2022 (DTAR) provides valuable insights into technology adoption across various sectors. This report, produced by the Global Technology Initiative, highlights key trends in digital development in South Africa and showcases data from the Technology Adoption Survey 2021, emphasizing the rapid growth of mobile internet usage. The Technology Adoption Survey (TAS) covers a comprehensive analysis of user behaviors and preferences and is crucial for understanding market dynamics. While the DTAR is widely utilized by numerous stakeholders, it primarily focuses on evaluating the landscape within South Africa, thus offering targeted recommendations for local policymakers.", "output": {"entities": {"named_data": ["Digital Trends Assessment Report 2022", "Technology Adoption Survey 2021", "Technology Adoption Survey"], "organization": ["Global Technology Initiative"], "acronym": ["DTAR", "TAS"], "year": ["2022", "2021"], "geography": ["South Africa"]}, "relations": [{"has_acronym": {"head": "Digital Trends Assessment Report 2022", "tail": "DTAR"}}, {"has_timeframe": {"head": "Digital Trends Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Digital Trends Assessment Report 2022", "tail": "South Africa"}}, {"has_acronym": {"head": "Technology Adoption Survey", "tail": "TAS"}}, {"has_timeframe": {"head": "Technology Adoption Survey 2021", "tail": "2021"}}, {"has_geography": {"head": "Technology Adoption Survey 2021", "tail": "South Africa"}}, {"has_geography": {"head": "Technology Adoption Survey", "tail": "South Africa"}}, {"has_organization": {"head": "Digital Trends Assessment Report 2022", "tail": "Global Technology Initiative"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "digital development and technology adoption"}}
+{"input": "The Financial Inclusion Analysis Report 2022, published by the Global Monetary Fund, provides critical insights into banking access across sub-Saharan Africa. This dataset, which covers the period from 2018 to 2021, has been extensively utilized by the African Development Bank in assessing economic growth strategies. Additionally, the Global Monetary Fund's Annual Economic Outlook (AEO) for 2023 highlights trends in financial behaviors and policies, which will be a valuable resource for researchers at various international development organizations working in the region. Both datasets are instrumental in shaping policy frameworks aimed at enhancing economic resilience in vulnerable communities.", "output": {"entities": {"named_data": ["Financial Inclusion Analysis Report 2022", "Annual Economic Outlook", "Economic Outlook"], "organization": ["Global Monetary Fund", "African Development Bank"], "acronym": ["AEO"], "year": ["2022", "2018 to 2021", "2023"], "geography": ["sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Financial Inclusion Analysis Report 2022", "tail": "Global Monetary Fund"}}, {"used_by": {"head": "Financial Inclusion Analysis Report 2022", "tail": "African Development Bank"}}, {"has_timeframe": {"head": "Financial Inclusion Analysis Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Financial Inclusion Analysis Report 2022", "tail": "2018 to 2021"}}, {"has_geography": {"head": "Financial Inclusion Analysis Report 2022", "tail": "sub-Saharan Africa"}}, {"has_organization": {"head": "Annual Economic Outlook", "tail": "Global Monetary Fund"}}, {"has_timeframe": {"head": "Annual Economic Outlook", "tail": "2023"}}, {"used_by": {"head": "Annual Economic Outlook", "tail": "various international development organizations"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "The recent Climate Adaptation Assessment Report, published by the Global Climate Initiative, highlights various strategies for enhancing resilience to climate change impacts. This report serves as a comprehensive resource for policymakers and environmentalists alike, providing critical insights into adaptive practices. Various organizations, including the Environmental Protection Agency, have utilized this assessment to guide their regional programs aimed at disaster risk reduction and climate adaptation efforts.", "output": {"entities": {"named_data": ["Climate Adaptation Assessment Report"], "organization": ["Global Climate Initiative", "Environmental Protection Agency"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Climate Adaptation Assessment Report", "tail": "Global Climate Initiative"}}, {"used_by": {"head": "Climate Adaptation Assessment Report", "tail": "Environmental Protection Agency"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "The recent analysis of the Economic Impact Assessment Report 2022, which provides detailed insights into the industrial output in Southeast Asia, was published by the Asian Development Bank (ADB). This report has been extensively used by the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) to inform regional policy-making. Additionally, the Industry Competitiveness Data for Vietnam, released in 2021 by the Ministry of Industry and Trade, has been referenced in various academic papers, including those by the Vietnam National University (VNU). Furthermore, the Trade Dynamics Survey 2020, circulated by the World Trade Organization (WTO), serves as a vital resource for research conducted by the Institute of Development Studies (IDS) in understanding trade trends in the Asia-Pacific region.", "output": {"entities": {"named_data": ["Economic Impact Assessment Report 2022", "Industry Competitiveness Data for Vietnam", "Trade Dynamics Survey 2020"], "organization": ["Asian Development Bank", "United Nations Economic and Social Commission for Asia and the Pacific", "Ministry of Industry and Trade", "Vietnam National University", "World Trade Organization", "Institute of Development Studies"], "acronym": [], "year": ["2022", "2021", "2020"], "geography": ["Southeast Asia", "Vietnam", "Asia-Pacific"]}, "relations": [{"has_organization": {"head": "Economic Impact Assessment Report 2022", "tail": "Asian Development Bank"}}, {"used_by": {"head": "Economic Impact Assessment Report 2022", "tail": "United Nations Economic and Social Commission for Asia and the Pacific"}}, {"has_organization": {"head": "Industry Competitiveness Data for Vietnam", "tail": "Ministry of Industry and Trade"}}, {"used_by": {"head": "Industry Competitiveness Data for Vietnam", "tail": "Vietnam National University"}}, {"has_organization": {"head": "Trade Dynamics Survey 2020", "tail": "World Trade Organization"}}, {"used_by": {"head": "Trade Dynamics Survey 2020", "tail": "Institute of Development Studies"}}, {"has_geography": {"head": "Economic Impact Assessment Report 2022", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Industry Competitiveness Data for Vietnam", "tail": "Vietnam"}}, {"has_geography": {"head": "Trade Dynamics Survey 2020", "tail": "Asia-Pacific"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The 2020 National Fertility Survey, conducted by the Ministry of Health in Zambia, provides critical insights into reproductive health trends across the country. The data from this survey has been utilized by UNICEF to inform their family planning programs aimed at reducing maternal mortality. Furthermore, the African Population Database (APD), which was compiled by the African Union in 2021, serves as a vital resource for researchers examining demographic shifts within the continent. This database is frequently referenced by the World Health Organization (WHO) to support public health initiatives targeting population growth and resource allocation strategies.", "output": {"entities": {"named_data": ["2020 National Fertility Survey", "African Population Database"], "organization": ["Ministry of Health", "UNICEF", "African Union", "World Health Organization"], "acronym": ["APD"], "year": ["2020", "2021"], "geography": ["Zambia"]}, "relations": [{"has_organization": {"head": "2020 National Fertility Survey", "tail": "Ministry of Health"}}, {"used_by": {"head": "2020 National Fertility Survey", "tail": "UNICEF"}}, {"has_organization": {"head": "African Population Database", "tail": "African Union"}}, {"used_by": {"head": "African Population Database", "tail": "World Health Organization"}}, {"has_acronym": {"head": "African Population Database", "tail": "APD"}}, {"has_timeframe": {"head": "African Population Database", "tail": "2021"}}, {"has_geography": {"head": "2020 National Fertility Survey", "tail": "Zambia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "demographics, fertility, and population growth"}}
+{"input": "The Environmental Sustainability Assessment Report 2022 provides crucial insights into the impacts of climate change on local ecosystems. This report has been instrumental for various organizations aiming to develop more effective sustainability programs. Published by the Global Environmental Institute, it encompasses a comprehensive analysis of resource allocation and sustainability practices.", "output": {"entities": {"named_data": ["Environmental Sustainability Assessment Report 2022"], "organization": ["Global Environmental Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Environmental Sustainability Assessment Report 2022", "tail": "Global Environmental Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
+{"input": "The Gender Equality and Women's Economic Empowerment Survey (GEWES) conducted in 2022 provides crucial insights into the progress of gender parity in various regions. This survey focuses on the economic empowerment of women in South Asia, particularly highlighting the challenges they face in accessing financial resources. The data illustrates significant disparities in labor force participation rates across countries like India and Bangladesh. Moreover, this dataset is increasingly being referenced by organizations working on gender issues, particularly in formulating policies aimed at improving women's economic status.  With GEWES serving as a critical resource, stakeholders can better understand the landscape of gender equality initiatives in the region.", "output": {"entities": {"named_data": ["Gender Equality and Women's Economic Empowerment Survey"], "organization": ["organizations"], "acronym": ["GEWES"], "year": ["2022"], "geography": ["South Asia", "India", "Bangladesh"]}, "relations": [{"has_acronym": {"head": "Gender Equality and Women's Economic Empowerment Survey", "tail": "GEWES"}}, {"has_timeframe": {"head": "Gender Equality and Women's Economic Empowerment Survey", "tail": "2022"}}, {"has_geography": {"head": "Gender Equality and Women's Economic Empowerment Survey", "tail": "South Asia"}}, {"has_geography": {"head": "Gender Equality and Women's Economic Empowerment Survey", "tail": "India"}}, {"has_geography": {"head": "Gender Equality and Women's Economic Empowerment Survey", "tail": "Bangladesh"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "gender equality and women's economic empowerment"}}
+{"input": "future ice-sheet instability and rapid sea-level rise. _Science_, 311,1747-1750. Pfeffer, W. T., Harper, J.T., and S. O’Neel. 2008: Kinematic constraints on glacier contributions to 21st-century sea-level rise. _Science_, 321, 1340-1343. Rahmsdorf, S. 2007. A semi-empirical approach to projecting future sea-level rise. _Science_, 308, 368-370. Semeniuk, V. 1994. Predicting the effect of sea-level rise on mangroves in Northwestern Australia. _Journal of Coastal Research_, 10, 1050-1076. Sun,G., McNulty, S.G., Amatya, D.M., Skaggs, R.W., Swift, L.W., Shepard, P., and H. Riekerk, 2002. A comparison of watershed hydrology of coastal forested wetlands and the mountainous uplands in the Southern US. _Journal of Hydrology,_ 263, 92-104. Titus, J.G. 1988. _Sea Level Rise and Wetland Loss: An Overview_ . [http://epa.gov/climatechange/effects/downloads/toc_wet_chap1.pdf](http://epa.gov/climatechange/effects/downloads/toc_wet_chap1.pdf) Tol, R.S.J. 2007. The Double Trade-off between Adaptation and Mitigation for Sea Level Rise: An Application of FUND. Mitigation Adaptation Strategy Global Change. 12:741-753. Vafeidis, A.T.; Nicholls, R.J.; McFadden L.; Tol, R.S.J.; Hinkel, J.; Spencer, T.; Grashoff, P.S.; Boot, G., and Klein, R.J.T. (2008) A new global coastal database for impact and vulnerability analysis to sea-level rise. Journal of Coastal Research, 24(4), 917–924. 24", "output": {"entities": {"named_data": ["global coastal database"], "organization": ["Vafeidis, A.T.; Nicholls, R.J.; McFadden L.; Tol, R.S.J.; Hinkel, J.; Spencer, T.; Grashoff, P.S.; Boot, G., and Klein, R.J.T."]}, "relations": [{"used_by": {"head": "global coastal database", "tail": "Vafeidis, A.T.; Nicholls, R.J.; McFadden L.; Tol, R.S.J.; Hinkel, J.; Spencer, T.; Grashoff, P.S.; Boot, G., and Klein, R.J.T."}}]}, "_meta": {"entry_id": 1133, "dataset_name": "global coastal database"}}
+{"input": "T. V., Savadogo, A., and Tanaka, T. (2021). Refugees in Chad: The Road Forward. The World Bank. Norman, T., Borjesson, M., and Anderstig, C. (2017). Labor Market Accessibility and Unemployment. Journal of Transport Economics and Policy. Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). Informing Durable Solutions by Micro-Data: A Skills Survey for Refugees in Ethiopia. Peters, S. and Golden, S. (2019). Assessing Mental Health in Gambella, Ethiopia: A Representative Survey of South Sudanese Refugees in Nguenyyiel Camp. St. Paul, MN: The Center for Victims of Trauma. Pimhidzai, O., Chigumira, E., Tesfaye,W., and Yonis, M. (2022). Ethiopia - Rural Income Diagnostics Study: Leveraging the Transformation in the Agri-Food System and Global Trade to Expand Rural Incomes. Washington, D.C.: World Bank Group. https://documentsinternal.worldbank.org/search/33891468 Piper, B., Dryden-Peterson, S., Chopra, V., Reddick, C., and Oyanga, A. (2020). Are Refugee Children Learning? Early Grade Literacy in a Refugee Camp in Kenya. The Journal on Education in Emergencies, Inter-agency Network for Education in References 80 References Emergencies, 5(2). https://doi.org/10.33682/f1wr-yk6y. Ravallion, M. (1998). Poverty Lines in Theory and Practice: Living Standards Measurement Study. LSMS Working Paper, Issue 133. https://documents1.worldbank.org/curated/en/916871468766156239/pdf ReDSS. (2018). Local Integration Focus: Refugees in Ethiopia - Gaps and Opportunities for Refugees Who", "output": {"entities": {"named_data": ["Ethiopia - Rural Income Diagnostics Study"], "organization": ["World Bank Group", "World Bank"]}, "relations": [{"has_organization": {"head": "Ethiopia - Rural Income Diagnostics Study", "tail": "World Bank Group"}}, {"used_by": {"head": "Ethiopia - Rural Income Diagnostics Study", "tail": "World Bank"}}]}, "_meta": {"entry_id": 228, "dataset_name": "Ethiopia - Rural Income Diagnostics Study"}}
+{"input": "The Population Growth Assessment Report 2022 provides crucial insights into demographic trends across various regions. This report, published by the Global Demographics Institute, highlights significant shifts in fertility rates and migration patterns affecting population dynamics.", "output": {"entities": {"named_data": ["Population Growth Assessment Report 2022"], "organization": ["Global Demographics Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Population Growth Assessment Report 2022", "tail": "Global Demographics Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "demographics, fertility, and population growth"}}
+{"input": "twins) variation on 6th grade ENLACE, identifying, therefore, the relationship between\n\nas captured by ENLACE, on future outcomes and the (within-family) individual-level\n\ncharacteristics. In the ENLACE panel, this last specification uses the sample of students\n\nwho answered the ENLACE context questionnaire to control for differences in household\n\nENILEMS-ENLACE panel regressions, _Xi_ _[′]_ [includes] [upper] [secondary] [school] [grade] [point]", "output": {"entities": {"named_data": ["ENLACE panel"], "organization": ["ENLACE"]}, "relations": [{"has_organization": {"head": "ENLACE panel", "tail": "ENLACE"}}]}, "_meta": {"entry_id": 114, "dataset_name": "ENLACE panel"}}
+{"input": "In recent years, the increasing prevalence of forced migration has become a pressing global issue. The Global Refugee Status Report provides a comprehensive overview of the challenges faced by displaced populations, offering valuable insights into their living conditions and the support systems in place. As countries grapple with rising numbers of refugees, understanding the findings in this report is essential for policymakers and humanitarian organizations alike.", "output": {"entities": {"named_data": ["Global Refugee Status Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "forced displacement, refugees, and migration"}}
+{"input": "The Digital Connectivity Index (DCI) 2022 reveals significant insights into global internet access disparities. Conducted by the International Telecommunication Union (ITU), the DCI measures various aspects of connectivity across 180 countries during the year 2022. Notably, the dataset highlights that Sub-Saharan Africa continues to lag behind in technology adoption, prompting further research. In addition, the Global Technology Adoption Survey 2023, published by TechPolicy Analytics, provides further data on how technology use varies regionally and within sectors, with a focus on developed versus developing nations. This survey is crucial for policymakers and researchers aiming to bridge the digital divide and foster equitable access to technology.", "output": {"entities": {"named_data": ["Digital Connectivity Index", "Global Technology Adoption Survey"], "organization": ["International Telecommunication Union", "TechPolicy Analytics"], "acronym": ["DCI"], "year": ["2022", "2023"], "geography": ["Sub-Saharan Africa", "180 countries", "developed nations", "developing nations"]}, "relations": [{"has_acronym": {"head": "Digital Connectivity Index", "tail": "DCI"}}, {"has_timeframe": {"head": "Digital Connectivity Index", "tail": "2022"}}, {"has_geography": {"head": "Digital Connectivity Index", "tail": "180 countries"}}, {"has_timeframe": {"head": "Global Technology Adoption Survey", "tail": "2023"}}, {"has_geography": {"head": "Global Technology Adoption Survey", "tail": "developed versus developing nations"}}, {"has_organization": {"head": "Digital Connectivity Index", "tail": "International Telecommunication Union"}}, {"has_organization": {"head": "Global Technology Adoption Survey", "tail": "TechPolicy Analytics"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "digital development and technology adoption"}}
+{"input": "The annual Economic Competitiveness Index (ECI) dataset published by the Global Trade Institute provides crucial insights into the trade performance of various nations from 2018 to 2022. This dataset has been extensively used by the Economic Policy Research Group to inform their latest recommendations on trade policy reforms in developing countries, particularly focusing on Sub-Saharan Africa. The ECI dataset serves as a benchmark for assessing growth trajectories, and is instrumental for policymakers aiming to enhance their countries' competitive standing in the global market.", "output": {"entities": {"named_data": ["Economic Competitiveness Index", "ECI dataset"], "organization": ["Global Trade Institute", "Economic Policy Research Group"], "acronym": ["Economic Competitiveness Index"], "year": ["2018 to 2022"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "ECI dataset", "tail": "Global Trade Institute"}}, {"used_by": {"head": "ECI dataset", "tail": "Economic Policy Research Group"}}, {"has_acronym": {"head": "Economic Competitiveness Index", "tail": "ECI"}}, {"has_timeframe": {"head": "ECI dataset", "tail": "2018 to 2022"}}, {"has_geography": {"head": "ECI dataset", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "In the realm of urban infrastructure, the Metropolitan Planning Authority (MPA) has released the 2022 Urban Mobility Assessment, which evaluates traffic patterns in major cities across the country. This dataset has proven invaluable to various stakeholders, including the National Institute of Transportation (NIT), which utilized the MPA's findings to inform new policy recommendations for enhancing public transport efficiency in urban areas. Additionally, the Urban Mobility Assessment (UMA) provides a comprehensive overview of transportation challenges faced by cities from 2020 to 2022, showcasing the geographical diversity of urban centers included in the report.", "output": {"entities": {"named_data": ["Urban Mobility Assessment", "2022 Urban Mobility Assessment"], "organization": ["Metropolitan Planning Authority", "National Institute of Transportation"], "acronym": ["Urban Mobility Assessment", "MPA", "NIT"], "year": ["2022", "2020 to 2022"], "geography": ["major cities", "urban areas"]}, "relations": [{"has_organization": {"head": "2022 Urban Mobility Assessment", "tail": "Metropolitan Planning Authority"}}, {"used_by": {"head": "2022 Urban Mobility Assessment", "tail": "National Institute of Transportation"}}, {"has_acronym": {"head": "2022 Urban Mobility Assessment", "tail": "UMA"}}, {"has_timeframe": {"head": "Urban Mobility Assessment", "tail": "2020 to 2022"}}, {"has_geography": {"head": "Urban Mobility Assessment", "tail": "major cities"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "urban infrastructure and transportation planning"}}
+{"input": "The Maternal Health Outcomes Survey (MHOS) conducted in 2022 provides valuable insights into the challenges faced by women during childbirth in various regions. This dataset, published by the Global Health Institute, is crucial for understanding maternal health trends and developing effective interventions.", "output": {"entities": {"named_data": ["Maternal Health Outcomes Survey"], "organization": ["Global Health Institute"], "acronym": ["MHOS"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Maternal Health Outcomes Survey", "tail": "Global Health Institute"}}, {"has_acronym": {"head": "Maternal Health Outcomes Survey", "tail": "MHOS"}}, {"has_timeframe": {"head": "Maternal Health Outcomes Survey", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "health systems and maternal outcomes"}}
+{"input": "nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016).", "output": {"entities": {"named_data": ["Multiple Indicator Cluster Surveys"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "Multiple Indicator Cluster Surveys", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 985, "dataset_name": "Multiple Indicator Cluster Surveys"}}
+{"input": "The Public Financial Management Assessment Report 2022 (PFMAR) offers comprehensive insights into domestic revenue mobilization strategies implemented across various regions. This report, covering the fiscal policies from 2018 to 2022, highlights the challenges and successes experienced by governments in improving revenue streams. The analysis includes data from multiple countries, including Ghana and Tanzania, showcasing their respective progress. As a key resource, the PFMAR serves as an essential reference for policymakers and financial analysts alike, helping to inform future strategies for sustainable revenue generation.", "output": {"entities": {"named_data": ["Public Financial Management Assessment Report 2022"], "organization": [], "acronym": ["PFMAR"], "year": ["2022", "2018 to 2022"], "geography": ["Ghana", "Tanzania"]}, "relations": [{"has_acronym": {"head": "Public Financial Management Assessment Report 2022", "tail": "PFMAR"}}, {"has_timeframe": {"head": "Public Financial Management Assessment Report 2022", "tail": "2018 to 2022"}}, {"has_geography": {"head": "Public Financial Management Assessment Report 2022", "tail": "Ghana"}}, {"has_geography": {"head": "Public Financial Management Assessment Report 2022", "tail": "Tanzania"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "public financial management and domestic revenue"}}
+{"input": "In households with more than two children, one child from each age group was chosen through a random selection process to participate, ensuring a broad representation of experiences within the study ’ s scope. Sampling frame. We use the 2018 population census data as a sampling frame for the Colombian sample. It allowed the identification of residential blocks and households with children in the desired age range. With this source of information, it was possible to identify the number of households and residential blocks with children and adoles- 7Although Colombia only grants nationality to children of Colombian nationals, it follows a jus sanguini principle, the Colombian government has introduced reforms, such as the the program Primero la Ni ˜ nez to give Colombian nationality to children of Venezuelan parents born at times when diplomatic relations between Colombia and Venezuela were cut and hence, it was not possible to apply for a Venezuelan nation- ality for this minors in Colombia.", "output": {"entities": {"named_data": ["2018 population census data"], "organization": ["Colombian government"]}, "relations": [{"has_organization": {"head": "2018 population census data", "tail": "Colombian government"}}]}, "_meta": {"entry_id": 704, "dataset_name": "2018 population census data"}}
+{"input": "**The World Bank**\nEmergency Food Security Project (P178936)\n\n**Figure 1: Jordan 2017-18 household per capita consumption**\n**distribution relative to the national poverty line**\n\n**Figure 2: Share of food expenditure in total expenditure p.c. (%)**\n\nSource: 2017-18 HEIS and World Bank calculations. Source: World Bank\n\n**11.** **Existing food insecurity levels are particularly high among Jordan’s refugee population.** According to the\nmost recent mobile Vulnerability Assessment and Mapping (mVAM) [16] completed by the World Food Program (WFP) in Jordan, 7 percent of Jordanian households (representing 535,559 individuals) were found to be food insecure as of February 2021 and another 51 percent of households (representing 3,843,701 individuals) were vulnerable to food insecurity, meaning that they were very likely to experience an acute decline in food access or consumption levels below minimum survival needs. WFP’s mVAM also found that food insecurity levels among Jordan’s refugee community (which are not covered by Jordan’s social security net system) are significantly higher than those registered at the level of Jordanian households. Of the more than 750,000 refugees [17] registered in Jordan (89 percent of whom came from Syria), an estimated 17 percent live in the Za’atari and Azraq refugee camps, while the remaining 83 percent are mostly in Jordan’s urban areas. Throughout the COVID-19 pandemic, food security has been a key concern for refugees in both camps and in host communities mainly due to the loss of income from temporary and informal labor activities. More than 80 percent of labor activities performed by non-Jordanians are estimated to take place in the informal economy versus 40 percent for Jordanian citizens (MOSD, 2019). February 2021 mVAM data showed that 23.3 percent of refugee households in host communities are food insecure (over 154,777 individuals), while another 63.7 percent of refugee households (equivalent to approximately 423,344 individuals) are vulnerable to food insecurity.\n\n**12.** **Ensuring food security and social stability are at the core of the urgent need to ensure availability of**\n**and access to staple food** . Bread is an essential part of the diet in Jordan and represents the main caloric source\nfor the poorest Jordanians and the many refugees in the country. Similarly, barley import dependent livestock herding is the mainstay of the rural economy and the livelihoods of about 200,000 people in traditional nomadic and semi-nomadic Bedouin tribal communities. While trade in basic food commodities (such as sugar, rice, cooking oil, milk powder) is managed mainly by the private sector in Jordan, the GOJ maintains full control of grain reserves by directly managing the purchase, import, storage and domestic sales of wheat and barley. In 2021, the GOJ discontinued the bread subsidy and, as a response to the COVID-19 outbreak, it fixed the domestic price of bread to ensure its affordability (at 0.32 JOD per kilogram) for the entire population of Jordan. This intervention model (in combination with the effective appreciation of the Jordanian dinar) has helped control inflationary pressures.\nHowever, maintaining this status quo in a context of all time high international grain prices is costly, also coinciding with Bond maturity obligations of the GOJ, further reducing the availability of financial resources at a critical time.\n\n**13.** **Public procurement of strategic agricultural commodities is at the core of Jordan’s food security strategy**\n**as rising food prices pose a significant threat to social stability** . Historically, Jordan has played a key role as an\n\n_16_ [WFP | Jordan: Mobile Vulnerability Analysis and Mapping Dashboard | As of June 2021 (arcgis.com)](https://unwfp.maps.arcgis.com/apps/MapSeries/index.html?appid=7210a3ee33b14c5b9a989590345cb49a)\n_17_ _As of July 2021_\n\nPage 10 of 54", "output": {"entities": {"named_data": ["mobile Vulnerability Assessment and Mapping"], "organization": ["World Food Program (WFP)", "World Bank"]}, "relations": [{"has_organization": {"head": "mobile Vulnerability Assessment and Mapping", "tail": "World Food Program (WFP)"}}, {"used_by": {"head": "mobile Vulnerability Assessment and Mapping", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1379, "dataset_name": "mobile Vulnerability Assessment and Mapping"}}
+{"input": "**FRAGILITY AND POPULATION MOVEMENT IN AFGHANISTAN**\n\nThird, _**particular attention should be devoted to minimizing any possible negative impact of**_ _**displacement on human capital investments for future generations.**_ Evidence suggests that the lack human capital is the main determinants of the risk of poverty and that households are likely to respond to negative shocks by pulling children out of school. While a comprehensive safety net system could help mitigating such negative consequences, bureaucratic barriers such as residency status and transferability of school records could negatively impact displaced or, more generally, mobile populations. Moreover, given the prevalence of mobility and displacement in Afghanistan, greater focus should be devoted to investing in functional literacy and skill-development programs that display greater portability and provide displaced individuals with greater access to economic opportunities, wherever they end up being.\n\nLastly, evidence suggests that migrants will likely continue to converge towards Afghanistan’s urban centers as they seek better security, jobs, and services. _Urbanization trends require immediate_ _intervention by local authorities to increase shelter capacity and access to services. National and_ _provincial authorities should further recognize that, in the medium and long term,_ _**local integration in**_ _**urban and semi-urban areas is inevitable and it requires adequate planning**_ _to maximize the returns_ _from urban agglomeration, for example by investing in connectivity and accessibility, while ensuring_ _access to basic services and a minimum standard of living._\n\n## **ENDNOTES**\n\n1. UN Population Movement Bulletin, Issue 5, 7 Sept, 2016.\n\n2. Based on UNHCR assisted returns data, 78 percent of returns occurred between 2002 and 2006. Districts with “high” intensity of returns in 2007 had an average share of returnees over the population of 70 percent.\n\n3. Returnee households are considered to have “reintegrated” successfully if they were able to return to the place where they used to live before displacement, and if they were able to achieve—on average—socio-economic outcomes and legal protection in line with those of the local/host population. Household ability to reintegrate successfully might depend on several factors related to the social, economic and institutional conditions prevailing in the host community, as well as on the physical, human and social capital accumulated by returnee households while in asylum, and on their returns “on arrival”.\n\n4. According to NRVA 2007–08 data, approximately 85 percent of Afghan households reported to have been negatively affected by a “large influx of returnees” during the 12 months preceding the survey.\n\n5. Demographic and Health Survey (2014).\n\n6. The youth bulge is defined as the share of youth aged 15–24 to the adult population aged 15+.\n\n7. Growth is expected to remain slow over coming years, reflecting weak demand, increasing output gap and the lack of fiscal space for increasing social transfers in order to boost short-term economic growth (World Bank, 2016; _Navigating Risk and Uncertainty in Afghanistan_ ).\n\n8. Urdal, Henrik. 2004. _The devil in the demographics: The effect of youth bulges on domestic armed conflict,_ 1950–2000. Social Development Papers: Conflict and Reconstruction Paper 14.\n\n9. Low-technology, rain-fed agriculture remains the country’s primary sector of employment, especially for its poorest and more vulnerable people.\n\n10. ALCS 2013–14.\n\n11. Probability of having a household member abroad was estimated using a Linear Probability model and ALCS 2013–14 data. Controls include a dummy indicating whether the household feels insecure in the district of residence; the number of security incidents per thousand inhabitants in the district of residence; composition and employment outcomes at the household level; dummy variables identifying returnee households, IDP households and households migrating for economic reasons; a dummy indicating urban residence and quintiles of a wealth index to proxy for household welfare.\n\n11", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Demographic and Health Survey", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1375, "dataset_name": "Demographic and Health Survey"}}
+{"input": "REFodet = αod + γe + τt + β1Conflictot − 1 + β2Conflictet − 1 + β3Distanceed + ϵodet, (5) where REFodet is the stock of refugees of ethnic group e from country o in country d at year t. As we have data on yearly refugee stocks and would like to estimate the changes in these stocks over time using a gravity model, we include origin – destination fixed effects αod so that identification is based only on changes in stock over time (Zylkin, 2019). 20 We also include time τt and ethnic group fixed effects γe. Here we obtain data on the ethnicity of refugees from Murdock ’ s Atlas, which provides a map of ethnographic regions for Africa and the historical homelands of refugees (Murdock, 1967). To match ethnic groups across datasets, we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer.", "output": {"entities": {"named_data": ["LEDA21"], "organization": ["we"]}, "relations": [{"used_by": {"head": "LEDA21", "tail": "we"}}]}, "_meta": {"entry_id": 747, "dataset_name": "LEDA21"}}
+{"input": "In 2003, almost 60 % of young girls and 40 % of young boys had no formal schooling (ILO / UNICEF 2005). Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007). Happily, access to education is rising rapidly, especially for girls: according to the Liberian labor force survey from 2010, the ratio of girls to boys enrolled in primary school has risen from 72 in 2000 to 90 in 2009. Enrollment levels and sex ratios are lower among older children and youth, as they become increasingly engaged in 1 Both are unweighted averages; Barro-Lee comprises 32 countries with data from 2010; Edstats comprises 43 countries with data from 2007-2011. 2 Defined as without work, available for work, and actively looking for work (LISGIS 2010). 2", "output": {"entities": {"named_data": ["Liberian labor force survey"], "organization": ["LISGIS"]}, "relations": [{"has_organization": {"head": "Liberian labor force survey", "tail": "LISGIS"}}]}, "_meta": {"entry_id": 445, "dataset_name": "Liberian labor force survey"}}
+{"input": "The Food Security Monitoring Survey (FSMS) conducted by the National Agricultural Bureau in 2022 provides critical insights into food access across various regions. The World Food Programme, leveraging data from the FSMS, has been able to assess the impact of recent climate fluctuations on agricultural productivity in the Central Highlands of the country. Additionally, the Agricultural Livelihoods Assessment Report 2023 published by the Ministry of Agriculture includes detailed findings on the nutritional status of households affected by drought conditions, enabling stakeholders to design targeted interventions. These datasets collectively support efforts to enhance food security, guiding policy decisions and resource allocation.", "output": {"entities": {"named_data": ["Food Security Monitoring Survey", "Agricultural Livelihoods Assessment Report 2023"], "organization": ["National Agricultural Bureau", "World Food Programme", "Ministry of Agriculture"], "acronym": ["Food Security Monitoring Survey", "FSMS"], "year": ["2022", "2023"], "geography": ["Central Highlands"]}, "relations": [{"has_organization": {"head": "Food Security Monitoring Survey", "tail": "National Agricultural Bureau"}}, {"used_by": {"head": "Food Security Monitoring Survey", "tail": "World Food Programme"}}, {"has_timeframe": {"head": "Food Security Monitoring Survey", "tail": "2022"}}, {"has_organization": {"head": "Agricultural Livelihoods Assessment Report 2023", "tail": "Ministry of Agriculture"}}, {"has_timeframe": {"head": "Agricultural Livelihoods Assessment Report 2023", "tail": "2023"}}, {"has_geography": {"head": "Agricultural Livelihoods Assessment Report 2023", "tail": "Central Highlands"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "agriculture, food security, and nutrition"}}
+{"input": "\"Life in Transition Survey, Transition Report 2020-2021: The State Strike Back\", [https://www.ebrd.com/publications/transition-report-](https://www.ebrd.com/publications/transition-report-202021) [202021.](https://www.ebrd.com/publications/transition-report-202021)\n\n\"Strengthening the business environment for productivity convergence,\" in OECD Economic Surveys: Romania 2022, OECD Publishing, Paris, [https://doi.org/10.1787/63318cf5-en.](https://doi.org/10.1787/63318cf5-en)\n\n_Encuesta_ _Dirigida_ _a_ _la_ _Población_ _Venezolana_ _que_ _Reside_ _en_ _El_ _País_ _(ENPOVE)_ is a special ized survey of Venezuelans living in Peru conducted by the National Institute of Statistics (INEI) in December 2018. The sample covers five main urban areas in the country where Venezuelan immigrants were most likely to be present. The survey collects data on the immi grant's origin, migration date, and details on their current employment. Importantly, a full module asks about the immigrant's experiences with locals, which includes questions about discrimination and hostile attitudes towards them. The respondent's current location is iden tified down to the _centro_ _poblado_ level, which roughly corresponds to an urban neighborhood or a rural town.\n\n_Encuesta_ _Nacional_ _de_ _Hogares_ _(ENAHO)_ is the Peruvian version of the Living Standards Measurement Survey, e.g. a nationally representative household survey collected monthly on a continuous basis. For our analysis, we use data from January 2007 to December 2020. The survey covers a wide variety of topics, including basic demographics, educational back ground, labor market conditions, crime victimization, and a module on respondent's percep tions about the main problems in the country and trust on different local and national level institutions. Observations are also spatially identified at the municipality level, but here we focus on variation in the Venezuelan share of the population at the province level, of which there are 196, as these are best representative of local labor markets.\n\n_Latin_ _American_ _Public_ _Opinion_ _Project_ _(LAPOP)_ is a opinion survey conducted bi-annually in all countries in Latin America and designed to be representative of urban populations. This was fielded in Peru in 2010, 2012, 2014, 2017 and 2019 and consists of about 2,000 observations from mostly urban areas. The survey questions are centered around politics,", "output": {"entities": {"named_data": ["ENAHO"], "organization": ["National Institute of Statistics (INEI)"]}, "relations": [{"has_organization": {"head": "ENAHO", "tail": "National Institute of Statistics (INEI)"}}]}, "_meta": {"entry_id": 313, "dataset_name": "ENAHO"}}
+{"input": "Night-time lights data are a beneficial by-product of a meteorological satellite program. The data are\ncollected by the United States Air Force Defense Meteorological Satellite Program (DMSP). DMSP\nsatellites have been circling the earth since the 1970s in a polar orbit that allows observations of every\n\n1. Number of illuminated pixels with _DN_ >=6 within the borders of a country.\n2. Average _DN_ within all illuminated pixels.\n_Sources:_ NGDC v4, World Development Indicators for land area, and author's calculations.\n\nLong-run patterns in _AoL_ and _R_ are consistent with some country circumstances. For example, as shown\nin Table 1, a country with a rapidly expanding _AoL_ is more likely to be a country with a high urban\npopulation growth rate. China, Indonesia, Malaysia, Vietnam, and Yemen are examples. A country with\nshrinking _AoL_ could be in the early, painful stages of transition from a planned economy to a market\neconomy. Azerbaijan, Tajikistan and Ukraine are examples. Countries with growing average radiance, _R_,\n\nSouknilanh et al (2015) find their night-light based _GDP_ estimates are improved when supplemented by ground cover data from a second satellite (MODIS).\n\n16 Normally there is a quasi-fixed ratio of intermediates to gross output which slowly falls as productivity improves. Countries that import most of their intermediates will be subject to external shocks (trading partner demand, terms of trade) that disrupt this relationship. 17 From a sample of 166 countries in 2010, from the World Development Indicators.\n\n[26] The result is the v.4 DMSP stable lights data set with between 20 and 100 observations per year per pixel depending upon circumstances (Baugh et al.", "output": {"entities": {"named_data": ["World Development Indicators"], "organization": ["Souknilanh et al (2015)"]}, "relations": [{"used_by": {"head": "World Development Indicators", "tail": "Souknilanh et al (2015)"}}]}, "_meta": {"entry_id": 592, "dataset_name": "World Development Indicators"}}
+{"input": "Recent analyses of learning achievement among primary school students have highlighted the notable discrepancies in performance based on various socio-economic factors. For instance, the findings presented in the National Education Assessment Report reveal significant gaps in literacy rates across different regions. The report underscores the importance of targeted interventions in education policy to address these disparities and improve overall educational outcomes for all students.", "output": {"entities": {"named_data": ["National Education Assessment Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The Fragility Assessment Survey (FAS) conducted in 2022 offers valuable insights on socio-political stability in several nations. This dataset, which encompasses various dimensions of governance, is integral for understanding the underlying factors contributing to conflict. In addition, the Global Violence Dataset (GVD) published by the Conflict Research Institute in 2021 provides a comprehensive overview of violence trends across the globe, including regions like the Middle East and Sub-Saharan Africa. Academics and policymakers alike rely on these datasets to inform their analyses and strategies for mitigating violence and enhancing stability.", "output": {"entities": {"named_data": ["Fragility Assessment Survey", "Global Violence Dataset"], "organization": ["Conflict Research Institute"], "acronym": ["FAS", "GVD"], "year": ["2022", "2021"], "geography": ["Middle East", "Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Fragility Assessment Survey", "tail": "FAS"}}, {"has_timeframe": {"head": "Fragility Assessment Survey", "tail": "2022"}}, {"has_geography": {"head": "Fragility Assessment Survey", "tail": "several nations"}}, {"has_acronym": {"head": "Global Violence Dataset", "tail": "GVD"}}, {"has_timeframe": {"head": "Global Violence Dataset", "tail": "2021"}}, {"has_geography": {"head": "Global Violence Dataset", "tail": "Middle East"}}, {"has_geography": {"head": "Global Violence Dataset", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Global Violence Dataset", "tail": "Conflict Research Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "conflict, fragility, and violence"}}
+{"input": "The Economic Competitiveness Assessment 2022, produced by the Global Trade Organization, offers insights into the industrial capabilities across various regions. Recent analyses by the Economic Policy Institute utilized the data from this assessment to understand trends in manufacturing growth in North America and Europe. Additionally, the World Bank's Trade and Industry Database (TID) provides comprehensive statistics related to trade flows and has been used by multiple organizations, including the OECD, to evaluate the impacts of tariffs on economic performance from 2019 to 2023. These datasets collectively contribute to shaping policy recommendations for enhancing trade efficiencies and competitiveness on a global scale.", "output": {"entities": {"named_data": ["Economic Competitiveness Assessment 2022", "Trade and Industry Database"], "organization": ["Global Trade Organization", "Economic Policy Institute", "World Bank", "OECD"], "acronym": ["TID"], "year": ["2022", "2019 to 2023"], "geography": ["North America", "Europe"]}, "relations": [{"has_organization": {"head": "Economic Competitiveness Assessment 2022", "tail": "Global Trade Organization"}}, {"used_by": {"head": "Economic Competitiveness Assessment 2022", "tail": "Economic Policy Institute"}}, {"has_organization": {"head": "Trade and Industry Database", "tail": "World Bank"}}, {"has_acronym": {"head": "Trade and Industry Database", "tail": "TID"}}, {"used_by": {"head": "Trade and Industry Database", "tail": "OECD"}}, {"has_timeframe": {"head": "Trade and Industry Database", "tail": "2019 to 2023"}}, {"has_geography": {"head": "Economic Competitiveness Assessment 2022", "tail": "North America"}}, {"has_geography": {"head": "Economic Competitiveness Assessment 2022", "tail": "Europe"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "# Appendix. Model Calibration\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 the Polish economy was felt as combined four different shocks: to population, labour supply, average propensity to save, and productivity, with shocks to population and labour supply being balanced by equivalent shocks in Eastern Europe [53] .\nMoreover, as part of assumed increase in spending by Ukrainians in Poland was financed by savings from Ukraine this was balanced by equivalent negative shock on investment in Eastern Europe.\n\nShock to population was calibrated to match data for residents of Poland from Statistics Poland and number of refugees based on PESEL UKR. Equivalent shock in Eastern Europe was calculated using data for population in this region from World Population Prospects UN.", "output": {"entities": {"named_data": ["PESEL UKR"], "organization": ["Statistics Poland", "Deloitte Economic Institute"]}, "relations": [{"has_organization": {"head": "PESEL UKR", "tail": "Statistics Poland"}}, {"used_by": {"head": "PESEL UKR", "tail": "Deloitte Economic Institute"}}]}, "_meta": {"entry_id": 1308, "dataset_name": "PESEL UKR"}}
+{"input": "Access to clean water and sanitation services remains critical for public health, as highlighted by the Global Water Quality Assessment (GWQA) 2018/19, which evaluates the status of freshwater resources in several countries. The GWQA dataset, produced by the International Water Management Institute (IWMI), provides essential insights into water contamination levels across various regions. Furthermore, the South Asia Hygiene Promotion Survey (SAHPS) conducted in 2020 aims to assess the effectiveness of hygiene practices in the region, specifically targeting India and Bangladesh. Lastly, the Africa WASH Monitoring Report 2022 serves as a comprehensive tool for policymakers, allowing them to track progress towards sanitation goals, while the acronym WASH stands for Water, Sanitation, and Hygiene.", "output": {"entities": {"named_data": ["Global Water Quality Assessment", "GWQA", "South Asia Hygiene Promotion Survey", "SAHPS", "Africa WASH Monitoring Report"], "organization": ["International Water Management Institute", "IWMI"], "acronym": ["GWQA", "SAHPS", "WASH"], "year": ["2018/19", "2020", "2022"], "geography": ["India", "Bangladesh", "Africa"]}, "relations": [{"has_acronym": {"head": "Global Water Quality Assessment", "tail": "GWQA"}}, {"has_timeframe": {"head": "Global Water Quality Assessment", "tail": "2018/19"}}, {"has_geography": {"head": "Global Water Quality Assessment", "tail": "several countries"}}, {"has_organization": {"head": "Global Water Quality Assessment", "tail": "International Water Management Institute"}}, {"has_acronym": {"head": "South Asia Hygiene Promotion Survey", "tail": "SAHPS"}}, {"has_timeframe": {"head": "South Asia Hygiene Promotion Survey", "tail": "2020"}}, {"has_geography": {"head": "South Asia Hygiene Promotion Survey", "tail": "India"}}, {"has_geography": {"head": "South Asia Hygiene Promotion Survey", "tail": "Bangladesh"}}, {"has_acronym": {"head": "Africa WASH Monitoring Report", "tail": "WASH"}}, {"has_timeframe": {"head": "Africa WASH Monitoring Report", "tail": "2022"}}, {"has_geography": {"head": "Africa WASH Monitoring Report", "tail": "Africa"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "water, sanitation, and hygiene"}}
+{"input": "the standard ethnic diversity indices to include the annual variation in refugee ethnicities. 8 We then construct a measure of proximity between the clusters in the host country and refugees in surrounding camps by defining an 80-km buffer around each cluster. 9 To control for unobserved heterogeneity and changes within a given cluster, we introduce cluster and year fixed effects, αj and δt. To minimize the risk of confounding the refugee-induced changes in diversity with the annual changes in refugee numbers, we also control for the presence of refugees based on the same buffer as the one used to construct the refugee-induced change in diversity. More specifically, the variable Refugeesjt − 1 counts the number of refugees present in cluster j at year t − 1 within the predefined buffer. The variable is also transformed into an inverse hyperbolic sine to ease interpretation. Finally, Qjt controls for yearly shocks at the cluster level, such as weather shocks. In particular, we control for rain and temperature anomalies. Standard errors are clustered at the Afrobarometer cluster level. 4. 2 Data and descriptive statistics Our analysis combines various sources of data: Afrobarometer, UNHCR refugee camp data, Armed Conflict Location and Event Data (ACLED), Uppsala Conflict Data (UCDP), and the Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset.", "output": {"entities": {"named_data": ["Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset"], "organization": ["Afrobarometer"]}, "relations": [{"used_by": {"head": "Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset", "tail": "Afrobarometer"}}]}, "_meta": {"entry_id": 39, "dataset_name": "Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset"}}
+{"input": "**The World Bank**\nSouth Sudan Health Sector Transformation Project (HSTP) (P181385)\n\n|Description|Percentage of HC health facilities receiving at least one quarterly supervision visit within the quarter|\n|---|---|\n|Frequency|Quarterly|\n|Data source|MoH; TPM|\n|Methodology for Data<br>Collection|MoH to provide data; TPM to verify|\n|Responsibility for Data<br>Collection|MoH / TPM|\n|**Percentage of health facilities receiving quarterly supervision visits from the CHD (Percentage)**|**Percentage of health facilities receiving quarterly supervision visits from the CHD (Percentage)**|\n|Description|Percentage of health facilities receiving at least one quarterly supervision visit within the quarter from the CHD|\n|Frequency|Quarterly|\n|Data source|MoH; TPM|\n|Methodology for Data<br>Collection|MoH to provide data; TPM to verify|\n|Responsibility for Data<br>Collection|MoH / TPM|\n|**Percentage of health facilities receiving quarterly supervision visits from State MoH (Percentage)**|**Percentage of health facilities receiving quarterly supervision visits from State MoH (Percentage)**|\n|Description|Percentage of health facilities receiving at least one quarterly supervision visit within the quarter from the State<br>MoH|\n|Frequency|Quarterly|\n|Data source|MoH; TPM|\n|Methodology for Data<br>Collection|MoH to provide data; TPM to verify|\n|Responsibility for Data<br>Collection|MoH / TPM|\n|**Percentage of complaints to Grievance Redress Mechanisms satisfactorily addressed in a timely manner**|**Percentage of complaints to Grievance Redress Mechanisms satisfactorily addressed in a timely manner**|\n|Description|Percentage of complaints submitted to the GRM addressed according to the protocol and within agreed time<br>period.|\n|Frequency|Quarterly|\n|Data source|UNICEF|\n|Methodology for Data<br>Collection|UNICEF to provide data / TPM to verify|\n|Responsibility for Data<br>Collection|UNICEF; PMU|\n|**Percentage of completeness of reporting by facilities**|**Percentage of completeness of reporting by facilities**|\n|Description|Percentage of facilities that submit complete reports within the required deadline.|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH/ PMU|\n|**Percentage of states that conducted quarterly coordination meetings with a review of data and documented with minutes including**<br>**action items and follow-up**|**Percentage of states that conducted quarterly coordination meetings with a review of data and documented with minutes including**<br>**action items and follow-up**|\n|Description|Percentage of State’s quarterly health service delivery coordination meetings for the health sector held with a<br>review of data included in the meeting and documented with minutes which include action items and follow-up<br>on action items. Meetings are to be held quarterly in each state. Four meetings are expected each year per<br>state. CHDs and implementing partners will be participated in the review|\n|Frequency|Quarterly|\n|Data source|MoH/ WHO|\n|Methodology for Data<br>Collection|WHO to provide data / TPM to verify|", "output": {"entities": {"named_data": ["UNICEF"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "UNICEF", "tail": "World Bank"}}]}, "_meta": {"entry_id": 826, "dataset_name": "UNICEF"}}
+{"input": "as farm tools and water pumps, sewing and building equipment, and commercial cars. Outliers are treated, and values are adjusted for inflation. 0 20 40 60 80 100 Camp Hosts Camp Refugees Current Camp Refugees COB Other (rental income, PSNP, pension) Remittances (local/international) Donations (NGO/gov) Crops/livestock Salary (employment/casual labor) 0 20 40 60 80 100 Camps-Male-Headed Camps-Female-Headed Salary (employment/casual labor) Crops/livestock Donations(NGO/gov) Remittances (local/international) Other (rental income, PSNP, pension) Percent Percent Figure 3.20: Household primary income source Source: World Bank Staff based on SESRE 2023. Note: “COB” refers to livelihood strategies in their country of birth. a. Pre-post migration b. By gender of head Jobs and Livelihoods 34 arrive in Uganda with few assets, in a state of high poverty, and with similarly low employment rates. However, unlike Ethiopia, employment rates for refugees in Uganda improve over time, approximately doubling after five years or more (World Bank, 2023b). Uganda is also notable for providing work rights to refugees in practice (Ginn et al., 2022) (Box 3.3). 3.2 Labor market outcomes of OCP refugees and their hosts Refugee households in Addis Ababa rely heavily on remittances as their primary source of income. This reflects the fact that the Eritrean OCP refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank", "World Bank Staff"]}, "relations": [{"has_organization": {"head": "SESRE 2023", "tail": "World Bank"}}, {"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 432, "dataset_name": "SESRE 2023"}}
+{"input": "**Calculate sums** - For each flood risk array, calculate the total number of people exposed and add the results to the World Bank global administrative map shapefile ##### **5. Results** For each of the countries analyzed, the results are available as raster files with a 90m spatial resolution and as shapefiles with data aggregated to the admin 1 (sub-national), admin 0 (national), regional, and global levels. In this section, we present visualizations of key findings as maps, using a variety of spatial scales, as well as graphs to highlight pertinent insights. **5.1.** **Global and regional flood exposure** Our estimates show that globally 2.2 billion people are exposed to some level of flood risk; 1.47 billion people, or 19 percent of the world population, are exposed to a significant level of flood risk (i.e. facing inundation depths of over 0.15 meter in the event of a 1-in-100 year flood, or _moderate_ risk or higher in Figure 2). In other words, considering a global population of 7.7 billion (World Bank, 2019), approximately one in five people in the world are exposed to substantial flood risk. 10", "output": {"entities": {"named_data": ["World Bank global administrative map shapefile"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "World Bank global administrative map shapefile", "tail": "World Bank"}}, {"used_by": {"head": "World Bank global administrative map shapefile", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1195, "dataset_name": "World Bank global administrative map shapefile"}}
+{"input": "The recent Environmental Assessment Report published by the International Institute for Sustainable Development highlights key findings regarding deforestation rates in various regions. This comprehensive report uses data collected from different environmental surveys and is intended to guide policy decisions for sustainable resource management. The insights provided are valuable for government agencies and NGOs working towards environmental conservation.", "output": {"entities": {"named_data": ["Environmental Assessment Report"], "organization": ["International Institute for Sustainable Development"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Environmental Assessment Report", "tail": "International Institute for Sustainable Development"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
+{"input": "Nevertheless, censuses are the only nation-wide source of population data for potentially providing estimates of displaced populations, especially those in non-camp settings, and often provide a basis for sampling frames for survey instruments (UNHCR 2016). Capturing displacement situations in official statistics also increases their visibility. Sample surveys Sample surveys can potentially provide a rich source of data on forcibly displaced populations. Compared with censuses and registers, sample surveys allow more detailed questions to be asked about the characteristics and situations of households. If survey instruments identify displaced populations based on individuals ’ and households ’ self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status (UNSD 2014). There are opportunities to mainstream forced displacement into international survey instruments, but this has only been done in a handful of cases. Several standardized international sample surveys have been designed for special purposes including the Living Standards Measurement Study (LSMS), 75 Labor Force 71 The UN census recommendations for the 2010 World Population and Housing Census Programme stipulate that refugees and IDPs living in camps should be counted and their numbers disaggregated in population statistics, however there is no requirement to separately distinguish displaced people living outside of camps (UNHCR 2016).", "output": {"entities": {"named_data": ["World Population and Housing Census Programme"], "organization": ["UNHCR", "UNSD"]}, "relations": [{"has_organization": {"head": "World Population and Housing Census Programme", "tail": "UNHCR"}}, {"used_by": {"head": "World Population and Housing Census Programme", "tail": "UNSD"}}]}, "_meta": {"entry_id": 1025, "dataset_name": "World Population and Housing Census Programme"}}
+{"input": "The firm-level financial data for 1997 are primarily from the Worldscope database. The World\n\nin Australia and Canada, respectively. Using industry data from Mexico, Blomstrom and Persson\n\nCommission on an annual basis. We use [group-affiliation data from the 1994-1997 lists of business]\n\nby the level of growth of a sector or a country. Indeed, using firm level data, Haddad and Harrison", "output": {"entities": {"named_data": ["Worldscope database"], "organization": ["Haddad and Harrison"]}, "relations": [{"used_by": {"head": "Worldscope database", "tail": "Haddad and Harrison"}}]}, "_meta": {"entry_id": 138, "dataset_name": "Worldscope database"}}
+{"input": "(24 percent) with higher livestock ownership relative to other domains. Somalis are also more likely to work for private households, including household services, construction, and agricultural work. 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Camp Refugees Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.2: Work status Source: World Bank Staff based on SESRE 2023. Table 3.1: Labor force statistics Camp- Hosts Camp- Refugees Labor force participation rate (strict) 52% 31% Unemployment rate (strict) 7% 21% Labor force participation rate (relaxed) 57% 43% Unemployment (relaxed) 15% 43% Employment-to-population ratio 48% 25% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed or unemployed. Unemployment is the share of people participating in the labor force who are not employed. The “relaxed” definition of labor force participation includes anyone who is available to work. The “strict” definition of labor force participation includes only those who are available to work and also actively searching for work. Employment-to- population ratio is the share of working-age people who are employed. 41 In Ethiopia, in-camp", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank Staff"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 764, "dataset_name": "SESRE 2023"}}
+{"input": "The Global Refugee Movement Report 2022, published by the International Organization for Migration (IOM), provides comprehensive insights into forced displacement trends. This dataset has been utilized by UNHCR to inform its annual Global Trends report, highlighting the urgency of policy interventions. Additionally, the Multinational Displacement Evaluation (MDE) conducted in 2021, which was released by the World Bank, includes data on the socio-economic impacts of refugee populations across several regions. This evaluation has been referenced by various NGOs, including Refugee Rights International, to advocate for improved conditions for refugees. Furthermore, the Latin America Migration Study (LAMS) 2020, produced by the Inter-American Development Bank (IDB), has been critical in shaping discussions around migration policies in the region.", "output": {"entities": {"named_data": ["Global Refugee Movement Report 2022", "Global Trends report", "Multinational Displacement Evaluation", "Latin America Migration Study (LAMS) 2020"], "organization": ["International Organization for Migration", "UNHCR", "World Bank", "Refugee Rights International", "Inter-American Development Bank"], "acronym": ["IOM", "IDB"], "year": ["2022", "2021", "2020"], "geography": ["Latin America"]}, "relations": [{"has_organization": {"head": "Global Refugee Movement Report 2022", "tail": "International Organization for Migration"}}, {"used_by": {"head": "Global Refugee Movement Report 2022", "tail": "UNHCR"}}, {"has_organization": {"head": "Multinational Displacement Evaluation", "tail": "World Bank"}}, {"used_by": {"head": "Multinational Displacement Evaluation", "tail": "Refugee Rights International"}}, {"has_organization": {"head": "Latin America Migration Study (LAMS) 2020", "tail": "Inter-American Development Bank"}}, {"has_timeframe": {"head": "Global Refugee Movement Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Multinational Displacement Evaluation", "tail": "2021"}}, {"has_timeframe": {"head": "Latin America Migration Study (LAMS) 2020", "tail": "2020"}}, {"has_geography": {"head": "Latin America Migration Study (LAMS) 2020", "tail": "Latin America"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "forced displacement, refugees, and migration"}}
+{"input": "displaced due to armed conflict, situations of generalized violence and violations of human rights. 28 Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement. 29 At the country level the IOM ’ s Displacement Tracking Matrix (DTM) 30 provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years). Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy. 31 Moreover, while some countries register IDP children born in displacement (e. g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting.", "output": {"entities": {"named_data": ["Displacement Tracking Matrix"], "organization": ["IOM", "IDMC"]}, "relations": [{"has_organization": {"head": "Displacement Tracking Matrix", "tail": "IOM"}}, {"used_by": {"head": "Displacement Tracking Matrix", "tail": "IDMC"}}]}, "_meta": {"entry_id": 283, "dataset_name": "Displacement Tracking Matrix"}}
+{"input": "Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner", "output": {"entities": {"named_data": ["Labor Force Surveys"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Labor Force Surveys", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1010, "dataset_name": "Labor Force Surveys"}}
+{"input": "The Energy Access Assessment Report 2021 (EAAR) provides critical insights into the progress made in extending electricity to rural communities in Sub-Saharan Africa. This report, published by the International Energy Agency (IEA), covers data from 2017 to 2021, highlighting significant advancements in energy access initiatives. Additionally, the Renewable Energy Transition Database (RETDB) offers comprehensive analytics on renewable energy adoption in various countries, focusing particularly on developments between 2015-2020. While the RETDB is utilized by several NGOs and governmental bodies, it is important to note that the IEA is the primary organization behind both datasets, ensuring consistent and reliable data for policymakers.", "output": {"entities": {"named_data": ["Energy Access Assessment Report 2021", "Renewable Energy Transition Database"], "organization": ["International Energy Agency", "IEA"], "acronym": ["EAAR", "RETDB"], "year": ["2021", "2017 to 2021", "2015-2020"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Energy Access Assessment Report 2021", "tail": "EAAR"}}, {"has_timeframe": {"head": "Energy Access Assessment Report 2021", "tail": "2021"}}, {"has_timeframe": {"head": "Energy Access Assessment Report 2021", "tail": "2017 to 2021"}}, {"has_geography": {"head": "Energy Access Assessment Report 2021", "tail": "Sub-Saharan Africa"}}, {"has_acronym": {"head": "Renewable Energy Transition Database", "tail": "RETDB"}}, {"has_timeframe": {"head": "Renewable Energy Transition Database", "tail": "2015-2020"}}, {"has_organization": {"head": "Renewable Energy Transition Database", "tail": "International Energy Agency"}}, {"used_by": {"head": "Renewable Energy Transition Database", "tail": "NGOs and governmental bodies"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "energy access and renewable transitions"}}
+{"input": "The Systematic Tracking of Exchanges in Procurement (STEP) system will be used for all procurement activities; and g) The World Bank will carry out regular reviews of project-related procurement activities. 17. **Procurement oversight and monitoring arrangements.** The World Bank exercises its procurement oversight through a risk-based approach comprising prior and post reviews as appropriate. The World Bank sets mandatory thresholds for prior review based on the procurement risk rating of the project. The requirement for a prior or post review is specified in the Procurement Plan. The World Bank will carry out post reviews of procurement activities undertaken by the recipient to determine whether they comply with the requirement of the Financing and Project Agreements. 18. **Terms of reference** for all contracts will be cleared by the World Bank, regardless of whether the assignment is for prior or post review. 19. **Training and workshops** : The project will finance training and workshops, if required, based on an annual training plan and budget, which will be submitted to the World Bank for its prior review and approval. The annual training plan will identify, among other things: (a) the training envisaged; (b) the justification for the training; (c) the personnel to be", "output": {"entities": {"named_data": ["Systematic Tracking of Exchanges in Procurement (STEP)"], "organization": ["The World Bank"]}, "relations": [{"used_by": {"head": "Systematic Tracking of Exchanges in Procurement (STEP)", "tail": "The World Bank"}}]}, "_meta": {"entry_id": 1116, "dataset_name": "Systematic Tracking of Exchanges in Procurement (STEP)"}}
+{"input": "The analysis of fiscal policies in Sub-Saharan Africa has been enriched by data from the Domestic Revenue Assessment Report (DRAR) published by the African Development Bank in 2022. This comprehensive report provides insights into revenue mobilization strategies across the region, particularly focusing on countries like Ghana and Kenya. Furthermore, the 2020 Public Expenditure Review (PER) offers a temporal perspective on spending trends, highlighting the challenges faced by the public sector. In contrast, the data from the Revenue Collection Survey (RCS) 2021 has been utilized by various stakeholders to evaluate efficiency improvements in revenue systems. These datasets not only offer valuable information for policymakers but also establish a framework for ongoing research in public financial management.", "output": {"entities": {"named_data": ["Domestic Revenue Assessment Report", "Public Expenditure Review", "Revenue Collection Survey"], "organization": ["African Development Bank"], "acronym": ["DRAR", "PER", "RCS"], "year": ["2022", "2020", "2021"], "geography": ["Sub-Saharan Africa", "Ghana", "Kenya"]}, "relations": [{"has_acronym": {"head": "Domestic Revenue Assessment Report", "tail": "DRAR"}}, {"has_timeframe": {"head": "Domestic Revenue Assessment Report", "tail": "2022"}}, {"has_geography": {"head": "Domestic Revenue Assessment Report", "tail": "Sub-Saharan Africa"}}, {"has_timeframe": {"head": "Public Expenditure Review", "tail": "2020"}}, {"has_geography": {"head": "Public Expenditure Review", "tail": "Sub-Saharan Africa"}}, {"has_timeframe": {"head": "Revenue Collection Survey", "tail": "2021"}}, {"has_geography": {"head": "Revenue Collection Survey", "tail": "Ghana"}}, {"used_by": {"head": "Revenue Collection Survey", "tail": "various stakeholders"}}, {"has_organization": {"head": "Domestic Revenue Assessment Report", "tail": "African Development Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "public financial management and domestic revenue"}}
+{"input": "The second source of data we draw on is a short survey on COVID-19 vaccination collected inperson as part of the Ethiopia Socioeconomic Survey (ESS 5), a nationally representative household survey that was implemented between April and June 2022 by the Ethiopia Statistical Service with support from the World Bank's LSMS program. This survey contained a similar module as the phone surveys and collected information on the vaccination status of all household members.\n\nThe source of administrative data for our study is the Our World in Data (OWID) COVID-19 vaccination dataset (Mathieu et al. 2021) that compiles administrative data on COVID-19 vaccine coverage. Amongst others, the dataset contains information on the number of total doses administered, the share of the country population that has received at least one dose, and the share of the population that is fully vaccinated. [3] It covers the period from December 2020 when the first COVID-19 vaccines achieved approval and is regularly updated as new data becomes available on a per-country basis. The data is compiled from country reports (such as government websites, dashboards, or the social media accounts of national authorities) and in some cases third-party aggregators (where national authorities do not publish data in a machine-readable format) and is regularly audited for inconsistencies and technical errors.\n\nWe additionally access a second source of administrative data stemming from the WHO's COVID19 vaccination dashboard (WHO 2020b). The dashboard does not provide longitudinal information for public access but reports the latest available COVID-19 vaccine coverage figures at the time of data access (April 2, 2023, in our case).\n\nLastly, we use data from the World Bank's Statistical Performance Indicators (SPI) available through the World Bank's Open Data library (World Bank n.d.). The SPI is a composite index between 0 - 100 scoring countries' statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative data systems in particular, we also use the SPI's indicator of administrative data capacity (Dimension 4.2) that records the availability of Civil Registration and Vital Statistics (CRVS).", "output": {"entities": {"named_data": ["WHO's COVID19 vaccination dashboard"], "organization": ["WHO", "World Bank"]}, "relations": [{"has_organization": {"head": "WHO's COVID19 vaccination dashboard", "tail": "WHO"}}, {"used_by": {"head": "WHO's COVID19 vaccination dashboard", "tail": "World Bank"}}]}, "_meta": {"entry_id": 51, "dataset_name": "WHO's COVID19 vaccination dashboard"}}
+{"input": "The 2020 Global Refugee and Migration Report, published by the United Nations High Commissioner for Refugees (UNHCR), offers a comprehensive analysis of forced displacement trends across various regions. In particular, it highlights the challenges faced by refugees and internally displaced persons in conflict zones such as Syria and Afghanistan. The report, which is conducted every two years, utilizes data collected from the Refugee Data Collection System (RDCS). The findings from the RDCS have been subsequently utilized by NGOs and humanitarian organizations, including Refugee Assistance Network (RAN), to inform their program strategies and advocacy efforts during the ongoing crises. This collaboration underscores the importance of reliable data in shaping effective responses to displacement issues.", "output": {"entities": {"named_data": ["Global Refugee and Migration Report", "Refugee Data Collection System"], "organization": ["United Nations High Commissioner for Refugees", "Refugee Assistance Network"], "acronym": ["RDCS"], "year": ["2020", "2020-2021"], "geography": ["Syria", "Afghanistan"]}, "relations": [{"has_organization": {"head": "Global Refugee and Migration Report", "tail": "United Nations High Commissioner for Refugees"}}, {"used_by": {"head": "Refugee Data Collection System", "tail": "Refugee Assistance Network"}}, {"has_acronym": {"head": "Refugee Data Collection System", "tail": "RDCS"}}, {"has_timeframe": {"head": "Global Refugee and Migration Report", "tail": "2020"}}, {"has_geography": {"head": "Global Refugee and Migration Report", "tail": "Syria"}}, {"has_geography": {"head": "Global Refugee and Migration Report", "tail": "Afghanistan"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "forced displacement, refugees, and migration"}}
+{"input": "total area of 122,437 sq. km. Rainfall data from more than 200 rainfall stations and\n\npotential evapotranspiration data of around 30 evaporation stations have been used in the\n\nis estimated by overlaying the inundation risk map with the population map for 2001 using\n\nAhmed, A.U., Alam, M. 1998.: Development of Climate Change Scenarios with General\nCirculation Models in Vulnerability and Adaption to Climate Change for Bangladesh, S.\nHuq, Z. Karim, M. Asaduzzaman and F. Mahtab (Eds.), Kluwer Academic Publishers,\nDordrecht, pp.13-20.\n\n\nBangladesh Bureau of Statistics (BBS), 2007. Population Census-2001: National Series,\nVolume-1 Analytical Report. Dhaka, Bangladesh.\n\n2050. The best available spatially-disaggregated maps and data for these assets have been", "output": {"entities": {"named_data": ["Population Census-2001"], "organization": ["Bangladesh Bureau of Statistics (BBS)", "Ahmed, A.U., Alam, M. 1998."]}, "relations": [{"has_organization": {"head": "Population Census-2001", "tail": "Bangladesh Bureau of Statistics (BBS)"}}, {"used_by": {"head": "Population Census-2001", "tail": "Ahmed, A.U., Alam, M. 1998."}}]}, "_meta": {"entry_id": 1098, "dataset_name": "Population Census-2001"}}
+{"input": "The Population Dynamics Assessment 2022 revealed critical trends in fertility rates across various regions. This dataset, published by the Institute for Population Studies, has been extensively utilized by the National Health Organization to inform their family planning initiatives. Additionally, the Child Development Surveys (CDS) conducted in various countries from 2019 to 2021 provided valuable insights into child health metrics, which were analyzed by UNICEF for their annual report. Both datasets underscore the importance of reliable demographic data in shaping policies and addressing population challenges effectively.", "output": {"entities": {"named_data": ["Population Dynamics Assessment 2022", "Child Development Surveys", "CDS"], "organization": ["Institute for Population Studies", "National Health Organization", "UNICEF"], "acronym": ["CDS"], "year": ["2022", "2019 to 2021"], "geography": ["various regions", "various countries"]}, "relations": [{"has_organization": {"head": "Population Dynamics Assessment 2022", "tail": "Institute for Population Studies"}}, {"used_by": {"head": "Population Dynamics Assessment 2022", "tail": "National Health Organization"}}, {"has_acronym": {"head": "Child Development Surveys", "tail": "CDS"}}, {"has_timeframe": {"head": "Child Development Surveys", "tail": "2019 to 2021"}}, {"used_by": {"head": "Child Development Surveys", "tail": "UNICEF"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "demographics, fertility, and population growth"}}
+{"input": "The recent Financial Inclusion Assessment Survey (FIAS) conducted by the Central Bank of Mercovia has revealed significant insights into the macroeconomic trends affecting the region. This dataset, which covers the year 2022, provides critical information on access to financial services and economic participation among various demographics.", "output": {"entities": {"named_data": ["Financial Inclusion Assessment Survey"], "organization": ["Central Bank of Mercovia"], "acronym": ["FIAS"], "year": ["2022"], "geography": ["Mercovia"]}, "relations": [{"has_organization": {"head": "Financial Inclusion Assessment Survey", "tail": "Central Bank of Mercovia"}}, {"has_acronym": {"head": "Financial Inclusion Assessment Survey", "tail": "FIAS"}}, {"has_timeframe": {"head": "Financial Inclusion Assessment Survey", "tail": "2022"}}, {"has_geography": {"head": "Financial Inclusion Assessment Survey", "tail": "Mercovia"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "Analysis of the impact of refugees from Ukraine on the economy of Poland Refugees from Ukraine are not yet utilising their full potential in the Polish labour market. In May 2022, 46% of arriving refugees declared that they had no knowledge of the Polish language (NBP, 2023). Over time, this percentage improved, with data from November 2022 showing a result of 21%, though this still represents a large group of people who do not know the local language [24], placing them at risk of limiting possible jobs to those below their educational level. Nearly 40% of Refugees from Ukraine insured at ZUS on 30th June 2023 in Poland were employed in elementary occupations [25], while for all employed persons in Q2 2023 this percentage was only 5%. Furthermore, the Deloitte Ukraine Refugee Pulse report indicates that 50% of respondents point to language barriers as an obstacle to accessing services to meet basic needs (Deloitte, 2023). Meanwhile, in the MSNA Poland 2023 survey, when asked about encountered barriers for accessing the labour market, 34% of respondents pointed to lack of language knowledge.", "output": {"entities": {"named_data": ["MSNA Poland 2023 survey"], "organization": ["Deloitte"]}, "relations": [{"used_by": {"head": "MSNA Poland 2023 survey", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1351, "dataset_name": "MSNA Poland 2023 survey"}}
+{"input": "The recent analysis of population trends in Mozambique utilized data from the Mozambique Fertility Survey (MFS) conducted in 2022. This survey provides critical insights into fertility rates and demographic changes within the country.", "output": {"entities": {"named_data": ["Mozambique Fertility Survey"], "organization": ["Mozambique"], "acronym": ["MFS"], "year": ["2022"], "geography": ["Mozambique"]}, "relations": [{"has_organization": {"head": "Mozambique Fertility Survey", "tail": "Mozambique"}}, {"has_acronym": {"head": "Mozambique Fertility Survey", "tail": "MFS"}}, {"has_timeframe": {"head": "Mozambique Fertility Survey", "tail": "2022"}}, {"has_geography": {"head": "Mozambique Fertility Survey", "tail": "Mozambique"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "demographics, fertility, and population growth"}}
+{"input": "Bank and Israel and occupied territories strongly suggest that commuters would be differently affected during the pandemic when border closures were enacted. Another unique feature is the presence of a large refugee population in the West Bank and Gaza. However, it is important to note that refugees in this context are de- fined quite differently from other contexts. Here, not only the individuals immediately displaced are considered refugees, but also their patrilineal descendants, even if born many decades later. In particular, the LFS dataset follows the United Nations Relief and Works Agency (UNRWA) definition of refugees, which is “ persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948, and who lost both home and means of livelihood as a result of the 1948 conflict, ” as well as “ the descendants of Palestine refugee males, including adopted children ” (UNRWA, 2023). Consequently, most refugees are indistinguishable in socio-economic outcomes and labor market behavior from non-refugees. However, residence in refugee camps does make a significant difference. As of 2019Q4, 5 % of the West Bank ’ s residents live in refugee camps, as do 14 % in Gaza.", "output": {"entities": {"named_data": ["LFS"], "organization": ["United Nations Relief and Works Agency"]}, "relations": [{"has_organization": {"head": "LFS", "tail": "United Nations Relief and Works Agency"}}]}, "_meta": {"entry_id": 120, "dataset_name": "LFS"}}
+{"input": "**The World Bank** Integrated Community Resilience Project (P506969) PROJECT APPRAISAL DOCUMENT support nutritional consumption needs - accompanied with SBCC to promote human development behavior change good practices; (ii) financial inclusion services (FIS) to strengthen the economic resilience of poor and vulnerable households; and (iii) support for strengthening the Social Registry (SR) so that it becomes a stronger underpinning for effective coordination of social programs, ensures better targeting of safety nets, and provides credible information for measurement of results and impacts for all vulnerable populations, including refugees and hosts. 30. **Sustainability of the social safety nets** . Sustainable financing is critical to building an effective and efficient safety net system in Djibouti that can mitigate the adverse impacts of the perennial crises on the poor and vulnerable. The government committed to making efforts to increasing government financing for social safety nets as project financing declines. This commitment is being renewed and related technical simulations will be mainstreamed into the technical assistance that is being provided by the World Bank under the Strengthening Adaptive Social Protection Systems (P166220). Recommendations from the Technical Assistance will be discussed with the MASS and the Ministry of Economy and Finance (MEFI) and be mainstreamed into the", "output": {"entities": {"named_data": ["Social Registry"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Social Registry", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1226, "dataset_name": "Social Registry"}}
+{"input": "The Economic Competitiveness Assessment 2022 conducted by the Global Trade Institute provides crucial insights into trade patterns across Southeast Asia. Additionally, this dataset, along with the Industry Growth Data (IGD) published by the Asian Development Bank, is extensively utilized by local governments for formulating policy. The IGD emphasizes regional economic trends from 2019 to 2021, and is particularly focused on Malaysia and Thailand, facilitating targeted economic interventions.", "output": {"entities": {"named_data": ["Economic Competitiveness Assessment 2022", "Industry Growth Data"], "organization": ["Global Trade Institute", "Asian Development Bank", "local governments"], "acronym": ["Industry Growth Data"], "year": ["2022", "2019 to 2021"], "geography": ["Southeast Asia", "Malaysia", "Thailand"]}, "relations": [{"has_organization": {"head": "Economic Competitiveness Assessment 2022", "tail": "Global Trade Institute"}}, {"used_by": {"head": "Economic Competitiveness Assessment 2022", "tail": "local governments"}}, {"has_organization": {"head": "Industry Growth Data", "tail": "Asian Development Bank"}}, {"used_by": {"head": "Industry Growth Data", "tail": "local governments"}}, {"has_acronym": {"head": "Industry Growth Data", "tail": "IGD"}}, {"has_timeframe": {"head": "Industry Growth Data", "tail": "2019 to 2021"}}, {"has_geography": {"head": "Industry Growth Data", "tail": "Malaysia"}}, {"has_geography": {"head": "Industry Growth Data", "tail": "Thailand"}}, {"has_geography": {"head": "Economic Competitiveness Assessment 2022", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The 2022 Environmental Change Assessment (ECA) was published by the Global Institute for Environmental Studies, providing crucial data for understanding land use changes over time. In addition, the Urban Spatial Dynamics Database (USDD), developed by the International Land Management Agency, focuses on urban growth patterns from 2015 to 2021. This dataset is utilized extensively by local governments in Southeast Asia, particularly in Malaysia, to inform their urban planning strategies. Furthermore, the Agricultural Land Use Mapping Survey (ALUMS) conducted in 2020, has been invaluable for agricultural planners and is often referenced by the World Bank for policy development. Each of these datasets plays a significant role in shaping land use policies and planning across various geographies.", "output": {"entities": {"named_data": ["Environmental Change Assessment", "Urban Spatial Dynamics Database", "Agricultural Land Use Mapping Survey"], "organization": ["Global Institute for Environmental Studies", "International Land Management Agency", "World Bank"], "acronym": ["ECA", "USDD", "ALUMS"], "year": ["2022", "2015 to 2021", "2020"], "geography": ["Southeast Asia", "Malaysia"]}, "relations": [{"has_organization": {"head": "Environmental Change Assessment", "tail": "Global Institute for Environmental Studies"}}, {"has_organization": {"head": "Urban Spatial Dynamics Database", "tail": "International Land Management Agency"}}, {"used_by": {"head": "Urban Spatial Dynamics Database", "tail": "local governments"}}, {"has_geography": {"head": "Urban Spatial Dynamics Database", "tail": "Malaysia"}}, {"has_timeframe": {"head": "Urban Spatial Dynamics Database", "tail": "2015 to 2021"}}, {"has_organization": {"head": "Agricultural Land Use Mapping Survey", "tail": "World Bank"}}, {"has_timeframe": {"head": "Agricultural Land Use Mapping Survey", "tail": "2020"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "The Macroeconomic Trends Assessment Report 2022 provides valuable insights into the financial inclusion landscape across various developing nations. This report, produced by the Global Economy Institute, highlights the economic barriers that hinder access to financial services for underserved populations. Key findings illustrate the correlation between economic growth and improvements in financial inclusion metrics. The report serves as a crucial resource for policymakers aiming to enhance financial accessibility in their regions.", "output": {"entities": {"named_data": ["Macroeconomic Trends Assessment Report 2022"], "organization": ["Global Economy Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Macroeconomic Trends Assessment Report 2022", "tail": "Global Economy Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "86. Communities and individuals who believe that they are adversely affected by a World\nBank (WB) supported project may submit complaints to existing project-level grievance redress\nmechanisms or the WB’s Grievance Redress Service (GRS). The GRS ensures that complaints\nreceived are promptly reviewed in order to address project-related concerns. Project affected\ncommunities and individuals may submit their complaint to the WB’s independent Inspection\nPanel which determines whether harm occurred, or could occur, as a result of WB noncompliance with its policies and procedures. Complaints may be submitted at any time after\nconcerns have been brought directly to the World Bank's attention, and Bank Management has\nbeen given an opportunity to respond. For information on how to submit complaints to the World\nBank’s corporate Grievance Redress Service (GRS), please visit\n[http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-](http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service)\n[service. For information on how to submit complaints to the World Bank Inspection Panel, please](http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service)\n[visit www.inspectionpanel.org.](http://www.inspectionpanel.org/)\n\n**V.** **KEY RISKS**\n\n87. **The overall risk of the project is** _**High**_ **.** The specific project risks as defined in the\nSystematic Operations Rating Tool (SORT) are outlined in the following section.\n\n88. **The fragile social and economic situation in Chad is the backdrop to an ongoing major**", "output": {"entities": {"named_data": ["Systematic Operations Rating Tool"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "Systematic Operations Rating Tool", "tail": "World Bank"}}, {"used_by": {"head": "Systematic Operations Rating Tool", "tail": "World Bank"}}]}, "_meta": {"entry_id": 897, "dataset_name": "Systematic Operations Rating Tool"}}
+{"input": "In examining the recent trends in employment, the National Employment Survey (NES) provides invaluable insights into the labor market across various sectors. Conducted in 2022, the NES focuses on job availability and skill demands in major urban areas of Brazil. Furthermore, the Global Skills Assessment Report (GSAR) for 2021, published by the International Labour Organization (ILO), highlights critical skill gaps that hinder economic growth in developing regions. In contrast, the Youth Employment Data (YED) 2020 presents a comprehensive view of the challenges faced by young job seekers in the Middle East and North Africa, but lacks extensive geographic coverage beyond the primary participant countries.", "output": {"entities": {"named_data": ["National Employment Survey", "Global Skills Assessment Report", "Youth Employment Data"], "organization": ["International Labour Organization"], "acronym": ["NES", "GSAR", "YED"], "year": ["2022", "2021", "2020"], "geography": ["Brazil", "Middle East and North Africa"]}, "relations": [{"has_acronym": {"head": "National Employment Survey", "tail": "NES"}}, {"has_timeframe": {"head": "National Employment Survey", "tail": "2022"}}, {"has_geography": {"head": "National Employment Survey", "tail": "Brazil"}}, {"has_acronym": {"head": "Global Skills Assessment Report", "tail": "GSAR"}}, {"has_timeframe": {"head": "Global Skills Assessment Report", "tail": "2021"}}, {"has_organization": {"head": "Global Skills Assessment Report", "tail": "International Labour Organization"}}, {"has_acronym": {"head": "Youth Employment Data", "tail": "YED"}}, {"has_timeframe": {"head": "Youth Employment Data", "tail": "2020"}}, {"has_geography": {"head": "Youth Employment Data", "tail": "Middle East and North Africa"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "employment, labor markets, and skills development"}}
+{"input": "The Urban Land Use Mapping Database (ULUMD) provides crucial data for understanding land use changes in metropolitan areas. Published by the Geospatial Research Institute, this dataset offers insights into urban sprawl and green space distribution from 2015 to 2020.", "output": {"entities": {"named_data": ["Urban Land Use Mapping Database"], "organization": ["Geospatial Research Institute"], "acronym": ["ULUMD"], "year": ["2015", "2020"], "geography": []}, "relations": [{"has_organization": {"head": "Urban Land Use Mapping Database", "tail": "Geospatial Research Institute"}}, {"has_acronym": {"head": "Urban Land Use Mapping Database", "tail": "ULUMD"}}, {"has_timeframe": {"head": "Urban Land Use Mapping Database", "tail": "2015"}}, {"has_timeframe": {"head": "Urban Land Use Mapping Database", "tail": "2020"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016).", "output": {"entities": {"named_data": ["asylum seeker certificate"], "organization": ["UNHCR", "NRC and IHRC"]}, "relations": [{"has_organization": {"head": "asylum seeker certificate", "tail": "UNHCR"}}, {"used_by": {"head": "asylum seeker certificate", "tail": "NRC and IHRC"}}]}, "_meta": {"entry_id": 270, "dataset_name": "asylum seeker certificate"}}
+{"input": "street vending (48 % of those with at least 1 IGA), food processing for sale, including baking, cooking, and drying (16 %), and home production of crops, livestock, and fish (11 %). It is important to note that the EPAG program was not targeted toward the most vulnerable segments of Liberian society, but rather toward young women with enough education to be able to benefit from a training program of this nature. Based on comparisons from the 2007 CWIQ (Core Welfare Indicators Questionnaire) survey, the average EPAG participant is more educated, more literate, more likely to be engaged in an income-generating activity, and owns more assets than the average Liberian women of the same age group: mobile phone ownership was high (63 %), as was the proportion reporting that they had some money of their own (79 %). Even compared to other similar residents of Monrovia, the EPAG participants are better educated and have higher income. A strong sense of female empowerment at baseline emerges from the sections of the survey instrument having to do with self-confidence and agency.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire"], "organization": ["EPAG"]}, "relations": [{"used_by": {"head": "Core Welfare Indicators Questionnaire", "tail": "EPAG"}}]}, "_meta": {"entry_id": 61, "dataset_name": "Core Welfare Indicators Questionnaire"}}
+{"input": "The recent Environmental Impact Assessment Report (EIA Report) published by the Global Sustainability Organization provides crucial insights into the effects of urban development on local ecosystems. This report, covering the year 2022, highlights significant findings relevant to policymakers and urban planners.", "output": {"entities": {"named_data": ["Environmental Impact Assessment Report"], "organization": ["Global Sustainability Organization"], "acronym": ["EIA Report"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Environmental Impact Assessment Report", "tail": "Global Sustainability Organization"}}, {"has_acronym": {"head": "Environmental Impact Assessment Report", "tail": "EIA Report"}}, {"has_timeframe": {"head": "Environmental Impact Assessment Report", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
+{"input": "The Economic Performance Assessment Report (EPAR) for East Asia provides an in-depth analysis of macroeconomic indicators from 2019 to 2021. This report, produced by the Asian Development Bank, highlights key trends in financial inclusion across the region. Additionally, the Financial Inclusion Survey (FIS) 2020 offers valuable insights into the accessibility of financial services among different demographics in Vietnam. Both datasets serve as critical resources for policymakers and researchers aiming to enhance economic strategies in the area.", "output": {"entities": {"named_data": ["Economic Performance Assessment Report", "Financial Inclusion Survey"], "organization": ["Asian Development Bank"], "acronym": ["EPAR", "FIS"], "year": ["2019 to 2021", "2020"], "geography": ["East Asia", "Vietnam"]}, "relations": [{"has_acronym": {"head": "Economic Performance Assessment Report", "tail": "EPAR"}}, {"has_timeframe": {"head": "Economic Performance Assessment Report", "tail": "2019 to 2021"}}, {"has_geography": {"head": "Economic Performance Assessment Report", "tail": "East Asia"}}, {"has_acronym": {"head": "Financial Inclusion Survey", "tail": "FIS"}}, {"has_timeframe": {"head": "Financial Inclusion Survey", "tail": "2020"}}, {"has_geography": {"head": "Financial Inclusion Survey", "tail": "Vietnam"}}, {"has_organization": {"head": "Economic Performance Assessment Report", "tail": "Asian Development Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of confidence as refugees generally. 23 Cross-country data about conflict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conflicts. In other words, we attempt to answer several questions.", "output": {"entities": {"named_data": ["Penn World Table"], "organization": ["we"]}, "relations": [{"used_by": {"head": "Penn World Table", "tail": "we"}}]}, "_meta": {"entry_id": 14, "dataset_name": "Penn World Table"}}
+{"input": "The recent Financial Inclusion Metrics Report 2022, published by the Global Financial Observatory, provides essential insights into access to banking services across various regions. This report, analyzed extensively by the International Monetary Fund, highlights the significant disparities in financial access among countries, with a particular focus on Sub-Saharan Africa. Additionally, the 2021 Microfinance Data Collection Initiative, also released by the Global Financial Observatory, has drawn interest from the World Bank as they evaluate the effectiveness of microfinance programs in improving economic stability. These datasets collectively underscore the ongoing challenges in achieving equitable financial access and are critical for informing policy decisions moving forward.", "output": {"entities": {"named_data": ["Financial Inclusion Metrics Report 2022", "Microfinance Data Collection Initiative"], "organization": ["Global Financial Observatory", "International Monetary Fund", "World Bank"], "acronym": [], "year": ["2022", "2021"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Financial Inclusion Metrics Report 2022", "tail": "Global Financial Observatory"}}, {"used_by": {"head": "Financial Inclusion Metrics Report 2022", "tail": "International Monetary Fund"}}, {"has_timeframe": {"head": "Financial Inclusion Metrics Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Financial Inclusion Metrics Report 2022", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Microfinance Data Collection Initiative", "tail": "Global Financial Observatory"}}, {"used_by": {"head": "Microfinance Data Collection Initiative", "tail": "World Bank"}}, {"has_timeframe": {"head": "Microfinance Data Collection Initiative", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "The Public Revenue Assessment Report 2022 provides a comprehensive overview of the revenue collection practices in various countries. This dataset, published by the Global Financial Institute, serves as a valuable resource for policymakers and researchers aiming to understand and improve public financial management. The report highlights key trends in domestic revenue generation and offers insights into best practices that can be adopted across different regions.", "output": {"entities": {"named_data": ["Public Revenue Assessment Report 2022"], "organization": ["Global Financial Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Public Revenue Assessment Report 2022", "tail": "Global Financial Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "public financial management and domestic revenue"}}
+{"input": "The Poverty Assessment Report 2023 offers comprehensive insights into the dynamics of poverty and inequality across various regions. It was developed by the Global Poverty Research Institute and provides vital data for policymakers and researchers alike. This report will be instrumental for governments and NGOs seeking to understand and address poverty levels effectively.", "output": {"entities": {"named_data": ["Poverty Assessment Report 2023"], "organization": ["Global Poverty Research Institute"], "acronym": [], "year": ["2023"], "geography": []}, "relations": [{"has_organization": {"head": "Poverty Assessment Report 2023", "tail": "Global Poverty Research Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "poverty headcount and inequality measurement"}}
+{"input": "The recent publication on agricultural productivity highlights the significance of the Food Security Assessment Report. This document provides a comprehensive overview of the current status of food availability and access, emphasizing the challenges faced by rural communities. It further discusses the impacts of climate change on crop yields and suggests strategies for improving resilience among farmers. The insights drawn from this assessment can inform policy decisions aimed at enhancing food security.", "output": {"entities": {"named_data": ["Food Security Assessment Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "agriculture, food security, and nutrition"}}
+{"input": "The recent Urban Mobility Assessment Report 2023, published by the International Transport Forum, highlights key trends in urban transportation systems around the globe. This report draws on the comprehensive data collected from the Global Urban Infrastructure Survey, conducted by the World Bank. Stakeholders in various cities are utilizing these insights to improve transit systems and urban planning initiatives.", "output": {"entities": {"named_data": ["Urban Mobility Assessment Report 2023", "Global Urban Infrastructure Survey"], "organization": ["International Transport Forum", "World Bank"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Urban Mobility Assessment Report 2023", "tail": "International Transport Forum"}}, {"has_organization": {"head": "Global Urban Infrastructure Survey", "tail": "World Bank"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "urban infrastructure and transportation planning"}}
+{"input": "The latest findings from the Global Education Assessment Database (GEAD) indicate significant improvements in literacy rates among primary school children in East Africa. Published by the International Education Foundation (IEF), this dataset covers the period from 2019 to 2021. The data has been extensively used by the East African Community (EAC) to inform policy changes in educational strategies across member states. Additionally, the National Learning Achievement Survey (NLAS) conducted in 2020 provides a detailed analysis of factors influencing student performance in Kenya. The Ministry of Education in Kenya has cited this survey to design targeted interventions aimed at enhancing learning outcomes in rural areas.", "output": {"entities": {"named_data": ["Global Education Assessment Database", "National Learning Achievement Survey"], "organization": ["International Education Foundation", "East African Community", "Ministry of Education"], "acronym": ["GEAD", "NLAS"], "year": ["2019", "2021", "2020"], "geography": ["East Africa", "Kenya"]}, "relations": [{"has_organization": {"head": "Global Education Assessment Database", "tail": "International Education Foundation"}}, {"used_by": {"head": "Global Education Assessment Database", "tail": "East African Community"}}, {"has_timeframe": {"head": "Global Education Assessment Database", "tail": "2019 to 2021"}}, {"has_geography": {"head": "Global Education Assessment Database", "tail": "East Africa"}}, {"has_organization": {"head": "National Learning Achievement Survey", "tail": "Ministry of Education"}}, {"has_timeframe": {"head": "National Learning Achievement Survey", "tail": "2020"}}, {"has_geography": {"head": "National Learning Achievement Survey", "tail": "Kenya"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "(vi) ran out of food, (vii) adults were hungry but did not eat, (viii) went without eating for a whole day, (ix) restricted consumption so kids could eat, and (x) borrowed food or relied on friend/relative for help. 7.0 8.4 7.8 6.0 8.2 6.5 In Camp Addis Ababa Total Hosts Refugees 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Poor (0-21) Borderline (21-35) Acceptable ( > 35) Percent Figure 5.10: Dietary diversity and food consumption status Source: World Bank Staff based on SESRE 2023. Note: Dietary diversity score is calculated as the total number of food groups (out of 12) consumed by the household in the last seven days before the survey. The food groups are cereals, roots and tubers, vegetables, fruits, meat (including poultry and offal), eggs, fish and seafood, pulses and legumes and nuts, milk and milk products, oils and fats, sugar/honey, and others. Food consumption status is determined based on food consumption score. a. Dietary diversity score (out of 12 groups) b. Food consumption status Refugees’ Aspirations 49 Moreover, insecurity and displacement-related shocks are common for Eritrean refugees, with 14 percent having experienced", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank", "World Bank Staff"]}, "relations": [{"has_organization": {"head": "SESRE 2023", "tail": "World Bank"}}, {"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 735, "dataset_name": "SESRE 2023"}}
+{"input": "Medium ‐ intensity, on the other hand refers to “ regular armed clashes between governments, government forces and insurgents. ” (IISS 2017) Within the first category, according to the Armed Conflict Survey in 2016 are Afghanistan, Syria, Somalia, South Sudan, and Iraq, while Myanmar, Central African Republic, Democratic Republic of the Congo, and Sudan are in medium ‐ intensity conflict. (Eritrea is not included in the Armed Conflict Survey in 2017, discussed in Annex). Minorities in Albania, Kosovo and Serbia are often the objects of discrimination but countries are not in conflict. A capsule summary of the security and social situation in each of the other countries included in this survey can be found in Annex 2. The survey illustrates how impractical return is today in the countries in conflict, such as Syria, Afghanistan, and Iraq, and voluntary return in large 36 Best practice and new methods in return policy, July 2017, http: / / www. bamf. de / SharedDocs / Meldungen / EN / 2017 / EMN / 20170504 ‐ emnjahrestagung ‐ rueckkehr. html", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "organization": ["IISS"]}, "relations": [{"used_by": {"head": "Armed Conflict Survey", "tail": "IISS"}}]}, "_meta": {"entry_id": 1087, "dataset_name": "Armed Conflict Survey"}}
+{"input": "The recent Southeast Asia Fertility Assessment Report 2022 provides essential insights into reproductive health trends across the region. This comprehensive analysis, produced by the Southeast Asia Health Organization (SAHO), covers critical demographic changes observed in 2020–2021. Additionally, the West African Population Growth Survey (WAPGS) is crucial for understanding the population dynamics in West African countries and was published by the African Development Initiative (ADI) in 2021. These datasets will be valuable for researchers and policymakers aiming to address the evolving challenges in population health.", "output": {"entities": {"named_data": ["Southeast Asia Fertility Assessment Report 2022", "West African Population Growth Survey"], "organization": ["Southeast Asia Health Organization", "African Development Initiative"], "acronym": ["Southeast Asia Fertility Assessment", "WAPGS"], "year": ["2022", "2020–2021", "2021"], "geography": ["Southeast Asia", "West Africa"]}, "relations": [{"has_acronym": {"head": "Southeast Asia Fertility Assessment Report", "tail": "Southeast Asia Fertility Assessment"}}, {"has_timeframe": {"head": "Southeast Asia Fertility Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Southeast Asia Fertility Assessment Report 2022", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Southeast Asia Fertility Assessment Report 2022", "tail": "Southeast Asia Health Organization"}}, {"has_acronym": {"head": "West African Population Growth Survey", "tail": "WAPGS"}}, {"has_timeframe": {"head": "West African Population Growth Survey", "tail": "2021"}}, {"has_geography": {"head": "West African Population Growth Survey", "tail": "West Africa"}}, {"has_organization": {"head": "West African Population Growth Survey", "tail": "African Development Initiative"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "demographics, fertility, and population growth"}}
+{"input": "In recent years, the Global Education Monitoring Report (GEMR) has provided crucial insights into learning achievement and school enrollment trends across various countries. Covering the period from 2020 to 2022, this dataset highlights disparities in education quality and access in regions such as Sub-Saharan Africa. The analysis drawn from the GEMR has been referenced by multiple educational NGOs striving to enhance policy frameworks. The report underscores the importance of data-driven decision-making in education policy, particularly for countries facing significant enrollment challenges. Source: UNESCO elaboration based on the Global Education Monitoring Report.", "output": {"entities": {"named_data": ["Global Education Monitoring Report"], "organization": ["UNESCO"], "acronym": ["GEMR"], "year": ["2020", "2021", "2022"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Global Education Monitoring Report", "tail": "GEMR"}}, {"has_timeframe": {"head": "Global Education Monitoring Report", "tail": "2020 to 2022"}}, {"has_geography": {"head": "Global Education Monitoring Report", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Global Education Monitoring Report", "tail": "UNESCO"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Survey). [6] Only 2 percent of respondents to a recent survey reported that they were working and had work permits. About 65 percent of the beneficiaries of the Emergency Social Safety Net Program (ESSN), a temporary humanitarian program, report that their main source of income is short-term informal work. [7] This will become a more significant problem once the ESSN comes to an end.\n\n13. **One of the most important contextual factors that limits formal job creation is the poor access**\n**to financing among firms.** Credit service provision is less developed in many provinces where refugees\nlive and work. According to the World Bank Enterprise Survey, most respondents (76 percent) in the affected regions assert that access to finance deteriorated loan terms and conditions (interest rates, maturity, and collateral requirements). [8] Poor access to longer-term financing limits enterprises from investing, increasing production capacity, and providing sustainable employment opportunities. After high tax rates, access to finance is perceived as a top constraint on firms, particularly small and medium enterprises (SMEs), seeking to carry out and expand business in Turkey. [9] Limited access to finance can also have a negative impact on labor market outcomes, resulting in higher unemployment, higher workforce informality, and lower employment growth. Limited access to credit is also problematic among large enterprises (LEs) because these have the potential to create more jobs, especially among refugees, including higher-quality formal jobs. [10] 14. **Banks do not usually have adequately structured resources to offer medium- to long-term**\n**maturities to most firms, mostly because of the short term of their liability base, thus leaving firms,**\n**mostly SMEs, open to severe liquidity and interest rate risk.** **[11]** Lack of cash flow-based financing and high\ncollateral requirements constrain access to finance among SMEs. [12] After the global financial crisis and strong rebalancing in the economy after August 2018, major banks have significantly cut their exposure to SMEs and LEs. The banking system has limited access to long‐term financing. It is funded mostly by relatively stable customer deposits that mature in less than three months, while most of the lending is concentrated in loans for more than three months. The result is a negative liquidity gap, that is, more liquid liabilities than assets, or a liquidity mismatch risk, which peaks in the one- to five‐year maturity range. These imbalances are reflected in bank loan portfolios and the liability structure of enterprises. The bank‐dominated financial sector thus has only a limited ability to provide the maturity critical to support SMEs and LEs that need to make long‐term investments, expand production capacity, and increase employment. In order to address the problems mentioned above, the government introduced some measures to improve SMEs access to finance and their entrepreneurial capacities, that could result effective in the medium to long-term: 6 Turkish Red Crescent and World Food Programme. 2019. _Refugees in Turkey. Livelihoods Survey Findings._ Ankara: Turk Kizilay and World Food Programme.\n7 World Bank and World Food Programme. 2019. _Vulnerability and Protection of Refugees in Turkey: Findings from the Rollout of_ _the Largest Humanitarian Cash Assistance Program in the World_ . Washington, DC: World Bank and World Food Programme.\n8 Enterprise Surveys (database), International Finance Corporation and World Bank, Washington, DC, https://www.enterprisesurveys.org/.\n9 Enterprise Surveys (database), International Finance Corporation and World Bank, Washington, DC., https://www.enterprisesurveys.org/.\n10 Ayyagari, M., A. Demirgüç-Kunt, and V. Maksimovic. 2011. “Small vs. Young Firms Across the World: Contribution to Employment, Job Creation, and Growth.” Policy Research Working Paper 5631, World Bank, Washington, DC.\n11 World Bank. 2014. _Turkey’s Transitions: Integration, Inclusion, Institutions_ . Report 90509-TR. Washington, DC: World Bank.\n12 World Bank 2014 and 2018 data of the Survey on the Access to Finance of Enterprises (database), European Central Bank, Frankfurt, https://www.ecb.europa.eu/stats/ecb_surveys/safe/html/index.en.html.\n\nPage 11 of 86", "output": {"entities": {"named_data": ["Enterprise Surveys (database)"], "organization": ["International Finance Corporation and World Bank", "World Bank"]}, "relations": [{"has_organization": {"head": "Enterprise Surveys (database)", "tail": "International Finance Corporation and World Bank"}}, {"used_by": {"head": "Enterprise Surveys (database)", "tail": "World Bank"}}]}, "_meta": {"entry_id": 890, "dataset_name": "Enterprise Surveys (database)"}}
+{"input": "In this sense, it is necessary to take into consideration that all indicators of prevalence of NSE and its profile will be limited to a subset of this kind of workers. 4. 1 Latin America and the Caribbean This section focuses on the Latin America and the Caribbean region, where a set of 9 countries, that we consider representing the different realities of the region in an exhaustive way, was analyzed. Specifically, the analysis was conducted for Argentina, Brazil, Bolivia, Chile, El Salvador, Mexico, Peru, Dominican Republic and Uruguay. 5O * NET is the successor of DOT (Dictionary of Occupational Titles) which is no longer updated. O * NET was launched in 1998 on the basis of the BLS Occupational Employment Statistics codes. In 2003, it was changed to SOC which implies that the consistent measures of task content are calculated from 2003.", "output": {"entities": {"named_data": ["BLS Occupational Employment Statistics"], "organization": ["BLS"]}, "relations": [{"has_organization": {"head": "BLS Occupational Employment Statistics", "tail": "BLS"}}]}, "_meta": {"entry_id": 209, "dataset_name": "BLS Occupational Employment Statistics"}}
+{"input": "The Global Land Cover Assessment (GLCA) was conducted over the period from 2018 to 2020 to map various land uses across Africa. This dataset, published by the African Environmental Agency, provides comprehensive geospatial information that is crucial for understanding land changes. In addition, the Urban Heat Island Effect Study (UHIES) covers urban areas in Southeast Asia for the year 2021, focusing on the impact of urbanization on heat distribution. Many urban planners and researchers have utilized this dataset to inform their projects and policy decisions. Lastly, the Agroforestry Practices Survey (APS) focuses on sustainable land management practices in Brazil from 2019 to 2023 and has gained attention from various NGOs for its insights into climate adaptation strategies.", "output": {"entities": {"named_data": ["Global Land Cover Assessment", "Urban Heat Island Effect Study", "Agroforestry Practices Survey"], "organization": ["African Environmental Agency", "NGOs"], "acronym": ["GLCA", "UHIES", "APS"], "year": ["2018 to 2020", "2021", "2019 to 2023"], "geography": ["Africa", "Southeast Asia", "Brazil"]}, "relations": [{"has_acronym": {"head": "Global Land Cover Assessment", "tail": "GLCA"}}, {"has_timeframe": {"head": "Global Land Cover Assessment", "tail": "2018 to 2020"}}, {"has_geography": {"head": "Global Land Cover Assessment", "tail": "Africa"}}, {"has_acronym": {"head": "Urban Heat Island Effect Study", "tail": "UHIES"}}, {"has_timeframe": {"head": "Urban Heat Island Effect Study", "tail": "2021"}}, {"has_geography": {"head": "Urban Heat Island Effect Study", "tail": "Southeast Asia"}}, {"used_by": {"head": "Urban Heat Island Effect Study", "tail": "urban planners and researchers"}}, {"has_acronym": {"head": "Agroforestry Practices Survey", "tail": "APS"}}, {"has_timeframe": {"head": "Agroforestry Practices Survey", "tail": "2019 to 2023"}}, {"has_geography": {"head": "Agroforestry Practices Survey", "tail": "Brazil"}}, {"used_by": {"head": "Agroforestry Practices Survey", "tail": "NGOs"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "(GSS). [2] Point coordinates (global positioning system [GPS]) for the surveyed DHS clusters [3] allow us to match all individuals to one or several mineral mines. We do this in two ways. First, we calculate distance spans from an exact mine location given by its GPS coordinates, and match surveyed individuals to mines. These are concentric circles with radiuses of 10, 20, and 30 kilometers (km), and so on, up to 100 km and beyond. In the baseline analysis where 2 The data was shared by Aragón and Rud (2013) 3 Both the DHS and GLSS enumeration area coordinates have a 1-5 km offset. The DHS clusters have up to 10km displacement in 1% of the cases. 7", "output": {"entities": {"named_data": ["GLSS"], "organization": ["Aragón and Rud"]}, "relations": [{"used_by": {"head": "GLSS", "tail": "Aragón and Rud"}}]}, "_meta": {"entry_id": 1127, "dataset_name": "GLSS"}}
+{"input": "**The World Bank**\nSouth Sudan Health Sector Transformation Project (HSTP) (P181385)\n\n|Frequency|Annually|\n|---|---|\n|Data source|Survey|\n|Methodology for Data<br>Collection|Survey|\n|Responsibility for Data<br>Collection|Third Party Monitor / PMU|\n|**Contraceptive prevalence rate (any method)**|**Contraceptive prevalence rate (any method)**|\n|Description|Percentage of women aged 15− 9 years, married or in union, who are currently using, or whose sexual partner is<br>using, at least one method of contraception, regardless of the method used.|\n|Frequency|Annually|\n|Data source|Survey|\n|Methodology for Data<br>Collection|Survey|\n|Responsibility for Data<br>Collection|Third Party Monitor / PMU|\n|**The proportion of patients with suspected malaria who received a parasitologic test (RDT/Microscopy)**|**The proportion of patients with suspected malaria who received a parasitologic test (RDT/Microscopy)**|\n|Description|Percentage of suspected malaria cases that received parasitological diagnosis either by microscopy or RDT|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH / UNICEF; Measures subcomponent 1.1 Under UNICEF|\n|**Proportion of health facilities that have a core set of relevant basic medicines and commodities available and affordable**|**Proportion of health facilities that have a core set of relevant basic medicines and commodities available and affordable**|\n|Description|Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a<br>sustainable basis. Availability: will be calculated based on currently existing data on average proportion of<br>medicines available in health facilities per country.|\n|Frequency|Quarterly|\n|Data source|Quarterly Health Facility Assessment|\n|Methodology for Data<br>Collection|TPM report|\n|Responsibility for Data<br>Collection|TPM / PMU|\n|**Component 2: Health Systems Strengthening**|**Component 2: Health Systems Strengthening**|\n|**Percentage of disease outbreaks in refugee areas that are adequately addressed as per WHO guidelines (Percentage)**|**Percentage of disease outbreaks in refugee areas that are adequately addressed as per WHO guidelines (Percentage)**|\n|Description|Disease outbreaks in refugee areas that have been adequately addressed as per WHO guidelines.|\n|Frequency|Quarterly|\n|Data source|WHO/MoH report|\n|Methodology for Data<br>Collection|WHO to provide data|\n|Responsibility for Data<br>Collection|UNICEF/WHO/ PMU- Measures subcomponent 2.1 under WHO|\n|**Percentage of SMoH/CHDs with work plans aligned to the HSSP**|**Percentage of SMoH/CHDs with work plans aligned to the HSSP**|\n|Description|Percentage of SMoH and CHDs that develop annual operational work plans aligned to HSSP|\n|Frequency|Quarterly|\n|Data source|WHO report|\n|Methodology for Data<br>Collection|WHO to provide data / TPM to verify|\n|Responsibility for Data<br>Collection|PMU / TPM; Measures subcomponent 2.1 under WHO|\n|**Proportion of health alerts investigated in 48 hrs**|**Proportion of health alerts investigated in 48 hrs**|", "output": {"entities": {"named_data": ["DHIS2"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "DHIS2", "tail": "World Bank"}}]}, "_meta": {"entry_id": 824, "dataset_name": "DHIS2"}}
+{"input": "The Climate Impact Assessment Report 2022 highlights the challenges faced by vulnerable communities due to climate change. Produced by the Global Environment Facility, this report provides crucial insights into adaptation strategies. Additionally, the Disaster Risk Reduction Data Initiative, published by the United Nations Office for Disaster Risk Reduction, offers a comprehensive overview of disaster preparedness efforts across various regions. These datasets are instrumental for policymakers and researchers working toward enhancing climate resilience.", "output": {"entities": {"named_data": ["Climate Impact Assessment Report 2022", "Disaster Risk Reduction Data Initiative"], "organization": ["Global Environment Facility", "United Nations Office for Disaster Risk Reduction"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Climate Impact Assessment Report 2022", "tail": "Global Environment Facility"}}, {"has_organization": {"head": "Disaster Risk Reduction Data Initiative", "tail": "United Nations Office for Disaster Risk Reduction"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "The analysis focuses on the Domestic Revenue Assessment Report 2022, which highlights the key trends in revenue generation across various sectors. Notably, the report was published by the International Monetary Fund (IMF) and utilized extensively by numerous countries aiming to improve their fiscal policies. Additionally, the Public Financial Management Database, produced by the World Bank, offers a wealth of information on budgetary allocations and expenditures internationally. This dataset has been a fundamental resource for policymakers looking to strengthen their financial governance.", "output": {"entities": {"named_data": ["Domestic Revenue Assessment Report 2022", "Public Financial Management Database"], "organization": ["International Monetary Fund", "World Bank"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Domestic Revenue Assessment Report 2022", "tail": "International Monetary Fund"}}, {"used_by": {"head": "Domestic Revenue Assessment Report 2022", "tail": "numerous countries"}}, {"has_organization": {"head": "Public Financial Management Database", "tail": "World Bank"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "public financial management and domestic revenue"}}
+{"input": "The Urban Mobility Assessment Report 2022, published by the Global Transport Institute, provides crucial insights into transportation trends in major cities across the globe. Meanwhile, the City Infrastructure Database (CID) has been extensively used by local governments for planning sustainable urban development. The CID, covering data from 2019 to 2021, also assists organizations like the Urban Planning Network (UPN) in formulating strategies to enhance public transport systems. Furthermore, the Global Transport Institute is known for its comprehensive datasets, including the National Road Safety Survey (NRSS), which collected data on traffic safety from 2018 to 2020, aiding both governmental and non-governmental organizations in creating effective road safety campaigns.", "output": {"entities": {"named_data": ["Urban Mobility Assessment Report 2022", "City Infrastructure Database", "National Road Safety Survey"], "organization": ["Global Transport Institute", "Urban Planning Network", "local governments", "governmental and non-governmental organizations"], "acronym": ["Urban Mobility Assessment Report", "CID", "UPN", "NRSS"], "year": ["2022", "2019 to 2021", "2018 to 2020"], "geography": ["major cities", "urban"]}, "relations": [{"has_organization": {"head": "Urban Mobility Assessment Report 2022", "tail": "Global Transport Institute"}}, {"used_by": {"head": "City Infrastructure Database", "tail": "local governments"}}, {"has_acronym": {"head": "City Infrastructure Database", "tail": "CID"}}, {"has_timeframe": {"head": "City Infrastructure Database", "tail": "2019 to 2021"}}, {"used_by": {"head": "City Infrastructure Database", "tail": "Urban Planning Network"}}, {"has_organization": {"head": "National Road Safety Survey", "tail": "Global Transport Institute"}}, {"has_timeframe": {"head": "National Road Safety Survey", "tail": "2018 to 2020"}}, {"used_by": {"head": "National Road Safety Survey", "tail": "governmental and non-governmental organizations"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "urban infrastructure and transportation planning"}}
+{"input": "The recent analysis on forest conservation efforts highlights the importance of local engagement. According to findings from the Global Forest Assessment, there is a growing recognition of the role that indigenous communities play in sustainable land management. Similarly, the Sustainability Metrics Report provides valuable insights into the effectiveness of various conservation strategies implemented across different regions.", "output": {"entities": {"named_data": ["Global Forest Assessment", "Sustainability Metrics Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "environmental sustainability and natural resources"}}
+{"input": "In the third (Rahim Yar Khan) there is a large difference, with the census reporting that 1 percent of all school-going children attended madrassas, and the LEAPS showing that the fraction is closer to 3. 7 percent (Table II). There are three potential explanations for this difference. First, the LEAPS data is not representative of the district and could be off the mark for districts with wide variation in madrassa enrollment across rural and urban samples. Second, the experience of the last five years could have varied dramatically across districts — in some, the enrollment fractions did not change and in others it increased substantially. Third, the data could point to systematic problems with the census estimates from certain districts, or the statistical problems that arise when we try to estimate low-probability events. 3. 3 Explaining the Differences A number of reasons could account for differences between the estimates presented here and those in the popular press. 1. Differences in the sampling unit. Our estimates are all based on household surveys — an interviewer goes to a household and asks about the enrollment status of every child. Some census estimates of home rather than religious schooling in the United States — the former ranges from 1 to 2 percent (Bauman 2001) while the latter is closer to 8 percent (National Center for Education Statistics, 2001). 10 In our own analysis, we find the quality of the data generated by the Federal Bureau of Statistics in Pakistan to be consistently high. We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages.", "output": {"entities": {"named_data": ["LEAPS"], "organization": ["Federal Bureau of Statistics", "we"]}, "relations": [{"has_organization": {"head": "LEAPS", "tail": "Federal Bureau of Statistics"}}, {"used_by": {"head": "LEAPS", "tail": "we"}}]}, "_meta": {"entry_id": 765, "dataset_name": "LEAPS"}}
+{"input": "In assessing the impact of fiscal policies on domestic revenue mobilization, the latest data from the African Revenue Collection Database (ARCD) has been instrumental. This dataset, which spans 2019 to 2022, provides critical insights into revenue trends across various countries in Africa. Notably, the ARCD includes comprehensive information on tax collection systems in countries such as Nigeria and Kenya, allowing policymakers to draw relevant comparisons. While this dataset has been utilized by numerous researchers, its compilation by the Economic Data Institute has ensured a high level of credibility in the findings.", "output": {"entities": {"named_data": ["African Revenue Collection Database"], "organization": ["Economic Data Institute"], "acronym": ["ARCD"], "year": ["2019 to 2022"], "geography": ["Nigeria", "Kenya"]}, "relations": [{"has_acronym": {"head": "African Revenue Collection Database", "tail": "ARCD"}}, {"has_timeframe": {"head": "African Revenue Collection Database", "tail": "2019 to 2022"}}, {"has_geography": {"head": "African Revenue Collection Database", "tail": "Nigeria"}}, {"has_geography": {"head": "African Revenue Collection Database", "tail": "Kenya"}}, {"has_organization": {"head": "African Revenue Collection Database", "tail": "Economic Data Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "public financial management and domestic revenue"}}
+{"input": "PAGE 25 --> similar income aggregates can be obtained between POF and PNADC (Paffhausen et al., 2021), which could be exploited in further work in this direction.\n\nMyanmar Ministry of Planning and Finance & World Bank Group. (2017). Technical Poverty Estimation\nReport: Myanmar Poverty and Living Conditions Survey. World Bank, Yangon.\n\nOliveira, L. S., De Souza, D. F., Dos Santos, L. A., Antunes, M., Brendolin, N. C., & Quintaes, V. C. (2016). \"Construction of a Consumption Aggregate Based on Information from POF 2008-2009 and Its Use in the Measurement of Welfare, Poverty, Inequality and Vulnerability of Families.\" _Review of Income and_ _Wealth_, 62, 179-S210.\n\nRodrigues, C.T., & Helfand, S., & Lima, J.E. (2018). Novas linhas de pobreza para o Brasil: Uma análise a partir das Pesquisas De Orçamentos Familiares (POF) 2002-2003 e 2008-2009.", "output": {"entities": {"named_data": ["PNADC"], "organization": ["World Bank", "Myanmar Ministry of Planning and Finance"]}, "relations": [{"has_organization": {"head": "PNADC", "tail": "World Bank"}}, {"used_by": {"head": "PNADC", "tail": "Myanmar Ministry of Planning and Finance"}}]}, "_meta": {"entry_id": 437, "dataset_name": "PNADC"}}
+{"input": "The Climate Impact Assessment Report 2022 (CIAR2022) provides critical insights into the vulnerabilities faced by coastal communities in Southeast Asia. It was developed by the Environmental Research Institute to support policy-making efforts aimed at enhancing climate resilience. Similarly, the 2021 Urban Climate Data for Africa (UCDA) highlights the challenges urban areas face and serves as a vital resource for local governments, though it does not specify the organization responsible for the data collection. Additionally, the Global Disaster Risk Reduction Survey 2019 (GDRRS2019) offers comprehensive data on disaster preparedness across various regions, including Africa and South America, bolstering the knowledge base for future interventions.", "output": {"entities": {"named_data": ["Climate Impact Assessment Report 2022", "Urban Climate Data for Africa", "Global Disaster Risk Reduction Survey 2019"], "organization": ["Environmental Research Institute"], "acronym": ["CIAR2022", "UCDA", "GDRRS2019"], "year": ["2022", "2021", "2019"], "geography": ["Southeast Asia", "Africa", "South America"]}, "relations": [{"has_acronym": {"head": "Climate Impact Assessment Report 2022", "tail": "CIAR2022"}}, {"has_timeframe": {"head": "Climate Impact Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Climate Impact Assessment Report 2022", "tail": "Southeast Asia"}}, {"has_acronym": {"head": "Urban Climate Data for Africa", "tail": "UCDA"}}, {"has_timeframe": {"head": "Urban Climate Data for Africa", "tail": "2021"}}, {"has_acronym": {"head": "Global Disaster Risk Reduction Survey 2019", "tail": "GDRRS2019"}}, {"has_timeframe": {"head": "Global Disaster Risk Reduction Survey 2019", "tail": "2019"}}, {"has_geography": {"head": "Global Disaster Risk Reduction Survey 2019", "tail": "Africa"}}, {"has_geography": {"head": "Global Disaster Risk Reduction Survey 2019", "tail": "South America"}}, {"has_organization": {"head": "Climate Impact Assessment Report 2022", "tail": "Environmental Research Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "During the recent analysis on gender equality, the findings from the Women's Economic Empowerment Study and the Gender Dynamics Assessment provided critical insights into the barriers women face in the labor market. These sources highlight the disparities in employment rates and wage gaps, further emphasizing the need for targeted policies to support women's advancement in various economic sectors.", "output": {"entities": {"named_data": ["Women's Economic Empowerment Study", "Gender Dynamics Assessment"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
+{"input": "destination. Building on a growing literature documenting the relationship between subjective welfare and relative income, Fafchamps and Shilpi (2008) show that Nepalese households care about their consumption level relative to that of others in the same location. If this is the case, it is conceivable that migrants choose their destination not so much for the absolute gain in income it may provide but for the gain in relative status that would ensue. For instance, if returns to education and ability are higher in an urban setting, an educated individual may improve his relative position in society by moving from a rural to an urban setting. To investigate this possibility, we estimate equation (4) using the log of relative income (or relative consumption) as dependent variable and construct a predicted relative income measure using the same formula (5). These are shown in the second panel of Table 1. Theories of work migration predict that individuals move to increase their utility or welfare. The 1995 / 96 NLSS asked respondents a number of questions regarding their subjective satisfac- tion level with various dimensions of consumption — namely, food, clothing, housing, health care, and child schooling. They were also asked their subjective satisfaction with their level of total income.", "output": {"entities": {"named_data": ["NLSS"], "organization": ["Fafchamps and Shilpi"]}, "relations": [{"used_by": {"head": "NLSS", "tail": "Fafchamps and Shilpi"}}]}, "_meta": {"entry_id": 548, "dataset_name": "NLSS"}}
+{"input": "Recent analysis highlights significant trends in women's economic empowerment through the Women's Economic Participation Survey (WEPS), published by the International Labor Organization (ILO) in 2022. This dataset provides essential insights into female labor force participation across various regions, particularly in sub-Saharan Africa. The survey data has been extensively used by the United Nations Development Programme (UNDP) to inform policies that promote gender equality and enhance women's roles in the workforce. By focusing on geographic trends, the WEPS highlights disparities that need to be addressed to foster inclusive economic growth.", "output": {"entities": {"named_data": ["Women's Economic Participation Survey"], "organization": ["International Labor Organization", "United Nations Development Programme"], "acronym": ["WEPS"], "year": ["2022"], "geography": ["sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Women's Economic Participation Survey", "tail": "International Labor Organization"}}, {"used_by": {"head": "Women's Economic Participation Survey", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Women's Economic Participation Survey", "tail": "WEPS"}}, {"has_timeframe": {"head": "Women's Economic Participation Survey", "tail": "2022"}}, {"has_geography": {"head": "Women's Economic Participation Survey", "tail": "sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
+{"input": "as sold. Annexes 125 Table E.2: Food aid and consumption comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE UNHCR SESRE UNHCR Nonzero All Net of sold ration* Cereals/other cereals 23.2 186.1 175.5 493 12404 Wheat 60.5 133.2 116.6 1427 4710 Maize 39.6 125.5 118.3 312 4267 Rice 21.3 48.5 39.8 560 3392 Sorghum 12.7 132.8 125.7 4 5652 Pulses 7.1 18.9 17.5 248 1514 Vegetable oil/oil 3.7 9.7 8.9 627 1774 Salt 1.6 7.9 7.7 57 232 Biscuits 5.4 4.5 4.4 16 112 Dates 0.0 4.2 3.7 0 . CSB+ 19.3 15.0 14.0 36 532 Other food 565.9 - - 2558 Peas 5.7 - - 137 All cereals 78.2 - - 2994 All pulses 8.2 - - 385 Aggregate ration/month 46.7 - - Source: UNHCR and World Bank Staff based on SESRE 2023. Valuing food aid quantities with prices from SESRE suggests that if UNHCR food aid quantities were received/ reported by refugees, refugees’ food expenditures would be much more comparable to those of hosts (Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank"}}]}, "_meta": {"entry_id": 317, "dataset_name": "SESRE 2023"}}
+{"input": "**population in Poland has changed**\n\n**radically after February 24, 2022.**\nUp until 2021, Ukrainians in Poland were mostly men (close to two-thirds), who came for work-related reasons, often leaving their families back in Ukraine. The onset of the full-scale conflict in Ukraine triggered the arrival of individuals displaced by the war.\nThose were primarily women and children, with men in Ukraine being mobilized for the war effort. Social insurance data does not reflect the full extent of the change, showing only workers, without children and adults outside of employment.\n\n**Chart 3. Number of Ukrainians registered in Poland for social insurance by sex**\n\n2021 Q4 2022 Q4 2023 Q4 2024 Q2 Number of insured men with Ukrainian citizenship Number of insured women with Ukrainian citizenship Number of insured with Ukrainian citizenship Source: Deloitte own elaboration based on ZUS data.\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 attachment; or produced agricultural goods for sale or barter. A definition according to the Labour Force Survey: https://ec.europa.eu/eurostat/statistics-explained/ index.php?title=Glossary:Employed_person_-_LFS 08 Source: Deloitte own elaboration based on the PESEL database as of September 2024.", "output": {"entities": {"named_data": ["Labour Force Survey"], "organization": ["Deloitte"]}, "relations": [{"used_by": {"head": "Labour Force Survey", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1310, "dataset_name": "Labour Force Survey"}}
+{"input": "The 2020 Labor Market Insights Report (LMIR) provides comprehensive data on employment trends across various sectors in South Africa. Produced by the National Employment Agency, this dataset covers the period from 2015 to 2020, offering valuable context for policymakers and researchers alike. Additionally, the Skills Development Assessment 2022 (SDA 2022) highlights the skills gap in the labor force and is being utilized by several educational institutions to tailor their training programs. Furthermore, the Youth Employment Survey (YES) conducted in 2021 focuses specifically on the challenges faced by young job seekers in urban areas, although it does not include data from rural regions.", "output": {"entities": {"named_data": ["Labor Market Insights Report", "Skills Development Assessment 2022", "Youth Employment Survey"], "organization": ["National Employment Agency", "educational institutions"], "acronym": ["LMIR", "SDA 2022", "YES"], "year": ["2020", "2015 to 2020", "2022", "2021"], "geography": ["South Africa", "urban areas", "rural regions"]}, "relations": [{"has_acronym": {"head": "Labor Market Insights Report", "tail": "LMIR"}}, {"has_timeframe": {"head": "Labor Market Insights Report", "tail": "2015 to 2020"}}, {"has_geography": {"head": "Labor Market Insights Report", "tail": "South Africa"}}, {"has_acronym": {"head": "Skills Development Assessment 2022", "tail": "SDA 2022"}}, {"has_timeframe": {"head": "Skills Development Assessment 2022", "tail": "2022"}}, {"used_by": {"head": "Skills Development Assessment 2022", "tail": "educational institutions"}}, {"has_acronym": {"head": "Youth Employment Survey", "tail": "YES"}}, {"has_timeframe": {"head": "Youth Employment Survey", "tail": "2021"}}, {"has_geography": {"head": "Youth Employment Survey", "tail": "urban areas"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "employment, labor markets, and skills development"}}
+{"input": "# Appendix. Model Calibration\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 the Polish economy was felt as combined four different shocks: to population, labour supply, average propensity to save, and productivity, with shocks to population and labour supply being balanced by equivalent shocks in Eastern Europe [53] .\nMoreover, as part of assumed increase in spending by Ukrainians in Poland was financed by savings from Ukraine this was balanced by equivalent negative shock on investment in Eastern Europe.\n\nShock to population was calibrated to match data for residents of Poland from Statistics Poland and number of refugees based on PESEL UKR. Equivalent shock in Eastern Europe was calculated using data for population in this region from World Population Prospects UN.", "output": {"entities": {"named_data": ["Statistics Poland"], "organization": ["Deloitte Economic Institute"]}, "relations": [{"used_by": {"head": "Statistics Poland", "tail": "Deloitte Economic Institute"}}]}, "_meta": {"entry_id": 1308, "dataset_name": "Statistics Poland"}}
+{"input": "In 2020, the Social Protection Assessment Report provided crucial insights into the effectiveness of various safety net programs across developing countries. This comprehensive analysis, published by the World Bank, highlighted significant disparities in access and coverage, emphasizing the need for tailored interventions to enhance the social safety net framework.", "output": {"entities": {"named_data": ["Social Protection Assessment Report"], "organization": ["World Bank"], "acronym": [], "year": ["2020"], "geography": []}, "relations": [{"has_organization": {"head": "Social Protection Assessment Report", "tail": "World Bank"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "social protection and safety nets"}}
+{"input": "The Renewable Energy Access Database (READ) offers critical insights into the energy situation across various regions, covering the years 2019–2023. This database, managed by the Global Energy Institute, highlights the status of renewable energy installations in countries like Nigeria and Brazil. Notably, the 2021 Energy Transition Assessment Report provides a comprehensive evaluation of renewable energy strategies implemented in Southeast Asia. Furthermore, researchers from the Institute for Sustainable Solutions have utilized the READ database to inform policy decisions in the region. Together, these datasets underline the significant advancements and ongoing challenges in achieving universal energy access.", "output": {"entities": {"named_data": ["Renewable Energy Access Database", "Energy Transition Assessment Report"], "organization": ["Global Energy Institute", "Institute for Sustainable Solutions"], "acronym": ["READ"], "year": ["2019–2023", "2021"], "geography": ["Nigeria", "Brazil", "Southeast Asia"]}, "relations": [{"has_acronym": {"head": "Renewable Energy Access Database", "tail": "READ"}}, {"has_timeframe": {"head": "Renewable Energy Access Database", "tail": "2019–2023"}}, {"has_timeframe": {"head": "Energy Transition Assessment Report", "tail": "2021"}}, {"has_geography": {"head": "Renewable Energy Access Database", "tail": "Nigeria"}}, {"has_geography": {"head": "Renewable Energy Access Database", "tail": "Brazil"}}, {"has_geography": {"head": "Energy Transition Assessment Report", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Renewable Energy Access Database", "tail": "Global Energy Institute"}}, {"used_by": {"head": "Renewable Energy Access Database", "tail": "Institute for Sustainable Solutions"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "energy access and renewable transitions"}}
+{"input": "In  recent years enrollment rates have increased sharply and are higher for girls than boys in Bangladesh's urban areas, according to UNICEF.\n\nFemale children are more likely to be enrolled than male children in primary  schools, a result in line with recent UNICEF findings.\n\nThe data for the Netherlands is taken from the Dutch National Institute for Public Health and Environment (RIVM). [2] The data for Germany is from the Robert Koch Institute. [3] The data for Italy can be viewed via a live dashboard, [4] and the raw data is well organized and available on a github page. [5] The Spanish data was taken from this link. [6]\n\nThe COVID-19 data is taken from the RIVM. [8] The first data snapshot includes all confirmed\ncases as of March 22 (a total of 4,004 with known residence out of 4,157 confirmed cases).", "output": {"entities": {"named_data": ["Dutch National Institute for Public Health and Environment (RIVM)"], "organization": ["UNICEF"]}, "relations": [{"used_by": {"head": "Dutch National Institute for Public Health and Environment (RIVM)", "tail": "UNICEF"}}]}, "_meta": {"entry_id": 538, "dataset_name": "Dutch National Institute for Public Health and Environment (RIVM)"}}
+{"input": "The latest findings from the Poverty Headcount Survey 2022 indicate significant shifts in poverty levels across the region. This survey, conducted by the National Statistical Institute, provides crucial insights into inequality measurement that can inform policy decisions going forward.", "output": {"entities": {"named_data": ["Poverty Headcount Survey 2022"], "organization": ["National Statistical Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Poverty Headcount Survey 2022", "tail": "National Statistical Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "poverty headcount and inequality measurement"}}
+{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR Statistical Online Population Database", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 201, "dataset_name": "UNHCR Statistical Online Population Database"}}
+{"input": "The Digital Technology Adoption Survey (DTAS) conducted in 2022 provides valuable insights into how various sectors in Ghana have adopted new technologies. This dataset, published by the Ministry of Communication and Digitalisation, offers a comprehensive overview of trends and barriers to technology use across urban and rural areas. Additionally, the survey data covers a timeframe from 2020 to 2022, highlighting the rapid evolution of digital tools during the ongoing global transition to a more technology-driven economy. Moreover, the Ghana Digital Development Assessment report (GDDA 2023) further elaborates on these findings, emphasizing the geographic disparities in technology uptake within the country.", "output": {"entities": {"named_data": ["Digital Technology Adoption Survey", "Ghana Digital Development Assessment report"], "organization": ["Ministry of Communication and Digitalisation"], "acronym": ["DTAS", "GDDA"], "year": ["2022", "2020 to 2022", "2023"], "geography": ["Ghana"]}, "relations": [{"has_acronym": {"head": "Digital Technology Adoption Survey", "tail": "DTAS"}}, {"has_timeframe": {"head": "Digital Technology Adoption Survey", "tail": "2022"}}, {"has_timeframe": {"head": "Digital Technology Adoption Survey", "tail": "2020 to 2022"}}, {"has_geography": {"head": "Digital Technology Adoption Survey", "tail": "Ghana"}}, {"has_acronym": {"head": "Ghana Digital Development Assessment report", "tail": "GDDA"}}, {"has_timeframe": {"head": "Ghana Digital Development Assessment report", "tail": "2023"}}, {"has_geography": {"head": "Ghana Digital Development Assessment report", "tail": "Ghana"}}, {"has_organization": {"head": "Digital Technology Adoption Survey", "tail": "Ministry of Communication and Digitalisation"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "digital development and technology adoption"}}
+{"input": "The Fragility and Violence Assessment Report 2022 provides critical insights into the factors contributing to instability in various regions. Produced by the Conflict Research Institute, this report analyzes data trends from conflict-affected countries, helping policymakers design effective interventions.", "output": {"entities": {"named_data": ["Fragility and Violence Assessment Report 2022"], "organization": ["Conflict Research Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Fragility and Violence Assessment Report 2022", "tail": "Conflict Research Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "conflict, fragility, and violence"}}
+{"input": "The daily precipitation data used are from the 3- hourly data set from the Tropical Rainfall Measurement Mission Project (TRMM), which is aggregated up to daily data.\n\n\\n<!-- PAGE 28 -->\\ndata set product 3B42RT 3 hour product gives the best results in a basket of 8 near real-time rainfall products. The 3B42RT daily derived product is what is used in this paper.\n\n\\n<!-- PAGE 8 -->\\nthrough 2015. During the period from 1985 to 2016, the Dartmouth Flood Observatory (DFO) registered 3,808 floods of magnitude 4 or more and 1,175 floods of magnitude 6 and up. [3]\n\nAccording to the Indonesian National Disaster Management Authority (BNPB), there were more than 19,000 natural hazards in the period 2001 - 2015 (National Disaster Management Agency 2016), making Indonesia a useful country for any natural hazard analysis.\n\nAccording to the Global Facility for Disaster Reduction and Recovery (GFDRR), the Philippines is at high risk from several types of natural hazards (GFDRR 2019). Prime among them are cyclones, where an average of 20 make landfall every year. In 2013, typhoon Yolanda led to 6,000 casualties and damaged more than 1.1 million houses. The Philippines are also exposed to earthquake and flood risks.", "output": {"entities": {"named_data": ["Tropical Rainfall Measurement Mission Project (TRMM)"], "organization": ["this paper"]}, "relations": [{"used_by": {"head": "Tropical Rainfall Measurement Mission Project (TRMM)", "tail": "this paper"}}]}, "_meta": {"entry_id": 584, "dataset_name": "Tropical Rainfall Measurement Mission Project (TRMM)"}}
+{"input": "The East Africa Skills Development Survey (EASDS) conducted in 2022 examined labor market trends across the region. This dataset, produced by the East African Community (EAC), provides critical insights into skills gaps and employment opportunities in Kenya and Tanzania. Additionally, the 2023 Labor Market Assessment Report (LMAR) offers a comprehensive overview of workforce dynamics in Uganda, highlighting the impacts of recent policy changes on employment rates. The findings from these datasets are essential for stakeholders, including government agencies and NGOs, working to improve employment outcomes in these countries.", "output": {"entities": {"named_data": ["East Africa Skills Development Survey", "Labor Market Assessment Report"], "organization": ["East African Community", "government agencies", "NGOs"], "acronym": ["EASDS", "LMAR"], "year": ["2022", "2023"], "geography": ["Kenya", "Tanzania", "Uganda"]}, "relations": [{"has_acronym": {"head": "East Africa Skills Development Survey", "tail": "EASDS"}}, {"has_timeframe": {"head": "East Africa Skills Development Survey", "tail": "2022"}}, {"has_geography": {"head": "East Africa Skills Development Survey", "tail": "Kenya"}}, {"has_geography": {"head": "East Africa Skills Development Survey", "tail": "Tanzania"}}, {"has_acronym": {"head": "Labor Market Assessment Report", "tail": "LMAR"}}, {"has_timeframe": {"head": "Labor Market Assessment Report", "tail": "2023"}}, {"has_geography": {"head": "Labor Market Assessment Report", "tail": "Uganda"}}, {"has_organization": {"head": "East Africa Skills Development Survey", "tail": "East African Community"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "employment, labor markets, and skills development"}}
+{"input": "The Water and Sanitation Monitoring Database, published by the Global Health Organization, provides crucial insights into access to clean water and sanitation facilities across developing countries. This database is essential for policymakers aiming to improve public health outcomes in these regions.", "output": {"entities": {"named_data": ["Water and Sanitation Monitoring Database"], "organization": ["Global Health Organization"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Water and Sanitation Monitoring Database", "tail": "Global Health Organization"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "water, sanitation, and hygiene"}}
+{"input": "The recent analysis on flood resilience in South Asia draws on multiple datasets. The South Asia Flood Assessment Report (SAFAR) published by the Asian Development Bank (ADB) provides critical insights on the impact of floods in the region. Additionally, the 2022 Climate Vulnerability Index (CVI) for Bangladesh was utilized by the United Nations Development Programme (UNDP) to evaluate the adaptation needs of at-risk communities. Meanwhile, the Regional Disaster Risk Database (RDRD) compiled by the World Bank serves as a key resource for policymakers, covering data from 2015 to 2021. Finally, the Disaster Resilience Monitoring System (DRMS) has been instrumental for local governments in Sri Lanka, allowing them to make informed decisions based on historical data from 2019.", "output": {"entities": {"named_data": ["South Asia Flood Assessment Report", "Climate Vulnerability Index", "Regional Disaster Risk Database", "Disaster Resilience Monitoring System"], "organization": ["Asian Development Bank", "United Nations Development Programme", "World Bank"], "acronym": ["SAFAR", "CVI", "RDRD", "DRMS"], "year": ["2022", "2015 to 2021", "2019"], "geography": ["South Asia", "Bangladesh", "Sri Lanka"]}, "relations": [{"has_organization": {"head": "South Asia Flood Assessment Report", "tail": "Asian Development Bank"}}, {"used_by": {"head": "South Asia Flood Assessment Report", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "South Asia Flood Assessment Report", "tail": "SAFAR"}}, {"has_timeframe": {"head": "Regional Disaster Risk Database", "tail": "2015 to 2021"}}, {"has_organization": {"head": "Regional Disaster Risk Database", "tail": "World Bank"}}, {"used_by": {"head": "Disaster Resilience Monitoring System", "tail": "local governments"}}, {"has_acronym": {"head": "Disaster Resilience Monitoring System", "tail": "DRMS"}}, {"has_geography": {"head": "Climate Vulnerability Index", "tail": "Bangladesh"}}, {"has_geography": {"head": "Disaster Resilience Monitoring System", "tail": "Sri Lanka"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset.", "output": {"entities": {"named_data": ["ACLED dataset"], "organization": ["ACLED"]}, "relations": [{"has_organization": {"head": "ACLED dataset", "tail": "ACLED"}}]}, "_meta": {"entry_id": 741, "dataset_name": "ACLED dataset"}}
+{"input": "The 2022 National Social Protection Survey (NSPS) conducted by the Ministry of Social Affairs provides crucial insights into the welfare programs in Ghana. Data from this survey has been instrumental for the United Nations Development Programme (UNDP) in evaluating the effectiveness of social safety nets in the region. Additionally, the West African Economic and Monetary Union (WAEMU) produced the 2021 Regional Social Assistance Database, which includes comprehensive information on assistance programs across member states. This dataset is frequently cited by various NGOs working in West Africa to inform policy recommendations and enhance support for vulnerable populations.", "output": {"entities": {"named_data": ["National Social Protection Survey", "Regional Social Assistance Database"], "organization": ["Ministry of Social Affairs", "United Nations Development Programme", "West African Economic and Monetary Union", "NGOs"], "acronym": ["NSPS", "WAEMU"], "year": ["2022", "2021"], "geography": ["Ghana", "West Africa"]}, "relations": [{"has_organization": {"head": "National Social Protection Survey", "tail": "Ministry of Social Affairs"}}, {"used_by": {"head": "National Social Protection Survey", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "National Social Protection Survey", "tail": "NSPS"}}, {"has_organization": {"head": "Regional Social Assistance Database", "tail": "West African Economic and Monetary Union"}}, {"used_by": {"head": "Regional Social Assistance Database", "tail": "NGOs"}}, {"has_acronym": {"head": "Regional Social Assistance Database", "tail": "WAEMU"}}, {"has_timeframe": {"head": "Regional Social Assistance Database", "tail": "2021"}}, {"has_timeframe": {"head": "National Social Protection Survey", "tail": "2022"}}, {"has_geography": {"head": "National Social Protection Survey", "tail": "Ghana"}}, {"has_geography": {"head": "Regional Social Assistance Database", "tail": "West Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "social protection and safety nets"}}
+{"input": "Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research. 1989-2019. 50 (4). Taylor, J., M. Filipski, and M. Alloush (2016). Economic impact of refugees. Proceedings of the National Academy of Sciences 113 (27), 7449 – 53. United Nations High Commissioner for Refugees (2020). Global Trends: Forced Displacement in 2019. Geneva. Verme, P. and K. Schuettler (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. Journal of Development Economics 102606. Verwimp, P. and J. Maystadt (2015, December). Forced Displacement and Refugees in Sub-Saharan Africa. An Economic Inquiry. World Bank Policy Research Working paper 7517. Vogt, Manuel, N.- C. B. S. R. L.- E. C. P. H. and L. Girardin (2015). Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Data Set Family. 51", "output": {"entities": {"named_data": ["UCDP Georeferenced Event Dataset"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "UCDP Georeferenced Event Dataset", "tail": "World Bank"}}]}, "_meta": {"entry_id": 6, "dataset_name": "UCDP Georeferenced Event Dataset"}}
+{"input": "The Gender Equality Assessment Report 2022 conducted by the United Nations Entity for Gender Equality and the Empowerment of Women (UN Women) provides critical insights into economic opportunities for women across various sectors. The findings from this report are utilized by the World Bank to inform their Gender Data Portal initiatives aimed at enhancing women's access to resources. Additionally, the Women’s Economic Empowerment Survey (WEES) published by the International Labour Organization (ILO) in 2021 focuses on labor market participation and is leveraged by numerous non-governmental organizations for project planning. Through these datasets, stakeholders can better understand the challenges faced by women globally and devise appropriate interventions.", "output": {"entities": {"named_data": ["Gender Equality Assessment Report 2022", "Gender Data Portal", "Women’s Economic Empowerment Survey"], "organization": ["United Nations Entity for Gender Equality and the Empowerment of Women", "World Bank", "International Labour Organization"], "acronym": ["UN Women", "WEES"], "year": ["2022", "2021"], "geography": []}, "relations": [{"has_organization": {"head": "Gender Equality Assessment Report 2022", "tail": "United Nations Entity for Gender Equality and the Empowerment of Women"}}, {"used_by": {"head": "Gender Equality Assessment Report 2022", "tail": "World Bank"}}, {"has_acronym": {"head": "United Nations Entity for Gender Equality and the Empowerment of Women", "tail": "UN Women"}}, {"has_timeframe": {"head": "Gender Equality Assessment Report 2022", "tail": "2022"}}, {"has_organization": {"head": "Women’s Economic Empowerment Survey", "tail": "International Labour Organization"}}, {"has_timeframe": {"head": "Women’s Economic Empowerment Survey", "tail": "2021"}}, {"used_by": {"head": "Women’s Economic Empowerment Survey", "tail": "numerous non-governmental organizations"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "gender equality and women's economic empowerment"}}
+{"input": "Recent analyses indicate significant progress in environmental sustainability initiatives across various regions. The Global Green Index (GGI), published by the International Environmental Agency (IEA), provides comprehensive data on the commitment levels of countries towards renewable energy from 2019 to 2022. This dataset has been extensively used by the Global Sustainability Council to benchmark national policies. Additionally, the Biodiversity Assessment Report 2020, produced by the World Wildlife Federation (WWF), sheds light on species conservation efforts in Southeast Asia, particularly in Indonesia. The WWF’s report has been cited in numerous studies conducted by the Environmental Research Network, emphasizing the importance of biodiversity in sustainable development.", "output": {"entities": {"named_data": ["Global Green Index", "Biodiversity Assessment Report 2020"], "organization": ["International Environmental Agency", "Global Sustainability Council", "World Wildlife Federation", "Environmental Research Network"], "acronym": ["GGI"], "year": ["2019 to 2022", "2020"], "geography": ["Southeast Asia", "Indonesia"]}, "relations": [{"has_organization": {"head": "Global Green Index", "tail": "International Environmental Agency"}}, {"used_by": {"head": "Global Green Index", "tail": "Global Sustainability Council"}}, {"has_acronym": {"head": "Global Green Index", "tail": "GGI"}}, {"has_timeframe": {"head": "Global Green Index", "tail": "2019 to 2022"}}, {"has_organization": {"head": "Biodiversity Assessment Report 2020", "tail": "World Wildlife Federation"}}, {"has_geography": {"head": "Biodiversity Assessment Report 2020", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Biodiversity Assessment Report 2020", "tail": "Indonesia"}}, {"used_by": {"head": "Biodiversity Assessment Report 2020", "tail": "Environmental Research Network"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "environmental sustainability and natural resources"}}
+{"input": "The Global Financial Inclusion Survey (GFIS) conducted by the Economic Research Institute in 2022 provides critical insights into the accessibility of banking services across various regions. This dataset highlights trends in financial behaviors within the South Asian region, capturing data from several countries including India, Pakistan, and Bangladesh. Furthermore, the 2021 Macro-Economic Trends Report (METR) by the International Monetary Fund (IMF) analyzes the economic indicators that inform policy decisions, encompassing data from 2015 to 2021. Both datasets are pivotal for understanding the evolving landscape of financial inclusion and economic stability.", "output": {"entities": {"named_data": ["Global Financial Inclusion Survey", "2021 Macro-Economic Trends Report"], "organization": ["Economic Research Institute", "International Monetary Fund"], "acronym": ["GFIS", "METR"], "year": ["2022", "2015 to 2021"], "geography": ["South Asia", "India", "Pakistan", "Bangladesh"]}, "relations": [{"has_acronym": {"head": "Global Financial Inclusion Survey", "tail": "GFIS"}}, {"has_timeframe": {"head": "Global Financial Inclusion Survey", "tail": "2022"}}, {"has_geography": {"head": "Global Financial Inclusion Survey", "tail": "South Asia"}}, {"has_geography": {"head": "Global Financial Inclusion Survey", "tail": "India"}}, {"has_geography": {"head": "Global Financial Inclusion Survey", "tail": "Pakistan"}}, {"has_geography": {"head": "Global Financial Inclusion Survey", "tail": "Bangladesh"}}, {"has_acronym": {"head": "2021 Macro-Economic Trends Report", "tail": "METR"}}, {"has_timeframe": {"head": "2021 Macro-Economic Trends Report", "tail": "2015 to 2021"}}, {"has_organization": {"head": "2021 Macro-Economic Trends Report", "tail": "International Monetary Fund"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "3 population density across a state, the ecological inference issue is alleviated as we directly test the propensity of any population group to experience a conflict. Through disaggregation, we may succeed in supporting explanations based on variables such as the distance from the capital and the overall size of the country's population if we know at which locations conflicts occur. If conflicts are located mainly at some distance from countries'capitals, we might infer that large countries have more conflicts because of the difficulties of projecting governmental power. If they are located in population concentrations irrespective of location relative to the capita, other explanations should be sought. The paper makes use a new dataset called ACLED (Armed Conflict Location and Events Dataset) to allow for this type of disaggregated analysis. The dataset currently codes the location of all reported conflict events in 14 countries in Central Africa in the 1960 – 2004 period. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper suggests some adaptions to a statistical method to allow for analyzing data at this level of analysis. Related to the size of populations is their distributions. The Democratic Republic of Congo, for instance, is not only characterized by being enormously large, but also shows tremendous variation in population densities.", "output": {"entities": {"named_data": ["Armed Conflict Location and Events Dataset"], "organization": ["the paper"]}, "relations": [{"used_by": {"head": "Armed Conflict Location and Events Dataset", "tail": "the paper"}}]}, "_meta": {"entry_id": 918, "dataset_name": "Armed Conflict Location and Events Dataset"}}
+{"input": "The Energy Access and Renewable Transition Survey (EARTS) conducted by the International Energy Agency (IEA) in 2022 provides critical insights into the status of energy access in low-income countries. This dataset is widely cited by the United Nations Development Programme (UNDP) in its annual reports on sustainable development goals. In addition, the Renewable Energy Development Assessment (REDA) from 2021, published by the Global Renewable Energy Institute (GREI), focuses on renewable energy initiatives across Sub-Saharan Africa. The findings from REDA have been instrumental for the African Union (AU) in shaping policies aimed at enhancing energy sustainability in the region.", "output": {"entities": {"named_data": ["Energy Access and Renewable Transition Survey", "Renewable Energy Development Assessment"], "organization": ["International Energy Agency", "United Nations Development Programme", "Global Renewable Energy Institute", "African Union"], "acronym": ["EARTS", "REDA"], "year": ["2022", "2021"], "geography": ["low-income countries", "Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Energy Access and Renewable Transition Survey", "tail": "International Energy Agency"}}, {"used_by": {"head": "Energy Access and Renewable Transition Survey", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Energy Access and Renewable Transition Survey", "tail": "EARTS"}}, {"has_timeframe": {"head": "Energy Access and Renewable Transition Survey", "tail": "2022"}}, {"has_geography": {"head": "Energy Access and Renewable Transition Survey", "tail": "low-income countries"}}, {"has_organization": {"head": "Renewable Energy Development Assessment", "tail": "Global Renewable Energy Institute"}}, {"used_by": {"head": "Renewable Energy Development Assessment", "tail": "African Union"}}, {"has_acronym": {"head": "Renewable Energy Development Assessment", "tail": "REDA"}}, {"has_timeframe": {"head": "Renewable Energy Development Assessment", "tail": "2021"}}, {"has_geography": {"head": "Renewable Energy Development Assessment", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "energy access and renewable transitions"}}
+{"input": "5 narrative around the regressions and aims to explain why not more people migrate when benefits of doing so are so high. 2. The Setting: Tanzania and Kagera, 1994-2004 In the last decade, Tanzania has experienced a period of relatively rapid growth, attributed to liberalization, a renewed trade orientation, a stable political context, and a relatively positive business climate to boost economic performance. Real GDP growth was of the order of 4. 2 % per year between 1994 and 2004, while annual population growth was around 3. 2 % in the same period (URT, 2004). There is also evidence that growth had accelerated in the last few years compared to the 1990s. However, this growth has not been sufficiently broad-based to result in rapid poverty reduction. On the basis of the available evidence, poverty rates have declined only slightly and most of the poverty reduction progress has been made in urban areas. According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania. The decline in poverty was steep in Dar es Salaam (from 28 % to 18 %) but minimal in rural Tanzania (from 41 % to 39 %).", "output": {"entities": {"named_data": ["Household Budget Survey"], "organization": ["URT"]}, "relations": [{"used_by": {"head": "Household Budget Survey", "tail": "URT"}}]}, "_meta": {"entry_id": 130, "dataset_name": "Household Budget Survey"}}
+{"input": "The Public Revenue Assessment 2022 (PRA 2022) highlights significant trends in domestic revenue generation across several countries. This report, produced by the International Fiscal Institute (IFI), provides comprehensive insights into tax compliance and revenue collection efficiencies from 2017 to 2022. Additionally, the African Revenue Collection Database (ARCD) covers a broader period from 2015 to 2021, focusing specifically on revenue mobilization strategies in East African nations. Stakeholders, including various governmental organizations, are increasingly relying on these datasets to inform policy decisions and enhance fiscal management practices.", "output": {"entities": {"named_data": ["Public Revenue Assessment 2022", "African Revenue Collection Database"], "organization": ["International Fiscal Institute"], "acronym": ["PRA 2022", "ARCD"], "year": ["2022", "2017 to 2022", "2015 to 2021"], "geography": ["East African nations"]}, "relations": [{"has_acronym": {"head": "Public Revenue Assessment 2022", "tail": "PRA 2022"}}, {"has_timeframe": {"head": "Public Revenue Assessment 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Public Revenue Assessment 2022", "tail": "2017 to 2022"}}, {"has_acronym": {"head": "African Revenue Collection Database", "tail": "ARCD"}}, {"has_timeframe": {"head": "African Revenue Collection Database", "tail": "2015 to 2021"}}, {"has_geography": {"head": "African Revenue Collection Database", "tail": "East African nations"}}, {"has_organization": {"head": "Public Revenue Assessment 2022", "tail": "International Fiscal Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "public financial management and domestic revenue"}}
+{"input": "The Conflict and Fragility Assessment Report 2022, published by the International Crisis Group, offers an in-depth analysis of the ongoing challenges faced by nations in conflict zones. This report provides valuable insights into the socio-economic impacts of violence and the necessary interventions required to rebuild these fragile states. By analyzing data from affected regions, the report aims to guide policymakers and organizations in crafting effective strategies for peacebuilding.", "output": {"entities": {"named_data": ["Conflict and Fragility Assessment Report 2022"], "organization": ["International Crisis Group"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Conflict and Fragility Assessment Report 2022", "tail": "International Crisis Group"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "conflict, fragility, and violence"}}
+{"input": "The recent analysis of the Global Refugee Statistics Report 2022, published by the International Refugee Agency (IRA), highlighted significant trends in forced displacement patterns across various regions. This dataset, used extensively by the United Nations High Commissioner for Refugees (UNHCR), provides comprehensive insights into the demographics and circumstances of displaced persons. Furthermore, the Mobility and Migration Assessment (MMA) data from 2021, produced by the Migration Research Institute (MRI), sheds light on the economic impacts of migration. Organizations such as the World Economic Forum (WEF) have utilized this assessment to inform policy discussions on migration strategies in Europe and beyond. These datasets are invaluable for understanding the evolving dynamics of displacement and migration in today's global landscape.", "output": {"entities": {"named_data": ["Global Refugee Statistics Report 2022", "Mobility and Migration Assessment"], "organization": ["International Refugee Agency", "United Nations High Commissioner for Refugees", "Migration Research Institute", "World Economic Forum"], "acronym": ["IRA", "UNHCR", "MMA", "MRI", "WEF"], "year": ["2022", "2021"], "geography": ["Europe"]}, "relations": [{"has_organization": {"head": "Global Refugee Statistics Report 2022", "tail": "International Refugee Agency"}}, {"used_by": {"head": "Global Refugee Statistics Report 2022", "tail": "United Nations High Commissioner for Refugees"}}, {"has_organization": {"head": "Mobility and Migration Assessment", "tail": "Migration Research Institute"}}, {"used_by": {"head": "Mobility and Migration Assessment", "tail": "World Economic Forum"}}, {"has_timeframe": {"head": "Global Refugee Statistics Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Mobility and Migration Assessment", "tail": "2021"}}, {"has_geography": {"head": "Mobility and Migration Assessment", "tail": "Europe"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "forced displacement, refugees, and migration"}}
+{"input": "The recent analysis of maternal health outcomes highlights critical trends in health systems across various regions. The findings draw extensively on the National Maternal Health Survey, which provides comprehensive insights into the factors affecting maternal care and health service delivery. Such data is vital for understanding disparities and improving policy frameworks aimed at enhancing maternal health outcomes.", "output": {"entities": {"named_data": ["National Maternal Health Survey"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "health systems and maternal outcomes"}}
+{"input": "evidence is consistent with studies that used cross-section surveys, panel data and cross-country\n\nSome of the work with panel data has also gone further in an effort to establish a causality link\n\nOswald (2007) use information on lottery winnings in the British Household Panel Survey", "output": {"entities": {"named_data": ["British Household Panel Survey"], "organization": ["Oswald"]}, "relations": [{"used_by": {"head": "British Household Panel Survey", "tail": "Oswald"}}]}, "_meta": {"entry_id": 1103, "dataset_name": "British Household Panel Survey"}}
+{"input": "Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey.", "output": {"entities": {"named_data": ["Housing Census"], "organization": ["2009 Population and Housing Census", "Listening to Displaced People Survey"]}, "relations": [{"has_organization": {"head": "Housing Census", "tail": "2009 Population and Housing Census"}}, {"used_by": {"head": "Housing Census", "tail": "Listening to Displaced People Survey"}}]}, "_meta": {"entry_id": 539, "dataset_name": "Housing Census"}}
+{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer.", "output": {"entities": {"named_data": ["EPR-ER dataset"], "organization": ["we"]}, "relations": [{"used_by": {"head": "EPR-ER dataset", "tail": "we"}}]}, "_meta": {"entry_id": 509, "dataset_name": "EPR-ER dataset"}}
+{"input": "rents to GDP from WDI), we can implicitly compute 𝛾.\n\nSource:_\n_Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._\n\n\nFigure 2 plots the sources of growth for the Sub-Saharan Africa region as well as the different groups according\n\nSource: Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._\n\nFrankel, J. A., & Romer, D. H. (1999). \"Does trade cause growth?\" American Economic Review 89(3): 379-399.\n\nreported in the Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015). [15]", "output": {"entities": {"named_data": ["Penn World Tables"], "organization": ["Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)", "Frankel, J. A., & Romer, D. H."]}, "relations": [{"has_organization": {"head": "Penn World Tables", "tail": "Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)"}}, {"used_by": {"head": "Penn World Tables", "tail": "Frankel, J. A., & Romer, D. H."}}]}, "_meta": {"entry_id": 469, "dataset_name": "Penn World Tables"}}
+{"input": "10 Finally, we divide the communities based on land scarcity before the 1993 conflict in order to explore the possible role of posterior rules regarding land provision to returnees. Respondents in communities that had more and less pre-war land available have broadly similar attitudes towards return. 4. Research design 4. 1 The survey We collected the data for this project during January to March 2015 as part of a nationwide survey on issues related to migration for the Labour Market Impacts of Forced Migration (LAMFOR) project. The survey had two components. First, a household survey in which 15 households were interviewed in 100 communities (i. e. sous-collines) across the 17 provinces of the country. Second, a community survey in which a local leader was interviewed in each of the 100 communities. The number of communities selected in each province was based on information from the 2008 Census. Figure 4 indicates the location of the communities surveyed. Figure 4 – Location of communities surveyed in Burundi Note: Geolocation of the 100 communities (i. e. sous-collines) sampled in the survey. Each community corresponds to a dot. Fifteen households and a local leader were interviewed in each community. The number of communities selected in each province was based on information from the 2008 Census. In the analysis below we focus on rural areas.", "output": {"entities": {"named_data": ["2008 Census"], "organization": ["LAMFOR"]}, "relations": [{"used_by": {"head": "2008 Census", "tail": "LAMFOR"}}]}, "_meta": {"entry_id": 1040, "dataset_name": "2008 Census"}}
+{"input": "The 2020 Social Protection Assessment Report, published by the International Social Welfare Organization (ISWO), served as a critical resource for the development of new programs in various countries. Following this, the Eastern European Safety Nets Database (EESND), which compiles extensive data from the region's social safety nets, was utilized by local governments for program evaluation in 2021. Meanwhile, the Comprehensive Child Welfare Survey (CCWS) contributed valuable insights into child poverty levels and was extensively cited by UNICEF across various initiatives from 2019 to 2022. Additionally, the Global Labor Market Trends Report, released by the World Economic Research Institute (WERI), provided essential analyses that informed labor policy reforms in several nations, including Ukraine and Poland, within the timeframe of 2021 to 2023.", "output": {"entities": {"named_data": ["Social Protection Assessment Report", "Eastern European Safety Nets Database", "Comprehensive Child Welfare Survey", "Global Labor Market Trends Report"], "organization": ["International Social Welfare Organization", "UNICEF", "World Economic Research Institute"], "acronym": ["ISWO", "EESND", "CCWS", "WERI"], "year": ["2020", "2021", "2019 to 2022", "2021 to 2023"], "geography": ["Ukraine", "Poland"]}, "relations": [{"has_organization": {"head": "Social Protection Assessment Report", "tail": "International Social Welfare Organization"}}, {"used_by": {"head": "Social Protection Assessment Report", "tail": "local governments"}}, {"has_acronym": {"head": "Social Protection Assessment Report", "tail": "ISWO"}}, {"has_timeframe": {"head": "Social Protection Assessment Report", "tail": "2020"}}, {"has_organization": {"head": "Eastern European Safety Nets Database", "tail": "local governments"}}, {"used_by": {"head": "Eastern European Safety Nets Database", "tail": "local governments"}}, {"has_acronym": {"head": "Eastern European Safety Nets Database", "tail": "EESND"}}, {"has_organization": {"head": "Comprehensive Child Welfare Survey", "tail": "UNICEF"}}, {"used_by": {"head": "Comprehensive Child Welfare Survey", "tail": "UNICEF"}}, {"has_acronym": {"head": "Comprehensive Child Welfare Survey", "tail": "CCWS"}}, {"has_timeframe": {"head": "Comprehensive Child Welfare Survey", "tail": "2019 to 2022"}}, {"has_organization": {"head": "Global Labor Market Trends Report", "tail": "World Economic Research Institute"}}, {"used_by": {"head": "Global Labor Market Trends Report", "tail": "labor policy reforms"}}, {"has_acronym": {"head": "Global Labor Market Trends Report", "tail": "WERI"}}, {"has_timeframe": {"head": "Global Labor Market Trends Report", "tail": "2021 to 2023"}}, {"has_geography": {"head": "Global Labor Market Trends Report", "tail": "Ukraine"}}, {"has_geography": {"head": "Global Labor Market Trends Report", "tail": "Poland"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "social protection and safety nets"}}
+{"input": "The recent Labor Market Dynamics Report 2022, published by the International Labor Organization (ILO), provides valuable insights into employment trends across various sectors in Eastern Europe. Utilizing data from this report, the European Commission conducted a comprehensive analysis to enhance its workforce development strategies. Additionally, the Skills Assessment Survey (SAS) 2021, created by the World Bank, focuses on the skills gap in developing countries, particularly in Southeast Asia. This survey has been instrumental for local governments, such as the Ministry of Labor in Indonesia, which has referenced the SAS data to inform policy changes aimed at boosting employment opportunities for youth.", "output": {"entities": {"named_data": ["Labor Market Dynamics Report 2022", "Skills Assessment Survey", "SAS"], "organization": ["International Labor Organization", "European Commission", "World Bank", "Ministry of Labor in Indonesia"], "acronym": ["SAS"], "year": ["2022", "2021"], "geography": ["Eastern Europe", "Southeast Asia", "Indonesia"]}, "relations": [{"has_organization": {"head": "Labor Market Dynamics Report 2022", "tail": "International Labor Organization"}}, {"used_by": {"head": "Labor Market Dynamics Report 2022", "tail": "European Commission"}}, {"has_timeframe": {"head": "Labor Market Dynamics Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Labor Market Dynamics Report 2022", "tail": "Eastern Europe"}}, {"has_organization": {"head": "Skills Assessment Survey", "tail": "World Bank"}}, {"has_acronym": {"head": "Skills Assessment Survey", "tail": "SAS"}}, {"has_timeframe": {"head": "Skills Assessment Survey", "tail": "2021"}}, {"has_geography": {"head": "Skills Assessment Survey", "tail": "Southeast Asia"}}, {"used_by": {"head": "Skills Assessment Survey", "tail": "Ministry of Labor in Indonesia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "employment, labor markets, and skills development"}}
+{"input": "9 Figure 10. Selected characteristics across Ugandan and refugee households, % Source: RHCS 2018, WB staff calculations. A simple comparison between refugees and Ugandan households demonstrates that refugees lag with regards to selected characteristics found to narrow the poverty gap. For example, refugees are less likely to have access to land than Ugandans. If refugees have access to land, the majority do not own it, but have user rights. The size of land also differs a lot among Ugandan and refugee households. Most Ugandans have at least 0. 05 hectare per capita, while the majority of refugees have less than 0. 05 hectare per capita. Refugee heads of household are also less likely to work and less likely to be literate compared to their Ugandan counterparts. Refugees have higher shares of children and elderly in household size compared to Ugandans. For example, among almost 60 percent of refugee households, more than half of the household members are children and elderly compared to 42 percent of households among Ugandans. Economic inclusion dividend When a development approach to hosting refugees is followed and refugees earn incomes, there are two key beneficiaries. Refugees themselves, who gain dignity, financial autonomy and pathways to self-reliance.", "output": {"entities": {"named_data": ["RHCS"], "organization": ["WB"]}, "relations": [{"used_by": {"head": "RHCS", "tail": "WB"}}]}, "_meta": {"entry_id": 217, "dataset_name": "RHCS"}}
+{"input": "The Global Refugee and Migration Data Collection (GRMDC) provides comprehensive statistics on the trends and patterns of forced displacement across various regions. Published by the International Organization for Migration (IOM), this dataset is essential for understanding the challenges faced by displaced populations. The data covers the period from 2018 to 2022, offering insights for policymakers and humanitarian organizations working in the domain of forced migration.", "output": {"entities": {"named_data": ["Global Refugee and Migration Data Collection"], "organization": ["International Organization for Migration"], "acronym": ["GRMDC"], "year": ["2018 to 2022"], "geography": []}, "relations": [{"has_organization": {"head": "Global Refugee and Migration Data Collection", "tail": "International Organization for Migration"}}, {"has_acronym": {"head": "Global Refugee and Migration Data Collection", "tail": "GRMDC"}}, {"has_timeframe": {"head": "Global Refugee and Migration Data Collection", "tail": "2018 to 2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "forced displacement, refugees, and migration"}}
+{"input": "10 Last, specific adjustments are made in the case of Germany and the Republic of Korea. For Germany, bilateral data are available only by nationality. However, these data fail to take adequate account of the large number of ethnic Germans who arrived from other countries between 1944 and 1950 (mainly expellees) and those who arrived after1950 (mainly resettlers). Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3). In the case of Korea, data by nationality are readily available for each census round. However, these data fail to account for the large numbers of migrants from the People ‘ s Democratic Republic of Korea living in the Republic of Korea. Since the United Nations Trends in International Migrant Stock details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition. The nationality total was then subtracted from the UN total and the remaining migrants were assigned to the People ‘ s Democratic Republic of Korea.", "output": {"entities": {"named_data": ["United Nations Trends in International Migrant Stock"], "organization": ["United Nations"]}, "relations": [{"has_organization": {"head": "United Nations Trends in International Migrant Stock", "tail": "United Nations"}}]}, "_meta": {"entry_id": 629, "dataset_name": "United Nations Trends in International Migrant Stock"}}
+{"input": "Respondents who were untraced were much more likely to be residing outside Kagera (43 %) compared to their counterparts who were re-interviewed (8 %). The consumption data come from an extensive consumption module administered in 1991 and again in 2004. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing / personal items, transfers out and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Monetary levels were adjusted to account for spatial and temporal price differences, using price data collected in the Kagera survey in 1991 and 2004, and, for households outside Kagera, data from the National Household Budget Survey. Consumption is expressed in per capita, per annum terms. The poverty line is set at TZS 109, 663, calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2000 / 1 National Household Budget Survey estimate for Kagera (29 %). 4. Growth, Poverty and Physical Mobility in Kagera In this section, we discuss changes in living standards overall, and the changes for four mutually exclusive groups based on residence in 2004: (i) still residing in the baseline community, (ii) residing in a", "output": {"entities": {"named_data": ["Kagera survey"], "organization": ["National Household Budget Survey"]}, "relations": [{"has_organization": {"head": "Kagera survey", "tail": "National Household Budget Survey"}}]}, "_meta": {"entry_id": 487, "dataset_name": "Kagera survey"}}
+{"input": "The recent trends in forced displacement highlight the critical need for comprehensive data analysis. The Global Refugee and Migration Trends Report provides essential insights into the patterns of migration and the challenges faced by displaced populations. Understanding these dynamics is vital for policymakers aiming to address the humanitarian needs effectively. Furthermore, ongoing discussions around refugee integration emphasize the importance of data in shaping inclusive policies.", "output": {"entities": {"named_data": ["Global Refugee and Migration Trends Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "forced displacement, refugees, and migration"}}
+{"input": "15 resulting in government forces recapturing rebel held territory, establishment of a rebel base or headquarters, rebel activity that is not battle related (e. g. presence or the killing of civilians), and territorial transfers. The dataset consists of 4, 145 battle events for the 1960 – 2004 period. In the present analysis, we use 2, 530 of these. The remaining events were dropped as they either were in countries not included in the analysis, or because information was missing for one of the key variables. Each conflict event is associated with geographic coordinates and a date of occurrence. This information allows for spatial and temporal modeling of conflict events. The dataset used in this article covers 14 countries in Central Africa. 6 of them had a conflict in the 1960 – 2004 period according to the Uppsala / PRIO Armed Conflict Dataset (Gleditsch et al., 2002): Angola, Burundi, Republic of Congo (Brazzaville), Democratic Republic of Congo (Zaire), Rwanda, and Uganda.", "output": {"entities": {"named_data": ["Uppsala / PRIO Armed Conflict Dataset"], "organization": ["we"]}, "relations": [{"used_by": {"head": "Uppsala / PRIO Armed Conflict Dataset", "tail": "we"}}]}, "_meta": {"entry_id": 135, "dataset_name": "Uppsala / PRIO Armed Conflict Dataset"}}
+{"input": "Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner", "output": {"entities": {"named_data": ["Multiple Indicator Cluster Surveys"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Multiple Indicator Cluster Surveys", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1010, "dataset_name": "Multiple Indicator Cluster Surveys"}}
+{"input": "The Renewable Energy Access Survey (REAS) conducted in 2022 provides critical insights into energy consumption patterns across various regions. This comprehensive dataset encompasses data from rural and urban areas, specifically focusing on countries like Ghana and Bangladesh, which are leading efforts in renewable transitions. While the survey highlights progress, it also sheds light on the challenges faced in energy access, particularly for marginalized communities. The findings are instrumental for policymakers and NGOs aiming to improve energy equity. Source: Elaborations based on REAS data by the Global Energy Initiative.", "output": {"entities": {"named_data": ["Renewable Energy Access Survey"], "organization": ["Global Energy Initiative"], "acronym": ["REAS"], "year": ["2022"], "geography": ["Ghana", "Bangladesh"]}, "relations": [{"has_acronym": {"head": "Renewable Energy Access Survey", "tail": "REAS"}}, {"has_timeframe": {"head": "Renewable Energy Access Survey", "tail": "2022"}}, {"has_geography": {"head": "Renewable Energy Access Survey", "tail": "Ghana"}}, {"has_geography": {"head": "Renewable Energy Access Survey", "tail": "Bangladesh"}}, {"used_by": {"head": "Renewable Energy Access Survey", "tail": "Global Energy Initiative"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}
+{"input": "The recent Energy Access Assessment Report 2022 (EAAR) produced by the Global Renewable Energy Institute provides critical insights into the progress of energy access across Sub-Saharan Africa. This dataset has been extensively used by the African Energy Commission to inform policy decisions aimed at improving electricity coverage in the region. The report highlights significant gaps in access and suggests targeted interventions for the 2019-2022 timeframe, emphasizing the urgent need for investment in renewable energy sources to meet the growing demand for electricity. As highlighted in the assessment, the potential for solar power utilization is enormous, particularly in rural areas where traditional grid connections are limited.", "output": {"entities": {"named_data": ["Energy Access Assessment Report 2022", "Energy Access Assessment Report"], "organization": ["Global Renewable Energy Institute", "African Energy Commission"], "acronym": ["Energy Access Assessment Report", "EAAR"], "year": ["2022", "2019-2022"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Energy Access Assessment Report 2022", "tail": "Global Renewable Energy Institute"}}, {"used_by": {"head": "Energy Access Assessment Report 2022", "tail": "African Energy Commission"}}, {"has_acronym": {"head": "Energy Access Assessment Report 2022", "tail": "EAAR"}}, {"has_timeframe": {"head": "Energy Access Assessment Report 2022", "tail": "2019-2022"}}, {"has_geography": {"head": "Energy Access Assessment Report 2022", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "energy access and renewable transitions"}}
+{"input": "The Digital Adoption Survey (DAS) 2022, which evaluates the technology usage patterns amongst households in Sub-Saharan Africa, provides essential insights into how digital resources are being accessed. Conducted by the International Technology Initiative (ITI), this survey's findings help shape policies for enhancing connectivity in the region. Additionally, the Urban Tech Index (UTI) 2021 highlights metropolitan trends in technology adoption across various cities in Asia. Although the UTI has been referenced by numerous urban planners, it is important to note that it does not include rural areas. Lastly, the Global E-Readiness Report (GER) 2019 focuses on assessing the e-readiness of nations worldwide, yet it does not cover specific geographical data for small island nations, providing a broad view instead.", "output": {"entities": {"named_data": ["Digital Adoption Survey", "Urban Tech Index", "Global E-Readiness Report"], "organization": ["International Technology Initiative"], "acronym": ["DAS", "UTI", "GER"], "year": ["2022", "2021", "2019"], "geography": ["Sub-Saharan Africa", "Asia", "nations worldwide"]}, "relations": [{"has_acronym": {"head": "Digital Adoption Survey", "tail": "DAS"}}, {"has_timeframe": {"head": "Digital Adoption Survey", "tail": "2022"}}, {"has_geography": {"head": "Digital Adoption Survey", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Digital Adoption Survey", "tail": "International Technology Initiative"}}, {"has_acronym": {"head": "Urban Tech Index", "tail": "UTI"}}, {"has_timeframe": {"head": "Urban Tech Index", "tail": "2021"}}, {"has_geography": {"head": "Urban Tech Index", "tail": "Asia"}}, {"has_acronym": {"head": "Global E-Readiness Report", "tail": "GER"}}, {"has_timeframe": {"head": "Global E-Readiness Report", "tail": "2019"}}, {"has_geography": {"head": "Global E-Readiness Report", "tail": "nations worldwide"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "digital development and technology adoption"}}
+{"input": "The recent Economic Empowerment Survey (EES) conducted in 2022 by the Institute for Economic Research (IER) provides crucial insights into financial inclusion trends across various demographics in Southeast Asia. This dataset has been utilized by the Asian Development Bank (ADB) to analyze the impacts of economic policies on low-income households. Furthermore, the Financial Inclusion Assessment Report 2023 (FIAR) published by the Global Finance Institute (GFI) serves as a complementary resource, with findings based on the EES and the 2021 National Financial Survey (NFS). Both the EES and NFS offer valuable data for understanding regional disparities in access to financial services, making them important for policymakers aiming to enhance economic access and equity in the region.", "output": {"entities": {"named_data": ["Economic Empowerment Survey", "Financial Inclusion Assessment Report 2023", "National Financial Survey"], "organization": ["Institute for Economic Research", "Asian Development Bank", "Global Finance Institute"], "acronym": ["EES", "FIAR", "NFS"], "year": ["2022", "2023", "2021"], "geography": ["Southeast Asia"]}, "relations": [{"has_organization": {"head": "Economic Empowerment Survey", "tail": "Institute for Economic Research"}}, {"used_by": {"head": "Economic Empowerment Survey", "tail": "Asian Development Bank"}}, {"has_acronym": {"head": "Economic Empowerment Survey", "tail": "EES"}}, {"has_timeframe": {"head": "Economic Empowerment Survey", "tail": "2022"}}, {"has_organization": {"head": "Financial Inclusion Assessment Report 2023", "tail": "Global Finance Institute"}}, {"has_acronym": {"head": "Financial Inclusion Assessment Report 2023", "tail": "FIAR"}}, {"has_timeframe": {"head": "Financial Inclusion Assessment Report 2023", "tail": "2023"}}, {"has_organization": {"head": "National Financial Survey", "tail": "Institute for Economic Research"}}, {"has_acronym": {"head": "National Financial Survey", "tail": "NFS"}}, {"has_timeframe": {"head": "National Financial Survey", "tail": "2021"}}, {"has_geography": {"head": "Economic Empowerment Survey", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Financial Inclusion Assessment Report 2023", "tail": "Southeast Asia"}}, {"has_geography": {"head": "National Financial Survey", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "The Global Employment Trends Report 2023 provides insights into the changing dynamics of labor markets worldwide. Published by the International Labour Organization (ILO), this report analyzes the impact of economic fluctuations on employment opportunities in various regions. It serves as a critical resource for policymakers aiming to address issues related to unemployment and skills development.", "output": {"entities": {"named_data": ["Global Employment Trends Report 2023"], "organization": ["International Labour Organization", "ILO"], "acronym": ["ILO"], "year": ["2023"], "geography": []}, "relations": [{"has_organization": {"head": "Global Employment Trends Report 2023", "tail": "International Labour Organization"}}, {"has_acronym": {"head": "International Labour Organization", "tail": "ILO"}}, {"has_timeframe": {"head": "Global Employment Trends Report 2023", "tail": "2023"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "employment, labor markets, and skills development"}}
+{"input": "Section 6 presents the differing views of IDPs, refugees and returnees on what happened during the crisis and prospects for peace. Section 7 concludes the paper. 2. The Listening to Displaced People Survey The Listening to Displaced People Survey (LDPS) combines a baseline face-to-face survey with mobile phone follow-up interviews. During the baseline survey respondents were identified and information on household and respondent characteristics was collected. Once the baseline interview was completed, respondents were given a mobile phone and started to receive, at monthly intervals, phone interviews from a call center in Bamako. During these phone interviews structured questions were asked about welfare of the household. Phone interviews are standard practice in developed countries and they are increasingly being used in less developed countries, as the coverage of cell phone networks expands. Not only do these kinds of surveys allow for low cost, high frequency representative data collection (Hoogeveen et", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "organization": ["Hoogeveen et"]}, "relations": [{"used_by": {"head": "Listening to Displaced People Survey", "tail": "Hoogeveen et"}}]}, "_meta": {"entry_id": 611, "dataset_name": "Listening to Displaced People Survey"}}
+{"input": "The East African Trade Assessment Report (EATAR) provides crucial insights into trade flows across the region. Produced by the East Africa Trade Organization, this comprehensive report covers the years 2018–2020 and includes analysis relevant to trade policies in Kenya and Uganda. Additionally, the data from the Global Competitiveness Index (GCI) offers a benchmark for assessing the economic competitiveness of various nations, including Rwanda and Tanzania. The GCI data spans the years 2019 to 2021 and is utilized by numerous regional policymakers to inform strategic initiatives. This mix of datasets highlights diverse perspectives on economic performance and trade dynamics in East Africa, offering valuable resources for stakeholders.", "output": {"entities": {"named_data": ["East African Trade Assessment Report", "Global Competitiveness Index"], "organization": ["East Africa Trade Organization"], "acronym": ["EATAR", "GCI"], "year": ["2018–2020", "2019 to 2021"], "geography": ["Kenya", "Uganda", "Rwanda", "Tanzania"]}, "relations": [{"has_acronym": {"head": "East African Trade Assessment Report", "tail": "EATAR"}}, {"has_timeframe": {"head": "East African Trade Assessment Report", "tail": "2018–2020"}}, {"has_geography": {"head": "East African Trade Assessment Report", "tail": "Kenya"}}, {"has_geography": {"head": "East African Trade Assessment Report", "tail": "Uganda"}}, {"has_acronym": {"head": "Global Competitiveness Index", "tail": "GCI"}}, {"has_timeframe": {"head": "Global Competitiveness Index", "tail": "2019 to 2021"}}, {"has_geography": {"head": "Global Competitiveness Index", "tail": "Rwanda"}}, {"has_geography": {"head": "Global Competitiveness Index", "tail": "Tanzania"}}, {"has_organization": {"head": "East African Trade Assessment Report", "tail": "East Africa Trade Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The Global Climate Change Assessment Report 2022, published by the Environmental Protection Agency (EPA), provides comprehensive data on greenhouse gas emissions across various regions. This dataset is being utilized by the United Nations Framework Convention on Climate Change (UNFCCC) to track progress towards emission reduction targets. Additionally, the assessment includes detailed statistics for North America, emphasizing the urgent need for policy interventions. The EPA has also released the Biodiversity and Ecosystem Services Survey (BESS) which focuses on evaluating ecosystem health from 2020 to 2022. This data is being referenced by the World Wildlife Fund (WWF) to advocate for conservation strategies in various ecosystems.", "output": {"entities": {"named_data": ["Global Climate Change Assessment Report 2022", "Biodiversity and Ecosystem Services Survey"], "organization": ["Environmental Protection Agency", "United Nations Framework Convention on Climate Change", "World Wildlife Fund"], "acronym": ["Biodiversity and Ecosystem Services Survey", "UNFCCC"], "year": ["2022", "2020 to 2022"], "geography": ["North America"]}, "relations": [{"has_organization": {"head": "Global Climate Change Assessment Report 2022", "tail": "Environmental Protection Agency"}}, {"used_by": {"head": "Global Climate Change Assessment Report 2022", "tail": "United Nations Framework Convention on Climate Change"}}, {"has_geography": {"head": "Global Climate Change Assessment Report 2022", "tail": "North America"}}, {"has_organization": {"head": "Biodiversity and Ecosystem Services Survey", "tail": "Environmental Protection Agency"}}, {"used_by": {"head": "Biodiversity and Ecosystem Services Survey", "tail": "World Wildlife Fund"}}, {"has_timeframe": {"head": "Biodiversity and Ecosystem Services Survey", "tail": "2020 to 2022"}}, {"has_acronym": {"head": "Biodiversity and Ecosystem Services Survey", "tail": "BESS"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "environmental sustainability and natural resources"}}
+{"input": "Recent analyses of macroeconomic trends have drawn upon the National Financial Inclusion Survey and the Global Economic Outlook Report, which provide valuable insights into the current state of financial access and economic performance across various regions. These datasets are crucial for understanding the dynamics of financial systems and their impact on poverty reduction initiatives. Furthermore, the implications of these studies extend beyond mere numbers, influencing policy recommendations and development strategies.", "output": {"entities": {"named_data": ["National Financial Inclusion Survey", "Global Economic Outlook Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "macroeconomic trends and financial inclusion"}}
+{"input": "The 2022 Labor Market Assessment Report, published by the International Labour Organization, provides a comprehensive overview of employment trends in various sectors. This report serves as a crucial resource for policymakers and researchers looking to understand labor dynamics during a challenging economic period.", "output": {"entities": {"named_data": ["Labor Market Assessment Report"], "organization": ["International Labour Organization"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Labor Market Assessment Report", "tail": "International Labour Organization"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "employment, labor markets, and skills development"}}
+{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**B. Project Components**\n\n30. The project design is guided by the following principles: (a) rapid response to support schools in light of the deteriorating economic conditions; (b) lessons learned from past education projects, in particular Basic Education Recovery Project (BERP) (P128644); (c) government ownership and priorities aligned to the 2018-2022 Education Sector Strategic Plan (ESSP); and (d) complementarity with other donor funded projects to fill strategic gaps.\n\n31. Attainment of the proposed PDO will be based on the Government’s achievement of results in the first component: (a) school grants program, which will include providing support to schools to improve learning environment and practices. The project will operate at system and school levels, targeting all public schools.\n\n**Component 1: School Grants Program (US$11.275 million).**\n\n32. This component will support provision of school grants to improve learnining environments and school planning. School grants will aim to: _(i) Incentivize parents’ engagement to reduce the risk of students (especially girls) dropping out_ . While basic education is officially free in Sudan, currently, families contribute greatly to education expenditures at the school level. As the economic situation has deteriorated, many vulnerable families may lose the ability to pay for basic services and pull the children out of school (especially girls). Furthermore, families may face challenges to provide the pupils with basic requirements for schooling, such as uniform, school bags, exercise books, etc. School Grants can play an important role in mitigating the expected economic shock on the most vulnerable and help reduce the education cost burdens during the hard time. It can also help provide girls in the upper primary grades with necessary packages such as sanitary napkins to encourage their retention.\n\n_(ii) Support teachers to reduce absenteeism_ . Due to high inflation rates, teacher remuneration has been deteriorating in real terms, posing the risk of teachers leaving schools temporarily or permanently for alternative livelihood pathways. The School Grants may be used to support teachers (in cash or in-kind).\n\n_(iii) Support the learning environment_ . School Grants are expected to be an important source of funding for the targeted schools to support the acquisition of basic learning materials, stationery, notebooks, classrooms furniture and equipment which contribute to improving the learning environment to attract and retain pupils and teachers, especially females in school. A list of eligible items will be developed and provided to the schools.\n\n_(iv) Improve efficiency by strengthening capacity for participatory planning, budgeting and monitoring at the school_ _level_ . School grants can help disadvantaged schools create a participatory management structure at the school level.\nA school profile report that provides information on the school will be provided to each school to support the participatory evidence-based planning process.\n\n_(v) Improve equity in education by helping children in disadvantaged situation including IDPs, refugees, girls_ .\nAccording to the latest Annual School Census, public schools enroll 30 thousand refugee students (in 1,681 schools) and 280 thousand IDPs (in 1,852 schools). While IDP children are concentrated in three Darfur states (68 percent of Page 14 of 40", "output": {"entities": {"named_data": ["Annual School Census"], "organization": ["The World Bank"]}, "relations": [{"used_by": {"head": "Annual School Census", "tail": "The World Bank"}}]}, "_meta": {"entry_id": 882, "dataset_name": "Annual School Census"}}
+{"input": "The recent analysis of the Coastal Resilience Assessment (CRA) highlights significant trends in environmental sustainability across multiple regions. The dataset, which covers the period from 2020 to 2022, has been instrumental in guiding policy decisions in coastal management. While the CRA data has been primarily used by the National Oceanic and Atmospheric Administration (NOAA), its utility extends to various stakeholders interested in climate adaptation strategies. The findings indicate that proactive measures taken between 2020 and 2022 have led to improved resilience in several coastal communities, particularly along the Eastern Seaboard. The CRA dataset provides invaluable insights into the effects of climate change and coastal erosion, ensuring that future developments are both sustainable and adaptive.", "output": {"entities": {"named_data": ["Coastal Resilience Assessment"], "organization": ["National Oceanic and Atmospheric Administration"], "acronym": ["CRA"], "year": ["2020", "2020 to 2022", "2022"], "geography": ["Eastern Seaboard"]}, "relations": [{"has_acronym": {"head": "Coastal Resilience Assessment", "tail": "CRA"}}, {"has_timeframe": {"head": "CRA", "tail": "2020 to 2022"}}, {"has_geography": {"head": "CRA", "tail": "Eastern Seaboard"}}, {"used_by": {"head": "CRA", "tail": "National Oceanic and Atmospheric Administration"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
+{"input": "The recent analysis of domestic revenue trends was largely informed by the 2021 Public Finance Assessment Report (PFAR) produced by the Ministry of Finance in Ghana. This dataset provides a comprehensive overview of the revenue collection mechanisms employed during the 2019-2021 period, offering crucial insights into the effectiveness of fiscal policies. Furthermore, the World Bank's Domestic Revenue Database (DRD) has been pivotal in validating these findings, particularly concerning its extensive geographical coverage throughout Africa and Asia. The DRD dataset spans a crucial timeframe from 2015 to 2022, allowing researchers and policymakers to design more effective revenue strategies based on empirical evidence.", "output": {"entities": {"named_data": ["Public Finance Assessment Report", "Domestic Revenue Database"], "organization": ["Ministry of Finance", "World Bank"], "acronym": ["PFAR", "DRD"], "year": ["2021", "2019-2021", "2015 to 2022"], "geography": ["Ghana", "Africa", "Asia"]}, "relations": [{"has_acronym": {"head": "Public Finance Assessment Report", "tail": "PFAR"}}, {"has_timeframe": {"head": "Public Finance Assessment Report", "tail": "2021"}}, {"has_geography": {"head": "Public Finance Assessment Report", "tail": "Ghana"}}, {"has_acronym": {"head": "Domestic Revenue Database", "tail": "DRD"}}, {"has_timeframe": {"head": "Domestic Revenue Database", "tail": "2015 to 2022"}}, {"has_geography": {"head": "Domestic Revenue Database", "tail": "Africa"}}, {"has_geography": {"head": "Domestic Revenue Database", "tail": "Asia"}}, {"has_organization": {"head": "Public Finance Assessment Report", "tail": "Ministry of Finance"}}, {"used_by": {"head": "Domestic Revenue Database", "tail": "World Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "public financial management and domestic revenue"}}
+{"input": "In recent years, the Digital Connectivity Assessment 2022 (DCA 2022) has provided critical insights into internet accessibility across underserved regions in South America. This dataset, published by the International Telecommunication Union (ITU), has been extensively used by various NGOs, including Connect4Change, to evaluate progress in digital inclusion initiatives. Additionally, the Digital Literacy Survey 2021, administered by Tech Access Alliance, was utilized by several educational organizations to assess the impact of training programs in urban areas of Brazil. This survey, along with the Mobile Adoption Index 2020 (MAI 2020) produced by the World Bank, sheds light on the growth of mobile technology usage among lower-income populations in both rural and metropolitan settings. The MAI 2020 has been referenced by multiple researchers aiming to understand the trends and barriers in mobile technology adoption.", "output": {"entities": {"named_data": ["Digital Connectivity Assessment 2022", "Digital Literacy Survey 2021", "Mobile Adoption Index 2020"], "organization": ["International Telecommunication Union", "Connect4Change", "Tech Access Alliance", "World Bank"], "acronym": ["DCA 2022", "MAI 2020"], "year": ["2022", "2021", "2020"], "geography": ["South America", "Brazil"]}, "relations": [{"has_organization": {"head": "Digital Connectivity Assessment 2022", "tail": "International Telecommunication Union"}}, {"used_by": {"head": "Digital Connectivity Assessment 2022", "tail": "Connect4Change"}}, {"has_organization": {"head": "Digital Literacy Survey 2021", "tail": "Tech Access Alliance"}}, {"used_by": {"head": "Digital Literacy Survey 2021", "tail": "educational organizations"}}, {"has_organization": {"head": "Mobile Adoption Index 2020", "tail": "World Bank"}}, {"has_acronym": {"head": "Digital Connectivity Assessment 2022", "tail": "DCA 2022"}}, {"has_acronym": {"head": "Mobile Adoption Index 2020", "tail": "MAI 2020"}}, {"has_timeframe": {"head": "Digital Connectivity Assessment 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Digital Literacy Survey 2021", "tail": "2021"}}, {"has_timeframe": {"head": "Mobile Adoption Index 2020", "tail": "2020"}}, {"has_geography": {"head": "Digital Connectivity Assessment 2022", "tail": "South America"}}, {"has_geography": {"head": "Digital Literacy Survey 2021", "tail": "Brazil"}}, {"has_geography": {"head": "Mobile Adoption Index 2020", "tail": "Brazil"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "digital development and technology adoption"}}
+{"input": "The recent analysis of the Digital Inclusion Index (DII) by the Global Connectivity Initiative highlights significant disparities across regions. The DII data, which covers 2020–2022, was published by the International Telecommunications Union (ITU) and has been instrumental in guiding policy reforms. Additionally, the World Economic Forum (WEF) utilized the DII data to measure technology adoption in low-income countries, particularly focusing on sub-Saharan Africa. The ITU's publication also includes the Technology Access Survey (TAS) 2021, which specifically examines internet access across different demographics in the region, further informing stakeholders about gaps in digital equity. Both datasets are crucial for understanding the ongoing challenges in digital development.", "output": {"entities": {"named_data": ["Digital Inclusion Index", "DII", "Technology Access Survey"], "organization": ["International Telecommunications Union", "ITU", "World Economic Forum", "WEF"], "acronym": ["Digital Inclusion Index", "DII", "Technology Access Survey"], "year": ["2020–2022", "2021"], "geography": ["sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Digital Inclusion Index", "tail": "International Telecommunications Union"}}, {"used_by": {"head": "Digital Inclusion Index", "tail": "World Economic Forum"}}, {"has_acronym": {"head": "Digital Inclusion Index", "tail": "DII"}}, {"has_timeframe": {"head": "Digital Inclusion Index", "tail": "2020–2022"}}, {"has_geography": {"head": "Technology Access Survey", "tail": "sub-Saharan Africa"}}, {"has_organization": {"head": "Technology Access Survey", "tail": "International Telecommunications Union"}}, {"has_timeframe": {"head": "Technology Access Survey", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "digital development and technology adoption"}}
+{"input": "The recent Data on Water Quality and Availability (DWQA) published by the Global Water Institute provides crucial insights into the trends affecting freshwater resources across the Southern Africa region. In 2022, this dataset has been utilized by the Southern African Development Community (SADC) to inform regional water management policies. Additionally, the 2021 Forest Assessment Report (FAR) released by the Forestry Research Agency has been referenced by multiple NGOs, including the Eco-Alliance, to advocate for sustainable forestry practices in Mozambique. The FAR also covers extensive data from 2020 to 2021, allowing stakeholders to understand the changes in forest cover and biodiversity in the area.", "output": {"entities": {"named_data": ["Data on Water Quality and Availability", "Forest Assessment Report"], "organization": ["Global Water Institute", "Southern African Development Community", "Forestry Research Agency", "Eco-Alliance"], "acronym": ["DWQA", "FAR"], "year": ["2022", "2021", "2020 to 2021"], "geography": ["Southern Africa", "Mozambique"]}, "relations": [{"has_organization": {"head": "Data on Water Quality and Availability", "tail": "Global Water Institute"}}, {"used_by": {"head": "Data on Water Quality and Availability", "tail": "Southern African Development Community"}}, {"has_acronym": {"head": "Data on Water Quality and Availability", "tail": "DWQA"}}, {"has_timeframe": {"head": "Data on Water Quality and Availability", "tail": "2022"}}, {"has_organization": {"head": "Forest Assessment Report", "tail": "Forestry Research Agency"}}, {"used_by": {"head": "Forest Assessment Report", "tail": "Eco-Alliance"}}, {"has_acronym": {"head": "Forest Assessment Report", "tail": "FAR"}}, {"has_timeframe": {"head": "Forest Assessment Report", "tail": "2020 to 2021"}}, {"has_geography": {"head": "Forest Assessment Report", "tail": "Mozambique"}}, {"has_geography": {"head": "Data on Water Quality and Availability", "tail": "Southern Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "environmental sustainability and natural resources"}}
+{"input": "34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey.", "output": {"entities": {"named_data": ["Survey on Income and Living Conditions survey"], "organization": ["Labor Force Survey"]}, "relations": [{"used_by": {"head": "Survey on Income and Living Conditions survey", "tail": "Labor Force Survey"}}]}, "_meta": {"entry_id": 93, "dataset_name": "Survey on Income and Living Conditions survey"}}
+{"input": "18 corridor, so only aggregate numbers can be compared. For this comparison, mid-year estimates of the world migrant stock for 1990 – 2000 are taken from the 2008 edition and estimates for the earlier censuses, 1960 – 1980, are taken from the 2005 edition (table 8). The analysis subtracts the estimated number of refugees from the total mid-year estimates of the world migrant stock from the Trends in International Migrant Stock database to yield the net number of migrants in each decade. These numbers are then compared with the decadal estimates generated through this project, both the total and the net, after subtracting estimates of migrants within the Soviet Union for 1960 – 1980 (data for 1990 and 2000 should be directly comparable) and the number of ethnic German migrants added to the German censuses. { Table 8 here} The aggregate estimates are remarkably close (the two net totals), differing at most by around 1 million migrants, except in 1990. There are several possible explanations for these differences. First, the census totals from the current work may not match because censuses do not always make allowances for temporary workers. For example, Singapore ‘ s official 2000 census records 563, 430 foreign-born migrants. The United Nations, however, reports 1, 351, 806 foreign-born migrants for 2000.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock database"], "organization": ["this project"]}, "relations": [{"used_by": {"head": "Trends in International Migrant Stock database", "tail": "this project"}}]}, "_meta": {"entry_id": 1022, "dataset_name": "Trends in International Migrant Stock database"}}
+{"input": "Following the approach used for the Global Costing of Refugee Inclusion, successful inclusion is defined as earning sufficient income to be no longer poor and to consume more than the (international) poverty line. This definition opens two tracks for investigation: first, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. This opens the second track which explores the factors that determine, or at least that are associated with, the size of the poverty gap. The note is organized as follows. The next section discusses data and presents some key descriptive statistics on refugees and host communities in Uganda. This is followed by a methodological section discussing how own income and aid are complements and how an analysis of poverty gaps informs about the need for assistance. This is followed by two analytical sections. The first identifies refugee poverty gaps, and assistance needs for refugees with distinct characteristics. The following section estimates how much has been saved by including refugees in the economy and explores how more could be saved. Conclusions follow. 2 The poverty numbers in World Bank (2019) are based on the official poverty line adopted in Uganda in 1997. There was a need to update this line as it was too old and producing a very low poverty rate. For example, using this line produced a national poverty rate of about 21 percent in 2019 / 20 compared to more the than 40 percent international poverty rate using the USD 2. 15 2017 PPP daily poverty line. In order to address this criticism, the poverty line was revised by the Uganda Bureau of Statistics in 2021, but it is not available for the 2018 Refugee and Host Communities Household Survey used in this note. Instead, we are using the international poverty line throughout.", "output": {"entities": {"named_data": ["2018 Refugee and Host Communities Household Survey"], "organization": ["Uganda Bureau of Statistics", "World Bank"]}, "relations": [{"has_organization": {"head": "2018 Refugee and Host Communities Household Survey", "tail": "Uganda Bureau of Statistics"}}, {"used_by": {"head": "2018 Refugee and Host Communities Household Survey", "tail": "World Bank"}}]}, "_meta": {"entry_id": 380, "dataset_name": "2018 Refugee and Host Communities Household Survey"}}
+{"input": "The Regional Budget Assessment Report (RBAR) provides critical insights into fiscal management across several countries in the Sub-Saharan Africa region. This dataset, covering the years 2020 to 2022, highlights the variations in domestic revenue mobilization efforts. Additionally, the 2021 Domestic Expenditure Survey (DES) for Nigeria offers a comprehensive overview of government spending patterns, emphasizing the need for improved financial transparency. Both datasets have been utilized by various organizations, including the African Development Bank, to enhance policy recommendations for better governance. Meanwhile, the National Revenue Analysis 2019 (NRA 2019) focuses on revenue sources and trends in Kenya, but it lacks detailed organizational citations in existing literature.", "output": {"entities": {"named_data": ["Regional Budget Assessment Report", "Domestic Expenditure Survey", "National Revenue Analysis 2019"], "organization": ["African Development Bank"], "acronym": ["RBAR", "DES", "NRA 2019"], "year": ["2020 to 2022", "2021", "2019"], "geography": ["Sub-Saharan Africa", "Nigeria", "Kenya"]}, "relations": [{"has_acronym": {"head": "Regional Budget Assessment Report", "tail": "RBAR"}}, {"has_timeframe": {"head": "Regional Budget Assessment Report", "tail": "2020 to 2022"}}, {"has_geography": {"head": "Regional Budget Assessment Report", "tail": "Sub-Saharan Africa"}}, {"has_acronym": {"head": "Domestic Expenditure Survey", "tail": "des"}}, {"has_timeframe": {"head": "Domestic Expenditure Survey", "tail": "2021"}}, {"has_geography": {"head": "Domestic Expenditure Survey", "tail": "Nigeria"}}, {"has_acronym": {"head": "National Revenue Analysis 2019", "tail": "NRA 2019"}}, {"has_timeframe": {"head": "National Revenue Analysis 2019", "tail": "2019"}}, {"has_geography": {"head": "National Revenue Analysis 2019", "tail": "Kenya"}}, {"used_by": {"head": "Domestic Expenditure Survey", "tail": "African Development Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "public financial management and domestic revenue"}}
+{"input": "social cohesion between refugees and host communities, creating a strong foundation for future economic progress. 5. **Building on the gains made, the government’s strategic aim for refugee management is now to promote** **self-reliance.** As one of the four objectives of the GCR, self-reliance is also central to both the National Strategy for Sustainable Graduation and the draft Refugee Sustainable Graduation Strategy. Through successful implementation of the two strategies, the government expects to: (a) reduce expenditure on social safety net programs for Rwandese and humanitarian aid for refugees; and (b) unlock the potential of refugees to support economic development. This will also contribute to key results in Rwanda’s _National Strategy for Transformation 2 (2024-2029)_ on creating jobs, improving education and the quality-of service-delivery. Shifting to a development approach to achieve self-reliance is increasingly urgent in light of dwindling humanitarian assistance. For instance, funding for UNHCR in Rwanda fell from US$49 million in 2020 (only 49 percent of needs) to US$37 million in 2023 (41 percent of needs), despite almost no change in refugee numbers. 6. **Progress towards achieving refugee self-reliance is hampered by a lack of employment and income-** **generating opportunities.** A tailored Refugee Self-Reliance Index (RSRI) prepared by the GoR,", "output": {"entities": {"named_data": ["tailored Refugee Self-Reliance Index"], "organization": ["GoR"]}, "relations": [{"has_organization": {"head": "tailored Refugee Self-Reliance Index", "tail": "GoR"}}]}, "_meta": {"entry_id": 1221, "dataset_name": "tailored Refugee Self-Reliance Index"}}
+{"input": "41 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Since some adjustments could take place over time, we always use the figures from the last available report.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR statistical population online dataset", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 1099, "dataset_name": "UNHCR statistical population online dataset"}}
+{"input": "**Several key messages emerge.** **First, SOEs in Romania were larger-employed more people and had**\n**larger assets per worker-and paid better wages, on average, than their POE peers from 2011 to**\n**2019.** On average, they had lower revenue per worker than POEs over the same period. These results are\nrobust for the various SOE ownership degrees (i.e., minority and majority owned SOEs) and align with\nother studies. In addition, the average SOE experienced higher job growth, investment, and labor\nproductivity growth but slower wage growth than the average POE over the same period. Nevertheless,\nthese growth effects are not uniform across the various ownership degrees.\n\nemployment Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sector size in economy Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations **[†]** 704 704 704 704 704 704 704 697 704 704 Within R-squared **[†]** 0.095 0.009 0.039 0.267 0.147 0.193 0.046 0.072 0.045 0.040 Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.\n\n<!-- PAGE 43 -->\nSource: World Bank staff analysis using Romania MoF firm-level data from 2018 to 2020. The sample includes all firms. Growth is calculated as the difference between the values\nin year t and t-1 divided by the average of the values in year t and t-1.\n\n5 According to the World Bank Businesses of the State database, SOEs with at least 10% state ownership accounted for 3.6% of the formal employment as of 2019 in Romania.\n\n(2022b) to supplement the World Bank BOS database.", "output": {"entities": {"named_data": ["World Bank BOS database"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "World Bank BOS database", "tail": "World Bank"}}, {"used_by": {"head": "World Bank BOS database", "tail": "World Bank"}}]}, "_meta": {"entry_id": 939, "dataset_name": "World Bank BOS database"}}
+{"input": "monthly temperature and rainfall data for the period 1961-2000 (CRU), provided for\n\nWe compute R [2] 's between CRU and each of the eight GCMs for temperature and rainfall\n\nWe require separate benchmarks for the CRU and each of the GCMs. We establish", "output": {"entities": {"named_data": ["CRU"], "organization": ["We"]}, "relations": [{"used_by": {"head": "CRU", "tail": "We"}}]}, "_meta": {"entry_id": 433, "dataset_name": "CRU"}}
+{"input": "Data sources: UNHCR PRIMES, UNHCR Resettlement Statistics Report.   For more information or to contribute, please contact UNHCR RBSA DIMA (rsarbdima@unhc\n\n**1,729**\n\n**154**\n\n**57**\n\n**53**\n\n**34**\n\n**13**\n\n**12**\n\n**4**\n\n**3**\n\n**2**\n\n**COD**\n\n**BDI**\n\n**SOM**\n\n**RWA**\n\n**TUR**\n\n**ZAM**\n\n**ETH**\n\n**UGA**\n\n**ANG**\n\n**AFG**\n\n**COD**\n\n**BDI**\n\n**SOM**\n\n**RWA**\n\n**ETH**\n\n**PAK**\n\n**TUR**\n\n**CAR**\n\n**BOT**\n\n**ERT**\n\n**3,619**\n\n**263**\n\n**235**\n\n**220**\n\n**40**\n\n**36**\n\n**19**\n\n**14**\n\n**8**\n\n**8**", "output": {"entities": {"named_data": ["UNHCR PRIMES"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR PRIMES", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 457, "dataset_name": "UNHCR PRIMES"}}
+{"input": "The Climate Impact Assessment (CIA) conducted by the Global Environmental Research Institute (GERI) has provided pivotal insights into climate vulnerabilities across sub-Saharan Africa. This dataset, covering the period from 2015 to 2020, is extensively used by the African Development Bank (AfDB) to inform their investment strategies in resilience-building initiatives. Additionally, the Urban Resilience Index (URI) — published by the Urban Planning Agency (UPA) — is another critical dataset focusing on urban areas in Southeast Asia, aiding organizations like the United Nations Development Programme (UNDP) in assessing city preparedness for disasters. These datasets underscore the collaboration among various entities towards enhancing climate resilience.", "output": {"entities": {"named_data": ["Climate Impact Assessment", "Urban Resilience Index"], "organization": ["Global Environmental Research Institute", "African Development Bank", "Urban Planning Agency", "United Nations Development Programme"], "acronym": ["CIA", "AfDB", "URI", "UPA", "UNDP"], "year": ["2015 to 2020"], "geography": ["sub-Saharan Africa", "Southeast Asia"]}, "relations": [{"has_organization": {"head": "Climate Impact Assessment", "tail": "Global Environmental Research Institute"}}, {"used_by": {"head": "Climate Impact Assessment", "tail": "African Development Bank"}}, {"has_acronym": {"head": "Climate Impact Assessment", "tail": "CIA"}}, {"has_timeframe": {"head": "Climate Impact Assessment", "tail": "2015 to 2020"}}, {"has_organization": {"head": "Urban Resilience Index", "tail": "Urban Planning Agency"}}, {"used_by": {"head": "Urban Resilience Index", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Urban Resilience Index", "tail": "URI"}}, {"has_geography": {"head": "Urban Resilience Index", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "The COVID-19 Panel Phone Survey of Households sample is nationally representative, representative of Bamako, and representative of both urban and rural areas.\n\nThe EHCVM sample itself covered 8,390 households across Mali and is nationally representative, representative of Bamako, and representative of both urban and rural areas. The survey relies on a multi-module instrument covering topics including a household's socio-economic characteristics, time use, production activities, and welfare indicators such as consumption expenditure and food security.\n\nuse sampling weights derived from the 2018 EHCVM sampling frame and adjusted for response rates\n\n\nin the COVID-19 Panel Phone Survey of Households. These sampling weights are applied both in our\n\nIn this study, we use the FAO's Food Insecurity Experience Scale (FIES) as primary outcome of interest. The FIES aims to measure food insecurity based on the direct experiences of people relating to food security (Ballard _et_ _al._, 2013; Smith _et_ _al._, 2017).", "output": {"entities": {"named_data": ["COVID-19 Panel Phone Survey of Households"], "organization": ["we"]}, "relations": [{"used_by": {"head": "COVID-19 Panel Phone Survey of Households", "tail": "we"}}]}, "_meta": {"entry_id": 23, "dataset_name": "COVID-19 Panel Phone Survey of Households"}}
+{"input": "recently introduced the first version of their Global Internal Displacement Database (GIDD) that allows users to explore, filter and sort IDMC ’ s data to produce graphs and tables, and export underlying data. 102 Such platforms need to incorporate safeguards to protect the privacy and confidentiality of individuals ’ data. UNHCR and Statistics Norway are currently leading an initiative to improve forced displacement statistics with the participation of national statistical agencies. This process began with the presentation of the “ Report on Statistics on Refugees and IDPs ” at the 46th session of the UN Statistical Commission in March 2015, 103 followed by an international conference in Turkey in October 2015. 104 The conference set in motion a process for national statistical agencies to collaborate to develop a set of recommendations that both countries and international organizations can use to improve data collection, reporting, data disaggregation, and overall quality, including the preparation of International Recommendations for Refugee Statistics (IRRS). Progress on this agenda was discussed at the 47th session of UNSD held in New York in March 2016, where it was recommended that the expert group should also include IDPs in its scope of work (UNSD 2016). 105 The current initiative is focused on refugees, asylum-seekers and IDPs but would ideally be extended to host communities and returnees.", "output": {"entities": {"named_data": ["Global Internal Displacement Database"], "organization": ["UNHCR", "Statistics Norway"]}, "relations": [{"has_organization": {"head": "Global Internal Displacement Database", "tail": "UNHCR"}}, {"used_by": {"head": "Global Internal Displacement Database", "tail": "Statistics Norway"}}]}, "_meta": {"entry_id": 338, "dataset_name": "Global Internal Displacement Database"}}
+{"input": "The Labor Market Trends Report 2022, published by the National Bureau of Labor Statistics (NBSL), provides comprehensive data on employment patterns across various sectors. This dataset has been extensively used by the Economic Development Agency (EDA) to analyze job growth in urban areas over the past year. Additionally, the Skills Development Assessment (SDA) conducted in 2021 by the International Labor Organization (ILO) focuses on workforce training initiatives and their impacts on employment rates. The findings from the SDA have been cited by local governments in the Midwest region to inform their skills training programs. Furthermore, the Global Employment Database (GED) offers insights from 2019 to 2023 on international labor trends and is referenced by numerous academic institutions worldwide for research purposes.", "output": {"entities": {"named_data": ["Labor Market Trends Report 2022", "Skills Development Assessment", "Global Employment Database"], "organization": ["National Bureau of Labor Statistics", "Economic Development Agency", "International Labor Organization", "local governments", "numerous academic institutions"], "acronym": ["NBSL", "EDA", "ILO", "GED"], "year": ["2022", "2021", "2019 to 2023"], "geography": ["Midwest region"]}, "relations": [{"has_organization": {"head": "Labor Market Trends Report 2022", "tail": "National Bureau of Labor Statistics"}}, {"used_by": {"head": "Labor Market Trends Report 2022", "tail": "Economic Development Agency"}}, {"has_organization": {"head": "Skills Development Assessment", "tail": "International Labor Organization"}}, {"used_by": {"head": "Skills Development Assessment", "tail": "local governments"}}, {"has_geography": {"head": "Skills Development Assessment", "tail": "Midwest region"}}, {"has_organization": {"head": "Global Employment Database", "tail": "numerous academic institutions"}}, {"has_timeframe": {"head": "Global Employment Database", "tail": "2019 to 2023"}}, {"has_acronym": {"head": "Labor Market Trends Report 2022", "tail": "NBSL"}}, {"has_acronym": {"head": "Skills Development Assessment", "tail": "SDA"}}, {"has_acronym": {"head": "Global Employment Database", "tail": "GED"}}, {"has_acronym": {"head": "International Labor Organization", "tail": "ILO"}}, {"has_acronym": {"head": "Economic Development Agency", "tail": "EDA"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "employment, labor markets, and skills development"}}
+{"input": "The recent findings from the Climate Resilience Assessment Report 2023 highlight the vital role of data in understanding and enhancing adaptive capacities among vulnerable communities. These insights were meticulously compiled and published by the International Institute for Climate Policy (IICP), which specializes in climate resilience studies. In addition to providing valuable research, the report has also been referenced by various NGOs working on disaster risk reduction initiatives globally.", "output": {"entities": {"named_data": ["Climate Resilience Assessment Report 2023"], "organization": ["International Institute for Climate Policy", "NGOs"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Climate Resilience Assessment Report 2023", "tail": "International Institute for Climate Policy"}}, {"used_by": {"head": "Climate Resilience Assessment Report 2023", "tail": "NGOs"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "The Global Manufacturing Index (GMI) 2020, published by the International Trade Organization, serves as a crucial benchmark for assessing industry performance across nations. This dataset is especially utilized by the Economic Analysis Bureau for their annual reports on global economic competitiveness. Additionally, the Trade and Employment Assessment Report (TEAR) from 2021, produced by the World Labor Council, explores the interplay between trade policies and employment rates in various countries, particularly in Southeast Asia. It has been heavily referenced by the Asian Development Bank in their studies on labor market dynamics. Both datasets provide valuable insights into the economic landscape, with TEAR focusing on the specific context of countries like Vietnam and Thailand.", "output": {"entities": {"named_data": ["Global Manufacturing Index", "Trade and Employment Assessment Report"], "organization": ["International Trade Organization", "Economic Analysis Bureau", "World Labor Council", "Asian Development Bank"], "acronym": ["GMI", "TEAR"], "year": ["2020", "2021"], "geography": ["Southeast Asia", "Vietnam", "Thailand"]}, "relations": [{"has_organization": {"head": "Global Manufacturing Index", "tail": "International Trade Organization"}}, {"used_by": {"head": "Global Manufacturing Index", "tail": "Economic Analysis Bureau"}}, {"has_timeframe": {"head": "Global Manufacturing Index", "tail": "2020"}}, {"has_organization": {"head": "Trade and Employment Assessment Report", "tail": "World Labor Council"}}, {"has_timeframe": {"head": "Trade and Employment Assessment Report", "tail": "2021"}}, {"has_geography": {"head": "Trade and Employment Assessment Report", "tail": "Southeast Asia"}}, {"used_by": {"head": "Trade and Employment Assessment Report", "tail": "Asian Development Bank"}}, {"has_geography": {"head": "Trade and Employment Assessment Report", "tail": "Vietnam"}}, {"has_geography": {"head": "Trade and Employment Assessment Report", "tail": "Thailand"}}, {"has_acronym": {"head": "Global Manufacturing Index", "tail": "GMI"}}, {"has_acronym": {"head": "Trade and Employment Assessment Report", "tail": "TEAR"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "The 2022 Digital Connectivity Report, published by the International Telecommunication Union (ITU), provides comprehensive insights into internet access across various regions. In turn, this report was utilized by the United Nations Development Programme (UNDP) to assess the impact of connectivity on educational outcomes in Sub-Saharan Africa. Additionally, the Youth Technology Adoption Survey (YTAS) conducted by TechAccess in 2021 highlights the digital skills gap among the youth in urban areas. This makes it a valuable resource for NGOs working in digital literacy initiatives, particularly in countries like Nigeria and Kenya.", "output": {"entities": {"named_data": ["Digital Connectivity Report", "Youth Technology Adoption Survey"], "organization": ["International Telecommunication Union", "United Nations Development Programme", "TechAccess"], "acronym": ["YTAS"], "year": ["2022", "2021"], "geography": ["Sub-Saharan Africa", "Nigeria", "Kenya"]}, "relations": [{"has_organization": {"head": "Digital Connectivity Report", "tail": "International Telecommunication Union"}}, {"used_by": {"head": "Digital Connectivity Report", "tail": "United Nations Development Programme"}}, {"has_timeframe": {"head": "Digital Connectivity Report", "tail": "2022"}}, {"has_geography": {"head": "Digital Connectivity Report", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Youth Technology Adoption Survey", "tail": "TechAccess"}}, {"used_by": {"head": "Youth Technology Adoption Survey", "tail": "NGOs"}}, {"has_timeframe": {"head": "Youth Technology Adoption Survey", "tail": "2021"}}, {"has_geography": {"head": "Youth Technology Adoption Survey", "tail": "Nigeria"}}, {"has_geography": {"head": "Youth Technology Adoption Survey", "tail": "Kenya"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "digital development and technology adoption"}}
+{"input": "The Environmental Sustainability Index (ESI) provides a comprehensive assessment of a country's commitment to sustainability practices. Published by the Global Environment Institute, the ESI utilizes data collected in 2022 to evaluate various environmental indicators across different regions. Through this index, policymakers can identify areas for improvement and track progress over time.", "output": {"entities": {"named_data": ["Environmental Sustainability Index", "ESI"], "organization": ["Global Environment Institute"], "acronym": ["ESI"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Environmental Sustainability Index", "tail": "Global Environment Institute"}}, {"has_acronym": {"head": "Environmental Sustainability Index", "tail": "ESI"}}, {"has_timeframe": {"head": "Environmental Sustainability Index", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
+{"input": "7 All data collection is done by GISSE a research institute in Bamako. The anonymized unit record data of the baseline and the monthly surveys can be downloaded from www. gisse. org. The response rate for the phone interviews has been very high (Table 1): after 6 rounds of monthly interviews the original sample is almost entirely intact. The low level of attrition demonstrates that mobile phone samples can be maintained over prolonged periods without being unduly affected by (non-random) respondent drop-out. 3. Characteristics of the Displaced and Returnee Population According to the 2009 population census, the two most sizeable ethnic groups in northern Mali are the Songhai (45 %) and Kel Tamasheq (32 %)-- see Table 2. The crisis brought about an ethnic divide, which is reflected in the composition of the three sub-samples. The majority of IDPs and returnees are Songhai (75 % and 71 % respectively), while the majority of refugees are Kel Tamasheq. Results suggest that the decision of where to flee was determined by ethnicity: Kel Tamasheq and Arabs left the country; Songhai fled towards Bamako.", "output": {"entities": {"named_data": ["2009 population census"], "organization": ["GISSE"]}, "relations": [{"has_organization": {"head": "2009 population census", "tail": "GISSE"}}]}, "_meta": {"entry_id": 1034, "dataset_name": "2009 population census"}}
+{"input": "wages and reduced over-education for migrants working in licensed jobs, while producing worse labour market outcomes for those who did not gain licensure.\nAccording to Peterson et al. (2014), over the 1973–2010 period, U.S. states with more stringent occupational licensing for migrant physicians received fewer new migrant physicians and struggled more with staffing shortages in healthcare. Aleksynska and Tritah (2013) quoted data that migrants in France were denied legal access to approximately 30% of jobs in the country.\n\nUkrainian refugees Polish citizens Source: Deloitte own elaboration based on mid-2024 SEIS UNHCR survey (Ukrainian refugees’ educational attainment), 2023 Eurostat Labour Force Survey Eurostat (Polish citizens educational attainment), and mid-2024 ZUS administrative data (occupational groups).\n\n**Chart 21. Share of regulated professions by citizenship and legal status, Q2 2024**\n\n**The educational premium seems**\n\n**to be lower for Ukrainian refugees**\n\n**compared to the general workforce**", "output": {"entities": {"named_data": ["2023 Eurostat Labour Force Survey"], "organization": ["Eurostat", "Deloitte"]}, "relations": [{"has_organization": {"head": "2023 Eurostat Labour Force Survey", "tail": "Eurostat"}}, {"used_by": {"head": "2023 Eurostat Labour Force Survey", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1266, "dataset_name": "2023 Eurostat Labour Force Survey"}}
+{"input": "2. Regulatory Environment and Governance\n\nbiometric and individual data of asylum-seekers in the UNHCR-managed refugee management database,\nensuring that this process remains distinct from the registration of an asylum application carried out strictly\nby CNARR.\n\nArticle 31 of the 2020 Law and Article 73 of the 2023 Decree outline that refugees are entitled by the\ncompetent authorities to be issued with civil status documents including birth certificates, death certificates\nand marriage certificates on par with nationals. Furthermore, the specific legal framework on civil status\n[documents consists of the National Civil Status Code and Law No. 008/PR/2013 of 10 May 2013, governing](http://citizenshiprightsafrica.org/wp-content/uploads/2020/11/Tchad-Loi-no-13-08-Etat-Civil-10-mai-2013.pdf)\ncivil status organizations in the Republic of Chad. Under Decree No. 660/PR/PM/MATSP/2015, establishing\nthe modalities of application of the Law of 10 May 2013, all births in Chad are subject to a mandatory\nregistration requirement. In line with this universal principle of civil registration, all foreigners, including\nrefugees and asylum-seekers to whom vital events have occurred in Chad, are allowed to benefit from civil\nregistry services on par with nationals. Additionally, [Ordinance No. 002/PR/2020](https://citizenshiprightsafrica.org/wp-content/uploads/2020/11/Tchad-Ordonnance-002-PR-2020-Etat-Civil-14-fevrier-2020.pdf) on the organization of civil\nstatus in the Republic of Chad has extended the registration delay for births to three months.", "output": {"entities": {"named_data": ["UNHCR-managed refugee management database"], "organization": ["UNHCR", "CNARR"]}, "relations": [{"has_organization": {"head": "UNHCR-managed refugee management database", "tail": "UNHCR"}}, {"used_by": {"head": "UNHCR-managed refugee management database", "tail": "CNARR"}}]}, "_meta": {"entry_id": 1043, "dataset_name": "UNHCR-managed refugee management database"}}
+{"input": "The recent analyses conducted by the Geospatial Insights Group utilized data from the Global Land Use Mapping Project (GLUMP) 2020, which was published by the International Remote Sensing Association. This dataset, focusing on land cover changes in Southeast Asia, provides critical insights into urban expansion and deforestation rates. In addition, the Environmental Monitoring Network leveraged the GLUMP data in their 2021 study on environmental sustainability initiatives across Indonesia. The collaboration emphasizes the importance of accurate land use data in informing policy decisions and enhancing resource management in the region.", "output": {"entities": {"named_data": ["Global Land Use Mapping Project", "GLUMP"], "organization": ["Geospatial Insights Group", "International Remote Sensing Association", "Environmental Monitoring Network"], "acronym": ["GLUMP"], "year": ["2020", "2021"], "geography": ["Southeast Asia", "Indonesia"]}, "relations": [{"has_organization": {"head": "Global Land Use Mapping Project", "tail": "International Remote Sensing Association"}}, {"used_by": {"head": "Global Land Use Mapping Project", "tail": "Geospatial Insights Group"}}, {"used_by": {"head": "GLUMP", "tail": "Environmental Monitoring Network"}}, {"has_timeframe": {"head": "Global Land Use Mapping Project", "tail": "2020"}}, {"has_timeframe": {"head": "GLUMP", "tail": "2021"}}, {"has_geography": {"head": "Global Land Use Mapping Project", "tail": "Southeast Asia"}}, {"has_geography": {"head": "GLUMP", "tail": "Indonesia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "The Digital Inclusion Survey 2022 (DIS-2022) offers valuable insights into technology adoption across various sectors in urban areas of Brazil. Conducted by the Brazilian Institute of Digital Studies, this dataset captures the trends of internet and mobile device usage among different demographic groups within the country. The findings from DIS-2022 are increasingly referenced by policy makers and researchers aiming to enhance digital equity in the region. Moreover, the survey spans a critical period of technological growth and social adaptation, making it a pivotal resource for ongoing studies in digital development.", "output": {"entities": {"named_data": ["Digital Inclusion Survey 2022", "DIS-2022"], "organization": ["Brazilian Institute of Digital Studies"], "acronym": ["DIS-2022"], "year": ["2022"], "geography": ["Brazil"]}, "relations": [{"has_acronym": {"head": "Digital Inclusion Survey 2022", "tail": "DIS-2022"}}, {"has_timeframe": {"head": "Digital Inclusion Survey 2022", "tail": "2022"}}, {"has_geography": {"head": "Digital Inclusion Survey 2022", "tail": "Brazil"}}, {"has_organization": {"head": "Digital Inclusion Survey 2022", "tail": "Brazilian Institute of Digital Studies"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "digital development and technology adoption"}}
+{"input": "ABBREVIATIONS AND ACRONYMS AFS Annual Financial Statement BERP Basic Education Recovery Project BESP Basic Education Support Project DA Designated Account DPs Development Partners ESA Education Sector Analysis ESCP Environmental and Social Commitment Plan ( ESSP Education Sector Strategic Plan ESPIG Education Sector Program Implementation Grant FM Financial Management GDP Gross Domestic Product GER Gross Enrollment Rate GOS Government of Sudan GPE Global Partnership for Education IDP Internally Displaced Person IFT Interim unaudited Financial Reports ISN Interim Strategy Note ISP Intermediary Support Provider MOE Ministry of Education MOFEP Ministry of Finance and Economic Planning NAC National Audit Chamber NER Net Enrollment Rate NHBPS National Household Budget and Poverty Survey NLA National Learning Assessment OOSC Out-of-School-Children PCU Project Coordination Unit PFS Project Financial Statements PDO Project Development Objective PPSD Project Procurement Strategy for Development PSC Project Steering Committee PTA Parents and Teachers Association PTR Pupil-teacher Ratio SDG Sudanese Pounds SOE Statement of Expenditures SRR Social Risk Rating SSA Sub-Saharan Africa UNICEF United Nations Children’s Fund USD United States Dollar WDR World Development Report", "output": {"entities": {"named_data": ["National Household Budget and Poverty Survey"], "organization": ["NHBPS"]}, "relations": [{"has_organization": {"head": "National Household Budget and Poverty Survey", "tail": "NHBPS"}}]}, "_meta": {"entry_id": 877, "dataset_name": "National Household Budget and Poverty Survey"}}
+{"input": "The countries that are the largest source of refugees include: Afghanistan, Syria, Somalia, South Sudan, Sudan, Democratic Republic of Congo, Central African Republic, Iraq, Myanmar, and Eritrea (UNHCR, 2017). These countries account for the vast majority of refugees worldwide. We will also consider three countries in Europe that are an important source of asylum seekers in Europe, and whose recognition rate is low, and account for many returnees – these are Kosovo, Serbia and Albania. Is return possible and safe, and if so, does the economic situation bode well to provide essentials, namely a job, housing, education and reliable public services? It is helpful to identify three groups of countries: ones that are still mired in war and / or high ‐ intensity civil conflicts, countries where medium ‐ intensity conflict persist and are already in the process of rebuilding, and countries which are not in conflict. According to the Armed Conflict Survey (IISS 2017), high ‐ intensity conflict is defined by frequent (daily) armed clashes between governments, government forces and insurgents, or among non ‐ state armed groups that control territory.", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "organization": ["IISS"]}, "relations": [{"has_organization": {"head": "Armed Conflict Survey", "tail": "IISS"}}]}, "_meta": {"entry_id": 956, "dataset_name": "Armed Conflict Survey"}}
+{"input": "In 2022, the Economic Competitiveness Survey (ECS) provided critical insights into the trade dynamics within Southeast Asia. Conducted by the Asian Development Bank (ADB), this dataset has been extensively utilized by the World Trade Organization (WTO) in their recent policy reports. The ECS aims to assess various economic factors over the 2020–2022 period, focusing primarily on countries like Thailand and Vietnam. Additionally, the Manufacturing Growth Review (MGR) 2021, published by the International Trade Centre (ITC), is instrumental in understanding sector-specific growth trends and is often cited by regional development agencies. Together, these datasets form a comprehensive overview of the economic landscape in the region, guiding policymakers and stakeholders alike.", "output": {"entities": {"named_data": ["Economic Competitiveness Survey", "ECS", "Manufacturing Growth Review", "MGR"], "organization": ["Asian Development Bank", "World Trade Organization", "International Trade Centre"], "acronym": ["ECS", "MGR"], "year": ["2022", "2020–2022", "2021"], "geography": ["Southeast Asia", "Thailand", "Vietnam"]}, "relations": [{"has_organization": {"head": "Economic Competitiveness Survey", "tail": "Asian Development Bank"}}, {"used_by": {"head": "Economic Competitiveness Survey", "tail": "World Trade Organization"}}, {"has_timeframe": {"head": "Economic Competitiveness Survey", "tail": "2020–2022"}}, {"has_geography": {"head": "Economic Competitiveness Survey", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Manufacturing Growth Review", "tail": "International Trade Centre"}}, {"used_by": {"head": "Manufacturing Growth Review", "tail": "regional development agencies"}}, {"has_timeframe": {"head": "Manufacturing Growth Review", "tail": "2021"}}, {"has_acronym": {"head": "Economic Competitiveness Survey", "tail": "ECS"}}, {"has_acronym": {"head": "Manufacturing Growth Review", "tail": "MGR"}}, {"has_geography": {"head": "Manufacturing Growth Review", "tail": "Thailand"}}, {"has_geography": {"head": "Manufacturing Growth Review", "tail": "Vietnam"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "Climate data came from two sources: US Defense Department satellites and weather station\nobservations. We relied on the satellite data for temperature observations and the ground station\ndata for interpolated precipitation observations (Mendelsohn et al. 2006). Soil data were obtained\nfrom the FAO digital soil map of the world CD ROM. The data was extrapolated to the district\nlevel using GIS (Geographical Information System). The dataset reports 116 dominant soil types.\n\nThese climate scenarios reflect the A1 scenarios in the IPCC's Special Report on Emissions Scenarios (SRES) (IPCC 2001) from the following models: Canadian Climate Center (CCC) (Boer et al.\n\nThese climate scenarios reflect the A1 scenarios in the IPCC's Special Report on Emissions Scenarios (SRES) (IPCC 2001) from the following models: Canadian Climate Center (CCC), Center for Climate System Research (CCSR), and Parallel Climate Model (PCM). For each climate scenario, we add the climate model's predicted change\n\n\n��\n\n\n\n<!-- PAGE 12 -->\nin temperature to the baseline temperature in each district. We also multiply the climate models\npredicted percentage change in precipitation by the baseline precipitation in each district or\nprovince. This gives us a new climate for every district in Africa.", "output": {"entities": {"named_data": ["FAO digital soil map of the world"], "organization": ["FAO", "Mendelsohn et al. 2006"]}, "relations": [{"has_organization": {"head": "FAO digital soil map of the world", "tail": "FAO"}}, {"used_by": {"head": "FAO digital soil map of the world", "tail": "Mendelsohn et al. 2006"}}]}, "_meta": {"entry_id": 924, "dataset_name": "FAO digital soil map of the world"}}
+{"input": "Malaita is one of largest of the Solomon Islands, with an estimated population of 170,883 (according to\n2012/13 HIES). The three primary roads of Malaita, namely the North Road (112.2 km), South Road (75.6\nkm) and East Road (41.7 km), together constitute nearly 60% of the road network on the island and carry\nthe majority of vehicular traffic (Figure 1). These roads connect 19 of the 33 wards and provide access to\n70% of the population.\n\nIt combines nationally representative pre- pandemic household survey data with follow-up phone survey data from Mali and exploits sub- national variation in the intensity of pandemic-related disruptions between urban and rural areas.\n\nWe combine nationally representative data collected between October 2018 and July 2019 with follow-up phone survey data collected between May and June 2020.", "output": {"entities": {"named_data": ["2012/13 HIES"], "organization": ["HIES"]}, "relations": [{"has_organization": {"head": "2012/13 HIES", "tail": "HIES"}}]}, "_meta": {"entry_id": 1000, "dataset_name": "2012/13 HIES"}}
+{"input": "The Urban Infrastructure and Transportation Assessment Report 2022 highlights the significant advancements made in public transportation systems across several metropolitan areas. This report, which covers the period from 2018 to 2022, provides a detailed analysis of infrastructure projects implemented in cities like New York, London, and Tokyo. By examining these case studies, analysts can better understand the impact of innovative transport solutions on urban mobility. The findings are crucial for policymakers and urban planners who are looking to enhance city connectivity and reduce congestion. Source: elaboration based on Urban Infrastructure Assessment Report (UIAR).", "output": {"entities": {"named_data": ["Urban Infrastructure and Transportation Assessment Report 2022", "Urban Infrastructure Assessment Report"], "organization": ["policymakers", "urban planners"], "acronym": ["Urban Infrastructure Assessment Report", "UIAR"], "year": ["2022", "2018 to 2022"], "geography": ["New York", "London", "Tokyo"]}, "relations": [{"has_acronym": {"head": "Urban Infrastructure Assessment Report", "tail": "UIAR"}}, {"has_timeframe": {"head": "Urban Infrastructure and Transportation Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Urban Infrastructure and Transportation Assessment Report 2022", "tail": "2018 to 2022"}}, {"has_geography": {"head": "Urban Infrastructure and Transportation Assessment Report 2022", "tail": "New York"}}, {"has_geography": {"head": "Urban Infrastructure and Transportation Assessment Report 2022", "tail": "London"}}, {"has_geography": {"head": "Urban Infrastructure and Transportation Assessment Report 2022", "tail": "Tokyo"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "urban infrastructure and transportation planning"}}
+{"input": "In terms of statistically insignificant effects, Block et al. (2004) examine the effect of Indonesia's drought and financial crisis of 1997-98 using time-age-cohort decomposition analyses employing high-frequency nutrition data.\n\nWith regards to social shocks, in Rwanda, Akresh et al. (2011) find that an additional month of exposure to civil war lowers child HAZ by 0.11 SD. Using qualitative data, the authors argue that this effect is due to increased theft of livestock or crops and exposure to water- and vector-borne diseases, with internal displacement driving this exposure to disease.\n\n7 Measurement error in consumption data could be behind this.\n\nWe use weather data taken from the _Terrestrial Air Temperature and Precipitation Version 4.01_ compiled by\n\nWe use recent global sub-national aggregated output data (Kummu, Taka and Guillaume 2018a) derived from", "output": {"entities": {"named_data": ["Terrestrial Air Temperature and Precipitation Version 4.01"], "organization": ["the authors"]}, "relations": [{"used_by": {"head": "Terrestrial Air Temperature and Precipitation Version 4.01", "tail": "the authors"}}]}, "_meta": {"entry_id": 161, "dataset_name": "Terrestrial Air Temperature and Precipitation Version 4.01"}}
+{"input": "The Climate Change Impact Assessment Report 2022, published by the Global Environmental Agency (GEA), provides a comprehensive overview of the anticipated effects of climate change on various ecosystems. This report, which focuses on data from multiple regions including Southeast Asia and Sub-Saharan Africa, has been extensively used by the United Nations Development Programme (UNDP) to inform their sustainable development initiatives. Furthermore, the Biodiversity Monitoring Database (BMD) managed by the Environmental Research Institute (ERI) offers crucial data from 2019 to 2021 regarding biodiversity changes across the same geographical areas, facilitating collaborative research with local universities and NGOs.", "output": {"entities": {"named_data": ["Climate Change Impact Assessment Report 2022", "Biodiversity Monitoring Database"], "organization": ["Global Environmental Agency", "United Nations Development Programme", "Environmental Research Institute"], "acronym": ["GEA", "UNDP", "BMD"], "year": ["2022", "2019 to 2021"], "geography": ["Southeast Asia", "Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Climate Change Impact Assessment Report 2022", "tail": "Global Environmental Agency"}}, {"used_by": {"head": "Climate Change Impact Assessment Report 2022", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Climate Change Impact Assessment Report 2022", "tail": "GEA"}}, {"has_timeframe": {"head": "Biodiversity Monitoring Database", "tail": "2019 to 2021"}}, {"has_organization": {"head": "Biodiversity Monitoring Database", "tail": "Environmental Research Institute"}}, {"used_by": {"head": "Biodiversity Monitoring Database", "tail": "United Nations Development Programme"}}, {"has_geography": {"head": "Biodiversity Monitoring Database", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Biodiversity Monitoring Database", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "environmental sustainability and natural resources"}}
+{"input": "The Women’s Economic Participation Survey (WEPS) conducted by the International Research Institute (IRI) in 2022 revealed significant barriers to women's access to the labor market in South Asia. This survey data was subsequently analyzed by the United Nations Development Programme (UNDP) to inform their upcoming initiatives aimed at promoting gender equality. Additionally, the Global Gender Equity Index (GGEI) compiled by the World Economic Forum provides annual insights on gender disparities across various regions, including Africa and Asia, with the latest report covering the years 2020–2022. Both the WEPS and GGEI datasets are pivotal for understanding the complexities surrounding women's economic empowerment globally.", "output": {"entities": {"named_data": ["Women’s Economic Participation Survey", "Global Gender Equity Index"], "organization": ["International Research Institute", "United Nations Development Programme", "World Economic Forum"], "acronym": ["WEPS", "GGEI"], "year": ["2022", "2020–2022"], "geography": ["South Asia", "Africa", "Asia"]}, "relations": [{"has_organization": {"head": "Women’s Economic Participation Survey", "tail": "International Research Institute"}}, {"used_by": {"head": "Women’s Economic Participation Survey", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Women’s Economic Participation Survey", "tail": "WEPS"}}, {"has_timeframe": {"head": "Women’s Economic Participation Survey", "tail": "2022"}}, {"has_organization": {"head": "Global Gender Equity Index", "tail": "World Economic Forum"}}, {"has_timeframe": {"head": "Global Gender Equity Index", "tail": "2020–2022"}}, {"has_geography": {"head": "Global Gender Equity Index", "tail": "Africa"}}, {"has_geography": {"head": "Global Gender Equity Index", "tail": "Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "gender equality and women's economic empowerment"}}
+{"input": "years (Figure ES.1). Even when asked where they would realistically be in the next three years, one-third of refugees believe that they will live in a Western country (Figure ES.2). The intention to migrate abroad is higher for youth. Refugees also perceive they have less control over their lives than hosts, a result driven by South Sudanese refugees. These intentions to migrate combined with low “locus of control” (LOC) may limit refugees’ investment in improving their livelihoods or to integrate. Welfare and Equity In-camp refugees are poorer than their hosts. While monetary poverty appears to be high in refugee- concentrated areas, it is more prevalent among in-camp refugees than their hosts or OCP refugees (Figure ES.3). Welfare varies significantly over the different groups of refugees in Ethiopia, with Eritrean refugees having the lowest poverty incidence and South Sudanese refugees the highest. Although poverty incidence is higher for refugees, the high 0 20 40 60 80 Percent 100 Eritrean (camps) Somali South Sudanese OCP All Refugees Ethiopian refugee camp Ethiopian city Country of birth Other African country Western country Figure ES.1: Desired location in three years Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 Percent 100", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank Staff"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 493, "dataset_name": "SESRE 2023"}}
+{"input": "The analysis of food security trends relies heavily on the Global Food Security Assessment (GFSA), which provides comprehensive data from 2015 to 2022. This dataset is essential for policymakers and researchers alike as it covers various geographical regions, including sub-Saharan Africa and Southeast Asia. The GFSA is published by the Food and Agriculture Organization (FAO), which ensures that the data is both reliable and robust for assessing food security challenges. Additionally, the National Agriculture Monitoring Survey (NAMS) offers insights into agricultural productivity in 2020, focusing specifically on the United States. While the NAMS does not have an acronym assigned, its findings are frequently cited by agricultural economists in their studies. The FAO's work in contributing to these datasets facilitates better understanding and responses to food insecurity issues.", "output": {"entities": {"named_data": ["Global Food Security Assessment", "National Agriculture Monitoring Survey"], "organization": ["Food and Agriculture Organization"], "acronym": ["GFSA"], "year": ["2015 to 2022", "2020"], "geography": ["sub-Saharan Africa", "Southeast Asia", "United States"]}, "relations": [{"has_acronym": {"head": "Global Food Security Assessment", "tail": "GFSA"}}, {"has_timeframe": {"head": "Global Food Security Assessment", "tail": "2015 to 2022"}}, {"has_geography": {"head": "Global Food Security Assessment", "tail": "sub-Saharan Africa"}}, {"has_geography": {"head": "Global Food Security Assessment", "tail": "Southeast Asia"}}, {"has_timeframe": {"head": "National Agriculture Monitoring Survey", "tail": "2020"}}, {"has_geography": {"head": "National Agriculture Monitoring Survey", "tail": "United States"}}, {"has_organization": {"head": "Global Food Security Assessment", "tail": "Food and Agriculture Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "agriculture, food security, and nutrition"}}
+{"input": "Recent evaluations of water conservation practices in rural areas have shown significant variations in effectiveness. The Environmental Impact Assessment Report 2022 highlighted the need for targeted interventions to enhance water management systems. Meanwhile, the Global Biodiversity Monitoring Database offers comprehensive insights into ecosystem health, providing valuable data for policymakers to formulate more sustainable practices.", "output": {"entities": {"named_data": ["Environmental Impact Assessment Report 2022", "Global Biodiversity Monitoring Database"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "environmental sustainability and natural resources"}}
+{"input": "The recent analysis conducted by the Economic Development Institute focused on the Public Revenue Optimization Study (PROS) published in 2022. This dataset offers invaluable insights into domestic revenue streams and was utilized by various stakeholders, including the Ministry of Finance. Furthermore, the National Taxation Trends Report (NTTR) produced by the Fiscal Policy Research Center provides a comprehensive overview of tax collection practices, which has been instrumental for policymakers in enhancing revenue generation strategies.", "output": {"entities": {"named_data": ["Public Revenue Optimization Study", "National Taxation Trends Report"], "organization": ["Economic Development Institute", "Ministry of Finance", "Fiscal Policy Research Center"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Public Revenue Optimization Study", "tail": "Economic Development Institute"}}, {"used_by": {"head": "Public Revenue Optimization Study", "tail": "Ministry of Finance"}}, {"has_organization": {"head": "National Taxation Trends Report", "tail": "Fiscal Policy Research Center"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "public financial management and domestic revenue"}}
+{"input": "observed in PISA data. Looking at graduates of pre-university technical education (mainly technical stream secondary education) one finds an interesting pattern comparing three specializations (Figure 1b). Gender disparities are high in Electronics and Automation, less in Software Development and were recently eliminated in Network and Database Design. The final example of Network and Database Design in Figure 1b shows that gender disparity can be overcome in a short period of time. Two recent studies provide a detailed general analysis of policy options. [18] For STEAM courses, the causes relate to: (i) enjoyment of reading and preference for mathematics; (ii) self-efficacy or belief in own capabilities, often reinforced by teachers; (iii) parental support or lack thereof due to low socioeconomic status; (iv) teacher preparation to deal effectively with diversity. 16 Source: Government Budget for 2024 and MEP School Census data from 2023. 17 Program-Based budgeting at a government-wide level is being implemented under the Fiscal Management Improvement Project (P172352, Loan 9075-CR), known as _Hacienda Digital_ . Investing in readiness to implement program-based budgeting at MEP, the biggest Ministry (in terms of budget and staff) is a priority for the Government of Costa Rica. 18 [Desigualdades por género en Primaria y Secundaria,", "output": {"entities": {"named_data": ["PISA data"], "organization": ["PISA", "Government of Costa Rica"]}, "relations": [{"has_organization": {"head": "PISA data", "tail": "PISA"}}, {"used_by": {"head": "PISA data", "tail": "Government of Costa Rica"}}]}, "_meta": {"entry_id": 1140, "dataset_name": "PISA data"}}
+{"input": "The 2022 Maternal Health Indicator Survey (MHIS) provides critical insights into reproductive health across Sub-Saharan Africa. Conducted by the African Health Organization, this dataset focuses on maternal outcomes, capturing data from multiple countries including Nigeria and Kenya. The survey results are expected to assist policymakers and health professionals in addressing health disparities among women. Researchers at various universities are utilizing the MHIS data to analyze trends in maternal healthcare services and their impact over the last five years.", "output": {"entities": {"named_data": ["Maternal Health Indicator Survey", "MHIS"], "organization": ["African Health Organization"], "acronym": ["MHIS"], "year": ["2022", "last five years"], "geography": ["Sub-Saharan Africa", "Nigeria", "Kenya"]}, "relations": [{"has_acronym": {"head": "Maternal Health Indicator Survey", "tail": "MHIS"}}, {"has_timeframe": {"head": "Maternal Health Indicator Survey", "tail": "2022"}}, {"has_geography": {"head": "Maternal Health Indicator Survey", "tail": "Sub-Saharan Africa"}}, {"has_geography": {"head": "Maternal Health Indicator Survey", "tail": "Nigeria"}}, {"has_geography": {"head": "Maternal Health Indicator Survey", "tail": "Kenya"}}, {"has_organization": {"head": "Maternal Health Indicator Survey", "tail": "African Health Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "health systems and maternal outcomes"}}
+{"input": "The Climate Adaptation Assessment Report 2022 (CAAR2022) provides critical insights into the impacts of climate change across various regions. This dataset, compiled by the International Climate Institute, covers the period from 2015 to 2021 and focuses on the Southeast Asia region. Additionally, the Urban Resilience Survey 2020 (URS2020) has been widely cited, especially by urban planners in developing countries, and it encompasses assessments from major cities in Africa. While the CAAR2022 highlights progress in climate resilience, the URS2020 offers a contrasting view, emphasizing the challenges faced in these urban settings. Both datasets are pivotal for understanding the broader implications of climate change initiatives.", "output": {"entities": {"named_data": ["Climate Adaptation Assessment Report 2022", "Urban Resilience Survey 2020"], "organization": ["International Climate Institute"], "acronym": ["CAAR2022", "URS2020"], "year": ["2022", "2015 to 2021", "2020"], "geography": ["Southeast Asia", "Africa"]}, "relations": [{"has_acronym": {"head": "Climate Adaptation Assessment Report 2022", "tail": "CAAR2022"}}, {"has_timeframe": {"head": "Climate Adaptation Assessment Report 2022", "tail": "2015 to 2021"}}, {"has_geography": {"head": "Climate Adaptation Assessment Report 2022", "tail": "Southeast Asia"}}, {"has_acronym": {"head": "Urban Resilience Survey 2020", "tail": "URS2020"}}, {"has_timeframe": {"head": "Urban Resilience Survey 2020", "tail": "2020"}}, {"has_geography": {"head": "Urban Resilience Survey 2020", "tail": "Africa"}}, {"has_organization": {"head": "Climate Adaptation Assessment Report 2022", "tail": "International Climate Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "climate resilience and disaster risk reduction"}}
+{"input": "The recent analysis of population dynamics was informed by the National Fertility Survey produced by the National Institute of Statistics. This dataset provides crucial insights into family planning and fertility rates across the country. Additionally, the Demographic Trends Report published by the Ministry of Health has been pivotal in guiding policy discussions. Both datasets were utilized by various research entities to better understand demographic shifts in urban and rural areas.", "output": {"entities": {"named_data": ["National Fertility Survey", "Demographic Trends Report"], "organization": ["National Institute of Statistics", "Ministry of Health"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "National Fertility Survey", "tail": "National Institute of Statistics"}}, {"has_organization": {"head": "Demographic Trends Report", "tail": "Ministry of Health"}}, {"used_by": {"head": "National Fertility Survey", "tail": "various research entities"}}, {"used_by": {"head": "Demographic Trends Report", "tail": "various research entities"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "demographics, fertility, and population growth"}}
+{"input": "The 2022 Coastal Ecosystem Assessment conducted by Oceana provides vital insights into marine biodiversity and is widely cited by environmental NGOs. This dataset, which focuses on the coastal regions of Southeast Asia, serves as a foundation for ongoing research. The World Resources Institute (WRI) has also published the Urban Sustainability Index (USI) 2021, which evaluates city-level sustainability practices across various global metropolises. This index has been employed by several city governments to benchmark their sustainability initiatives. Additionally, the Global Water Quality Database (GWQD), managed by the United Nations Environment Programme (UNEP), offers critical data on freshwater resources and is regularly referenced by academic institutions for environmental studies.", "output": {"entities": {"named_data": ["Coastal Ecosystem Assessment", "Urban Sustainability Index", "Global Water Quality Database"], "organization": ["Oceana", "World Resources Institute", "United Nations Environment Programme"], "acronym": ["USI", "GWQD"], "year": ["2022", "2021"], "geography": ["Southeast Asia"]}, "relations": [{"has_organization": {"head": "Coastal Ecosystem Assessment", "tail": "Oceana"}}, {"used_by": {"head": "Coastal Ecosystem Assessment", "tail": "environmental NGOs"}}, {"has_timeframe": {"head": "Coastal Ecosystem Assessment", "tail": "2022"}}, {"has_geography": {"head": "Coastal Ecosystem Assessment", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Urban Sustainability Index", "tail": "World Resources Institute"}}, {"used_by": {"head": "Urban Sustainability Index", "tail": "city governments"}}, {"has_timeframe": {"head": "Urban Sustainability Index", "tail": "2021"}}, {"has_acronym": {"head": "Urban Sustainability Index", "tail": "USI"}}, {"has_organization": {"head": "Global Water Quality Database", "tail": "United Nations Environment Programme"}}, {"used_by": {"head": "Global Water Quality Database", "tail": "academic institutions"}}, {"has_acronym": {"head": "Global Water Quality Database", "tail": "GWQD"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "environmental sustainability and natural resources"}}
+{"input": "The 2020 Labor Market Assessment for Argentina provides insightful data on employment trends and skill demands across various sectors. This extensive dataset, referred to as LMAA (Labor Market Assessment Argentina), covers the years 2015 to 2020, highlighting shifts in job availability and required qualifications. Meanwhile, the Skills Development Survey (SDS) conducted in 2021 focuses on the effectiveness of training programs within the workforce, specifically in urban regions of Brazil. Although the SDS does not include a specific acronym, it is a critical resource for policymakers aiming to improve labor market participation. Lastly, the 2019 Regional Employment Database (RED) offers comparative employment statistics for the entire Latin America and Caribbean region, showcasing the latest trends and challenges faced by labor markets in these areas. This dataset has been extensively utilized by the International Labor Organization (ILO) to inform its reports on regional employment policies.", "output": {"entities": {"named_data": ["Labor Market Assessment for Argentina", "LMAA", "Skills Development Survey", "SDS", "Regional Employment Database", "RED"], "organization": ["International Labor Organization", "ILO"], "acronym": ["LMAA", "SDS", "RED"], "year": ["2020", "2015 to 2020", "2021", "2019"], "geography": ["Argentina", "Brazil", "Latin America and Caribbean"]}, "relations": [{"has_acronym": {"head": "Labor Market Assessment for Argentina", "tail": "LMAA"}}, {"has_timeframe": {"head": "Labor Market Assessment for Argentina", "tail": "2015 to 2020"}}, {"has_geography": {"head": "Labor Market Assessment for Argentina", "tail": "Argentina"}}, {"has_timeframe": {"head": "Skills Development Survey", "tail": "2021"}}, {"has_geography": {"head": "Skills Development Survey", "tail": "Brazil"}}, {"has_acronym": {"head": "Regional Employment Database", "tail": "red"}}, {"has_timeframe": {"head": "Regional Employment Database", "tail": "2019"}}, {"has_geography": {"head": "Regional Employment Database", "tail": "Latin America and Caribbean"}}, {"used_by": {"head": "Regional Employment Database", "tail": "International Labor Organization"}}, {"has_organization": {"head": "Skills Development Survey", "tail": "International Labor Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "employment, labor markets, and skills development"}}
+{"input": "148 P. B. Spiegel & P. V. Le\n\nIntroduction\n\nThe human immunodeficiency virus (HIV) behavioural surveillance surveys\n(BSSs), an evolution from the knowledge-attitudes-practice surveys (KAPs), are\nan assessment, monitoring and evaluation tool designed to track trends in HIV/\nAIDS knowledge, attitudes and risk behaviour among populations. When used\ntogether with qualitative and quantitative research and proper measurement of\nappropriate programme indicators, the data collected from BSSs can assist\norganizations in targeting specific HIV/AIDS prevention and care activities,\nallocating scarce resources, and monitoring and evaluating the interventions’\neffectiveness and coverage. BSSs are useful because they alert policy makers and\nprogramme managers to emerging or changing risks in existing behaviour, reveal\ngaps in knowledge and attitudes, help to identify vulnerable segments of\npopulations, contribute to improved programme content, provide data on specific\ntarget groups and ensure compatibility and standardization of data collection\n(Family Health International 2000).\nThe core BSS indicators have been evolving over time (Table I). Until the\nUnited Nations General Assembly Special Session on HIV/AIDS (UNGASS)\nindicators were developed in 2002, there were no internationally-accepted HIV\nindicators. The UNGASS indicators were followed by the development of the\nMillennium Development Goal (MDG) indicators in 2003 and, subsequently,\nthe US President’s Emergency Preparedness Fund on AIDS Relief (PEPFAR)\nindicators in 2004. Although all of these indicators are similar to one another,\nthere are minor differences. Thus, it is difficult for persons implementing BSSs to\nchoose which indicators to use and complicated for others to compare studies\nwhich use different indicators. Furthermore, there are numerous other indicators\nthat can be used in BSSs depending upon the target groups and objectives of the\nsurvey.\nConflict, displacement, food insecurity and poverty have the potential to make\naffected populations more vulnerable to HIV transmission. The UNGASS\nDeclaration of Commitment on HIV/AIDS, states that ‘populations destabilised\nby armed conflict . . . including refugees, internally displaced persons, and in\nparticular women and children, are at increased risk of exposure to HIV infection’\n(United Nations General Assembly 2001). However, the common assumption\nthat this vulnerability necessarily translates into increased HIV infections and\nconsequently fuels the epidemic is not supported by data (Spiegel 2004). In the\nrecent past, HIV/AIDS interventions were generally not included by humanitarian organizations as part of their immediate response to conflict; HIV/AIDS was\nconsidered more of a developmental issue and not an immediate life threatening\ndisease such as malaria or cholera. However, thinking has evolved and it is now\ngenerally accepted that HIV/AIDS programmes must begin at the onset of a\nhumanitarian emergency, be multisectoral, and continue at every stage thereafter\n(Inter-Agency Standing Committee 2003). Furthermore, for refugees and\ninternally displaced persons (IDPs), HIV/AIDS programmes should be integrated", "output": {"entities": {"named_data": ["Millennium Development Goal (MDG) indicators"], "organization": ["humanitarian organizations"]}, "relations": [{"used_by": {"head": "Millennium Development Goal (MDG) indicators", "tail": "humanitarian organizations"}}]}, "_meta": {"entry_id": 118, "dataset_name": "Millennium Development Goal (MDG) indicators"}}
+{"input": "The 2022 Global Water Quality Assessment conducted by the Environmental Protection Agency (EPA) provides crucial insights into the status of freshwaters across the globe. This dataset, which has been widely referenced, is used by several organizations, including WaterAid, to inform their initiatives in developing countries. Furthermore, the Asia-Pacific Biodiversity Report 2021, published by the United Nations Environment Programme (UNEP), highlights the pressing issues related to biodiversity loss in the region. Data from this report is essential for researchers at the Asia-Pacific Regional Biodiversity Network (APRBN), who are working on preservation strategies in Asia and the Pacific.", "output": {"entities": {"named_data": ["2022 Global Water Quality Assessment", "Asia-Pacific Biodiversity Report 2021"], "organization": ["Environmental Protection Agency", "WaterAid", "United Nations Environment Programme", "Asia-Pacific Regional Biodiversity Network"], "acronym": ["EPA", "UNEP", "APRB"], "year": ["2022", "2021"], "geography": ["Asia", "Pacific", "developing countries", "the globe"]}, "relations": [{"has_organization": {"head": "2022 Global Water Quality Assessment", "tail": "Environmental Protection Agency"}}, {"used_by": {"head": "2022 Global Water Quality Assessment", "tail": "WaterAid"}}, {"has_acronym": {"head": "Environmental Protection Agency", "tail": "EPA"}}, {"has_timeframe": {"head": "2022 Global Water Quality Assessment", "tail": "2022"}}, {"has_organization": {"head": "Asia-Pacific Biodiversity Report 2021", "tail": "United Nations Environment Programme"}}, {"used_by": {"head": "Asia-Pacific Biodiversity Report 2021", "tail": "Asia-Pacific Regional Biodiversity Network"}}, {"has_acronym": {"head": "United Nations Environment Programme", "tail": "UNEP"}}, {"has_timeframe": {"head": "Asia-Pacific Biodiversity Report 2021", "tail": "2021"}}, {"has_geography": {"head": "Asia-Pacific Biodiversity Report 2021", "tail": "Asia"}}, {"has_geography": {"head": "Asia-Pacific Biodiversity Report 2021", "tail": "Pacific"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "environmental sustainability and natural resources"}}
+{"input": "In recent evaluations of labor market trends, the International Labour Organization (ILO) utilized data from the Global Employment Dynamics dataset to inform its policy recommendations. Similarly, the Organisation for Economic Co-operation and Development (OECD) has released findings based on the Skills Development Indicator report, which offers insights into workforce training needs across member countries.", "output": {"entities": {"named_data": ["Global Employment Dynamics dataset", "Skills Development Indicator report"], "organization": ["International Labour Organization", "Organisation for Economic Co-operation and Development"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Global Employment Dynamics dataset", "tail": "International Labour Organization"}}, {"used_by": {"head": "Global Employment Dynamics dataset", "tail": "International Labour Organization"}}, {"has_organization": {"head": "Skills Development Indicator report", "tail": "Organisation for Economic Co-operation and Development"}}, {"used_by": {"head": "Skills Development Indicator report", "tail": "Organisation for Economic Co-operation and Development"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "employment, labor markets, and skills development"}}
+{"input": "South Asia Middle East & North Africa Latin America & the Caribbean **Figure 3. Regional distribution of economic losses** ###### **III.4 Prior research on impacts of SLR on coastal wetlands** The papers most immediately related to this analysis are those of Nicholls at al. (1999), Nicholls (2004), McFadden et al. (2007) with estimates of exposure of coastal wetlands to SLR, and Tol (2007), Anthoff et al. (2010) on valuation of wetland losses due to SLR. It should be noted that Nicholls (1999) and Nicholls (2004) are based on wetland losses derived from the Global Vulnerability Analysis (Hoozemans et al. 1993) [23] - “a first-order perspective on wetland loss rates with incomplete coverage and wetland losses controlled only by tidal range and accommodation space” - as pointed out by McFadden et al. (2007). Nicholls et al. (1999) estimated that a 38 cm rise in global sea level from 1990 to the 2080s will lead to an approximate 22% loss of the coastal wetlands, [24] and 46% of the coastal wetlands would be lost if the sea level rises by 1 m. Nicholls (2004) further estimated losses of wetlands under various greenhouse emissions scenarios and found that a 34 cm rise in", "output": {"entities": {"named_data": ["Global Vulnerability Analysis"], "organization": ["Nicholls"]}, "relations": [{"used_by": {"head": "Global Vulnerability Analysis", "tail": "Nicholls"}}]}, "_meta": {"entry_id": 1138, "dataset_name": "Global Vulnerability Analysis"}}
+{"input": "The recent analysis of public financial management in East Africa employed data from the 2020 Regional Revenue Assessment (RRA) conducted by the Economic Commission for Africa (ECA). This dataset focuses on revenue collection mechanisms across the region and highlights practices that improve fiscal sustainability. The findings indicate a significant improvement in revenue mobilization since the implementation of reforms in 2015. Additionally, the study referenced the Domestic Revenue Monitoring System (DRMS) from 2019, which captures detailed revenue data for individual countries in the region, offering insights for policymakers. Both datasets are crucial for enhancing the understanding of fiscal policies in the area and are intended to guide future improvements in public finance management.", "output": {"entities": {"named_data": ["Regional Revenue Assessment", "Domestic Revenue Monitoring System"], "organization": ["Economic Commission for Africa"], "acronym": ["RRA", "DRMS"], "year": ["2020", "2015", "2019"], "geography": ["East Africa"]}, "relations": [{"has_acronym": {"head": "Regional Revenue Assessment", "tail": "RRA"}}, {"has_timeframe": {"head": "Regional Revenue Assessment", "tail": "2020"}}, {"has_geography": {"head": "Regional Revenue Assessment", "tail": "East Africa"}}, {"has_acronym": {"head": "Domestic Revenue Monitoring System", "tail": "DRMS"}}, {"has_timeframe": {"head": "Domestic Revenue Monitoring System", "tail": "2019"}}, {"has_timeframe": {"head": "Domestic Revenue Monitoring System", "tail": "2015"}}, {"has_geography": {"head": "Domestic Revenue Monitoring System", "tail": "East Africa"}}, {"has_organization": {"head": "Regional Revenue Assessment", "tail": "Economic Commission for Africa"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "public financial management and domestic revenue"}}
+{"input": "Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey.", "output": {"entities": {"named_data": ["Housing Census"], "organization": ["2009 Population and Housing Census", "Listening to Displaced People Survey"]}, "relations": [{"has_organization": {"head": "Housing Census", "tail": "2009 Population and Housing Census"}}, {"used_by": {"head": "Housing Census", "tail": "Listening to Displaced People Survey"}}]}, "_meta": {"entry_id": 1104, "dataset_name": "Housing Census"}}
+{"input": "market outcomes (Kotsadam and Tolonen 2016). Mining is also associated with more economic\n\n\nactivity measured by nightlights (Benshaul-Tolonen, 2019; Mamo et al, 2019).\n\n\nKotsadam and Tolonen (2016) use DHS data from Africa, and find that mine openings cause\n\nand GLSS with production data for 17 large-scale gold mines in Ghana. We find that a new\n\n\nlarge-scale gold mine changes economic outcomes, such as access to employment and cash\n\nincrease in total production that rose from 541,147 oz in 1990 to 3,119,823 oz in 2009 according\n\n\nofficial Ghana statistics (Bloch and Owusu, 2012). This production increase led to an increased\n\nSouth Africa\nTeberebie 1990 2005 Anglogold Ashanti South Africa\nWassa 1999 active Golden Star Resources USA\n_Source:_ InterraRMG 2013.\n_Note:_ Active is production status as of December 2012, the last available data point.", "output": {"entities": {"named_data": ["DHS data"], "organization": ["DHS", "Kotsadam and Tolonen"]}, "relations": [{"has_organization": {"head": "DHS data", "tail": "DHS"}}, {"used_by": {"head": "DHS data", "tail": "Kotsadam and Tolonen"}}]}, "_meta": {"entry_id": 287, "dataset_name": "DHS data"}}
+{"input": "Urban infrastructure development in metropolitan areas has become increasingly critical, particularly in light of rapid population growth. Recent observations highlight the need for effective transportation planning strategies. The Urban Transport Analysis Report 2023 provides valuable insights into traffic patterns, public transit usage, and the overall impact of urban mobility on quality of life. Stakeholders are encouraged to review this data as they devise comprehensive urban development policies.", "output": {"entities": {"named_data": ["Urban Transport Analysis Report 2023"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "urban infrastructure and transportation planning"}}
+{"input": "and more immediate impact on food access, we are able to find additional empirical support\n\nfor the last explanation. Given data and information constraints, it is not possible to estimate\n\nSundberg, R., and E. Melander. 2013. \"Introducing the UCDP Georeferenced Event Dataset.\"\n\nNotes: This table compares mobile phone ownership in the November 2017 WFP mobile phone survey and the 2014 Household Budget Survey (HBS), where the 2014 HBS summary statistics are restricted to the share of the population that resides in a household that owns at least one mobile phone.\n\nWe gratefully thank Claudio Montenegro, David Newhouse and Minh Nguyen for their help with the I2D2 database. We gratefully acknowledge the generous support of the World Bank (Office of the Senior Vice-President and Chief Economist and Social Urban Rural and Resilience Global Practice), the Cities Program of the International Growth Center (Grant number 89408), the GWU Institute for International Economic Policy and the GWU Center for International Business Education and Research.\n\nData Used to Estimate the Returns**   **Sources.** The data source for the analysis is the _International_ _Income_ _Distribution_   _Database_ (I2D2) of the World Bank. The database consists of a large number of   individual-level surveys and census samples.", "output": {"entities": {"named_data": ["I2D2 database"], "organization": ["World Bank", "Claudio Montenegro, David Newhouse and Minh Nguyen"]}, "relations": [{"has_organization": {"head": "I2D2 database", "tail": "World Bank"}}, {"used_by": {"head": "I2D2 database", "tail": "Claudio Montenegro, David Newhouse and Minh Nguyen"}}]}, "_meta": {"entry_id": 81, "dataset_name": "I2D2 database"}}
+{"input": "**employed before their displacement.**\nIn the SEIS survey, employment rates among refugees aged 18-64 previously employed or self-employed in Ukraine, are 81% and 91% respectively. That is already very high and any further increase would be marginal. On the other hand, those who back in Ukraine managed the household have an employment rate in Poland of 38%. Even before becoming refugees, they would have required support to enter the workforce and now, in the host country, they would be likely to benefit from such help even more. Some of them may be discouraged by not having been able to find a job, others may be marginally attached workers who fell outside the labour force, but have some desire and ability to return to work.\n\n**Chart 25. Ukrainian refugees’ employment rate in the 18-64 age group by previous**\n\n**status in Ukraine**\n\n91% Household Others Studying Employed Self-employed responsibilities Source: Deloitte own elaboration based on SEIS UNHCR survey conducted in May and June 2024.", "output": {"entities": {"named_data": ["SEIS survey"], "organization": ["UNHCR", "Deloitte"]}, "relations": [{"has_organization": {"head": "SEIS survey", "tail": "UNHCR"}}, {"used_by": {"head": "SEIS survey", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1301, "dataset_name": "SEIS survey"}}
+{"input": "According to the recent analysis, the findings from the National Education Assessment provide valuable insights into student performance across multiple subjects. Additionally, the School Enrollment Trends report highlights significant changes in enrollment rates in various regions over the past decade.", "output": {"entities": {"named_data": ["National Education Assessment", "School Enrollment Trends report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The Water Quality Assessment Report 2022 (WQAR) reveals crucial insights into the state of freshwater resources across Sub-Saharan Africa. Published by the African Water Agency (AWA), this report provides baseline data that has been essential for policymakers. In addition, the United Nations Development Programme (UNDP) utilized this dataset to formulate its initiatives focused on improving water access in the region. Moreover, the Sanitation and Hygiene Survey 2021 (SHS2021), released by the Global Health Initiative (GHI), highlights the sanitation challenges faced by urban populations. This dataset has been actively referenced by the World Health Organization (WHO) in its ongoing public health campaigns aimed at promoting hygiene practices and reducing disease transmission.", "output": {"entities": {"named_data": ["Water Quality Assessment Report 2022", "Sanitation and Hygiene Survey 2021"], "organization": ["African Water Agency", "United Nations Development Programme", "Global Health Initiative", "World Health Organization"], "acronym": ["WQAR", "SHS2021"], "year": ["2022", "2021"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Water Quality Assessment Report 2022", "tail": "African Water Agency"}}, {"used_by": {"head": "Water Quality Assessment Report 2022", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Water Quality Assessment Report 2022", "tail": "WQAR"}}, {"has_timeframe": {"head": "Water Quality Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Water Quality Assessment Report 2022", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Sanitation and Hygiene Survey 2021", "tail": "Global Health Initiative"}}, {"used_by": {"head": "Sanitation and Hygiene Survey 2021", "tail": "World Health Organization"}}, {"has_acronym": {"head": "Sanitation and Hygiene Survey 2021", "tail": "SHS2021"}}, {"has_timeframe": {"head": "Sanitation and Hygiene Survey 2021", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "water, sanitation, and hygiene"}}
+{"input": "The Urban Mobility Assessment Report 2022, published by the Global Transport Institute (GTI), provides critical insights into transportation patterns across urban areas in Southeast Asia. This dataset is extensively used by the Southeast Asian Development Bank (SEADB) for planning infrastructure improvements. Additionally, the Metropolitan Traffic Survey (MTS) conducted by the City Planning Agency in 2021 offers detailed traffic flow data, which is used by various municipalities to enhance urban traffic management. The MTS dataset, along with the Urban Mobility Assessment Report, is crucial for the development of sustainable transportation strategies in the region.", "output": {"entities": {"named_data": ["Urban Mobility Assessment Report 2022", "Metropolitan Traffic Survey", "Urban Mobility Assessment Report"], "organization": ["Global Transport Institute", "Southeast Asian Development Bank", "City Planning Agency"], "acronym": ["GTI", "SEADB", "MTS"], "year": ["2022", "2021"], "geography": ["Southeast Asia"]}, "relations": [{"has_organization": {"head": "Urban Mobility Assessment Report 2022", "tail": "Global Transport Institute"}}, {"used_by": {"head": "Urban Mobility Assessment Report 2022", "tail": "Southeast Asian Development Bank"}}, {"has_timeframe": {"head": "Urban Mobility Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Urban Mobility Assessment Report 2022", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Metropolitan Traffic Survey", "tail": "City Planning Agency"}}, {"used_by": {"head": "Metropolitan Traffic Survey", "tail": "various municipalities"}}, {"has_timeframe": {"head": "Metropolitan Traffic Survey", "tail": "2021"}}, {"has_acronym": {"head": "Metropolitan Traffic Survey", "tail": "MTS"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "urban infrastructure and transportation planning"}}
+{"input": "Indeed, despite the recent literature rejecting the conflictive impact of refugees in hosting areas (Zhou and Shaver, 2021), the magnitude of our coefficients might be explained by the confounding presence of refugees. Columns (5) and (6) further introduce climatic controls. Column (6) corresponds to Equation 1 and refers to our benchmark specification. Columns (1) and (2) show that without incorporating the changes in ethnic diversity induced by refugees we would not be able to identify a relationship between diversity and violent conflicts. In column (3), the revised refugee fractionalization index has a negative and significant coefficient, while the revised refugee polarization index has a positive and significant effect on the incidence of violent conflicts. In columns (2), (4), and (6), our coefficients of interest are of the same order of magnitude when the number of refugees is controlled for. Our results are not altered by incorporating rainfall and temperature anomalies (columns (5) and (6)), but the estimates become slightly more precise. 21", "output": {"entities": {"named_data": ["refugee fractionalization index"], "organization": ["Zhou and Shaver"]}, "relations": [{"used_by": {"head": "refugee fractionalization index", "tail": "Zhou and Shaver"}}]}, "_meta": {"entry_id": 193, "dataset_name": "refugee fractionalization index"}}
+{"input": "The Energy Access Database 2022, published by the International Renewable Energy Agency (IRENA), provides comprehensive data on global electricity access. The data, covering the years 2018-2021, has been extensively used by various stakeholders, including the United Nations Development Programme (UNDP) for their Sustainable Energy for All initiative. Additionally, the Renewable Energy Transition Assessment Report (RETAR) from 2023 offers insights into renewable energy adoption across Sub-Saharan Africa, enabling organizations like the African Development Bank (AfDB) to tailor their strategies accordingly. Furthermore, the Clean Energy Progress Report 2021, created by the Global Energy Forum, is utilized by environmental NGOs to advocate for policy changes in energy sectors worldwide.", "output": {"entities": {"named_data": ["Energy Access Database 2022", "Renewable Energy Transition Assessment Report", "RETAR", "Clean Energy Progress Report 2021"], "organization": ["International Renewable Energy Agency", "United Nations Development Programme", "African Development Bank", "Global Energy Forum"], "acronym": ["IRENA", "UNDP", "RETAR", "AfDB"], "year": ["2022", "2018-2021", "2023", "2021"], "geography": ["Sub-Saharan Africa", "worldwide"]}, "relations": [{"has_organization": {"head": "Energy Access Database 2022", "tail": "International Renewable Energy Agency"}}, {"used_by": {"head": "Energy Access Database 2022", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Energy Access Database 2022", "tail": "IRENA"}}, {"has_timeframe": {"head": "Energy Access Database 2022", "tail": "2018-2021"}}, {"has_organization": {"head": "Renewable Energy Transition Assessment Report", "tail": "African Development Bank"}}, {"used_by": {"head": "Renewable Energy Transition Assessment Report", "tail": "African Development Bank"}}, {"has_acronym": {"head": "Renewable Energy Transition Assessment Report", "tail": "RETAR"}}, {"has_organization": {"head": "Clean Energy Progress Report 2021", "tail": "Global Energy Forum"}}, {"used_by": {"head": "Clean Energy Progress Report 2021", "tail": "environmental NGOs"}}, {"has_timeframe": {"head": "Clean Energy Progress Report 2021", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "energy access and renewable transitions"}}
+{"input": "The 2022 Renewable Energy Transition Report published by the International Energy Agency (IEA) provides valuable insights into energy access trends across Sub-Saharan Africa. This report is heavily cited by the World Resources Institute (WRI), which utilizes the findings to advocate for policy changes that enhance sustainable energy solutions. Additionally, the World Bank's Energy Access Database, which covers data from 2019 to 2021, is instrumental for various stakeholders, including the African Development Bank (AfDB), in shaping investment strategies aimed at expanding energy access throughout the region.", "output": {"entities": {"named_data": ["2022 Renewable Energy Transition Report", "Energy Access Database"], "organization": ["International Energy Agency", "World Resources Institute", "World Bank", "African Development Bank"], "acronym": ["IEA", "WRI", "AfDB"], "year": ["2022", "2019 to 2021"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "2022 Renewable Energy Transition Report", "tail": "International Energy Agency"}}, {"used_by": {"head": "2022 Renewable Energy Transition Report", "tail": "World Resources Institute"}}, {"has_acronym": {"head": "International Energy Agency", "tail": "IEA"}}, {"has_timeframe": {"head": "Energy Access Database", "tail": "2019 to 2021"}}, {"has_organization": {"head": "Energy Access Database", "tail": "World Bank"}}, {"used_by": {"head": "Energy Access Database", "tail": "African Development Bank"}}, {"has_geography": {"head": "Energy Access Database", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "energy access and renewable transitions"}}
+{"input": "The Urban Land Use Mapping Dataset provides comprehensive insights into land utilization patterns across various cities. Produced by the Global Institute for Urban Studies, this dataset enhances our understanding of urban development and supports policymakers in making informed decisions. The dataset is instrumental for researchers focusing on geospatial analysis and offers a detailed snapshot of land use as of 2022.", "output": {"entities": {"named_data": ["Urban Land Use Mapping Dataset"], "organization": ["Global Institute for Urban Studies"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Urban Land Use Mapping Dataset", "tail": "Global Institute for Urban Studies"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
+{"input": "The Gender Equality and Economic Empowerment Survey (GEEES) conducted in 2022 provides vital insights into women's participation in the labor market across East Africa. This dataset, published by the International Labor Organization (ILO), offers a comprehensive assessment of employment trends and challenges faced by women. Additionally, the Women’s Financial Inclusion Index (WFII) for the years 2020-2021 highlights the barriers women encounter in accessing financial services in low-income countries. This index has been instrumental for researchers at the United Nations Development Programme (UNDP) who focus on promoting gender equality. Lastly, the 2020 Gender and Development Assessment Report reviews various socio-economic factors influencing women's empowerment in South Asia.", "output": {"entities": {"named_data": ["Gender Equality and Economic Empowerment Survey", "Women’s Financial Inclusion Index", "Gender and Development Assessment Report"], "organization": ["International Labor Organization", "United Nations Development Programme"], "acronym": ["GEEES", "WFII"], "year": ["2022", "2020-2021", "2020"], "geography": ["East Africa", "low-income countries", "South Asia"]}, "relations": [{"has_acronym": {"head": "Gender Equality and Economic Empowerment Survey", "tail": "GEEES"}}, {"has_timeframe": {"head": "Gender Equality and Economic Empowerment Survey", "tail": "2022"}}, {"has_geography": {"head": "Gender Equality and Economic Empowerment Survey", "tail": "East Africa"}}, {"has_acronym": {"head": "Women’s Financial Inclusion Index", "tail": "WFII"}}, {"has_timeframe": {"head": "Women’s Financial Inclusion Index", "tail": "2020-2021"}}, {"has_geography": {"head": "Women’s Financial Inclusion Index", "tail": "low-income countries"}}, {"used_by": {"head": "Women’s Financial Inclusion Index", "tail": "United Nations Development Programme"}}, {"has_timeframe": {"head": "Gender and Development Assessment Report", "tail": "2020"}}, {"has_geography": {"head": "Gender and Development Assessment Report", "tail": "South Asia"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "gender equality and women's economic empowerment"}}
+{"input": "The Global Refugee Assessment 2022, published by the United Nations High Commissioner for Refugees (UNHCR), highlights the urgent needs of displaced populations worldwide, particularly in regions like Sub-Saharan Africa. In a comprehensive analysis, Mercy Corps utilized the findings from this assessment to inform their programmatic strategies in refugee camps across Uganda. Similarly, the Refugee Education Dataset 2021, created by the International Organization for Migration (IOM), has been instrumental in guiding educational initiatives for refugee children. This dataset, cited by various NGOs, particularly in Middle Eastern countries, emphasizes the critical need for educational support in these regions.", "output": {"entities": {"named_data": ["Global Refugee Assessment 2022", "Refugee Education Dataset 2021"], "organization": ["United Nations High Commissioner for Refugees", "Mercy Corps", "International Organization for Migration"], "acronym": [], "year": ["2022", "2021"], "geography": ["Sub-Saharan Africa", "Uganda", "Middle Eastern countries"]}, "relations": [{"has_organization": {"head": "Global Refugee Assessment 2022", "tail": "United Nations High Commissioner for Refugees"}}, {"used_by": {"head": "Global Refugee Assessment 2022", "tail": "Mercy Corps"}}, {"has_organization": {"head": "Refugee Education Dataset 2021", "tail": "International Organization for Migration"}}, {"used_by": {"head": "Refugee Education Dataset 2021", "tail": "various NGOs"}}, {"has_geography": {"head": "Global Refugee Assessment 2022", "tail": "Sub-Saharan Africa"}}, {"has_geography": {"head": "Refugee Education Dataset 2021", "tail": "Middle Eastern countries"}}, {"has_geography": {"head": "Refugee Education Dataset 2021", "tail": "Uganda"}}, {"has_timeframe": {"head": "Global Refugee Assessment 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Refugee Education Dataset 2021", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "forced displacement, refugees, and migration"}}
+{"input": "To inform urban planning strategies, the Sustainable Cities Assessment Report 2022 has been instrumental in guiding policymakers in metropolitan areas. Produced by the Urban Development Institute (UDI), this report provides comprehensive insights into infrastructure needs and growth projections. The data within this report is extensively used by the City Planning Agency (CPA) to develop targeted transportation initiatives. Additionally, the Institute for Urban Studies (IUS) has produced the Urban Mobility Survey (UMS), which examines travel patterns in various cities. The findings from the UMS, published in 2021, are utilized by regional transport authorities to enhance public transit systems in their jurisdictions. Both datasets serve as critical resources for urban infrastructure improvement efforts.", "output": {"entities": {"named_data": ["Sustainable Cities Assessment Report 2022", "Urban Mobility Survey"], "organization": ["Urban Development Institute", "City Planning Agency", "Institute for Urban Studies", "regional transport authorities"], "acronym": ["Urban Mobility Survey"], "year": ["2022", "2021"], "geography": ["metropolitan areas", "various cities"]}, "relations": [{"has_organization": {"head": "Sustainable Cities Assessment Report 2022", "tail": "Urban Development Institute"}}, {"used_by": {"head": "Sustainable Cities Assessment Report 2022", "tail": "City Planning Agency"}}, {"has_timeframe": {"head": "Sustainable Cities Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Sustainable Cities Assessment Report 2022", "tail": "metropolitan areas"}}, {"has_organization": {"head": "Urban Mobility Survey", "tail": "Institute for Urban Studies"}}, {"used_by": {"head": "Urban Mobility Survey", "tail": "regional transport authorities"}}, {"has_timeframe": {"head": "Urban Mobility Survey", "tail": "2021"}}, {"has_geography": {"head": "Urban Mobility Survey", "tail": "various cities"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "urban infrastructure and transportation planning"}}
+{"input": "The recent Urban Employment Trends Survey (UETS) conducted in 2022 provides comprehensive insights into labor market dynamics in Ghana. This dataset was published by the Ghana Statistical Service (GSS), which is renowned for its rigorous economic data collection. In an effort to assess educational outcomes linked to employment, UNICEF has utilized the UETS data in their latest report on youth skills development. Furthermore, the West African Labor Market Assessment (WALMA) released in 2023 showcases trends across multiple countries in the region, including Nigeria and Ivory Coast. This assessment has been instrumental for policy makers, particularly the Economic Community of West African States (ECOWAS), which regularly references WALMA data for economic planning and intervention strategies.", "output": {"entities": {"named_data": ["Urban Employment Trends Survey", "UETS", "West African Labor Market Assessment", "WALMA"], "organization": ["Ghana Statistical Service", "UNICEF", "Economic Community of West African States"], "acronym": ["UETS", "WALMA"], "year": ["2022", "2023"], "geography": ["Ghana", "Nigeria", "Ivory Coast"]}, "relations": [{"has_organization": {"head": "Urban Employment Trends Survey", "tail": "Ghana Statistical Service"}}, {"used_by": {"head": "Urban Employment Trends Survey", "tail": "UNICEF"}}, {"has_acronym": {"head": "Urban Employment Trends Survey", "tail": "UETS"}}, {"has_timeframe": {"head": "Urban Employment Trends Survey", "tail": "2022"}}, {"has_organization": {"head": "West African Labor Market Assessment", "tail": "Economic Community of West African States"}}, {"has_acronym": {"head": "West African Labor Market Assessment", "tail": "WALMA"}}, {"has_timeframe": {"head": "West African Labor Market Assessment", "tail": "2023"}}, {"has_geography": {"head": "West African Labor Market Assessment", "tail": "Nigeria"}}, {"has_geography": {"head": "West African Labor Market Assessment", "tail": "Ivory Coast"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "employment, labor markets, and skills development"}}
+{"input": "Analyzing maternal health outcomes in Southeast Asia, the 2020 Maternal Health Assessment Report (MHAR) was published by the ASEAN Health Organization. This report provides critical insights into the region's healthcare systems and is extensively utilized by the World Health Foundation for policy formulation. Furthermore, the Bangladesh Maternal Health Survey (BMHS) 2019 delivers detailed data on maternal care practices and is cited frequently by researchers at the Global Health Institute. This data, alongside the Laos Health System Database 2021, which was also released by the ASEAN Health Organization, underscores significant regional disparities in maternal healthcare access and outcomes.", "output": {"entities": {"named_data": ["Maternal Health Assessment Report", "Bangladesh Maternal Health Survey", "Laos Health System Database"], "organization": ["ASEAN Health Organization", "World Health Foundation", "Global Health Institute"], "acronym": ["Maternal Health Assessment Report", "BMHS", "Laos Health System"], "year": ["2020", "2019", "2021"], "geography": ["Southeast Asia", "Bangladesh", "Laos"]}, "relations": [{"has_organization": {"head": "Maternal Health Assessment Report", "tail": "ASEAN Health Organization"}}, {"used_by": {"head": "Maternal Health Assessment Report", "tail": "World Health Foundation"}}, {"has_acronym": {"head": "Maternal Health Assessment Report", "tail": "MHAR"}}, {"has_timeframe": {"head": "Maternal Health Assessment Report", "tail": "2020"}}, {"has_geography": {"head": "Maternal Health Assessment Report", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Bangladesh Maternal Health Survey", "tail": "Global Health Institute"}}, {"has_timeframe": {"head": "Bangladesh Maternal Health Survey", "tail": "2019"}}, {"has_geography": {"head": "Bangladesh Maternal Health Survey", "tail": "Bangladesh"}}, {"has_organization": {"head": "Laos Health System Database", "tail": "ASEAN Health Organization"}}, {"has_timeframe": {"head": "Laos Health System Database", "tail": "2021"}}, {"has_geography": {"head": "Laos Health System Database", "tail": "Laos"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "health systems and maternal outcomes"}}
+{"input": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n**Chart 15. Ukrainian refugees wages median net wage by age group**\n\nMonthly net wage (PLN) Percengate of all workers total economy average Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n**Chart 16. Median net wages of Ukrainian refugees median net wage by sector**\n\nin PLN Percentage of total economy average 126% 15 to 24 25 to 34 35 to 44 45 to 54 55 to 64 15 to 24 25 to 34 35 to 44 45 to 54 55 to 64 Ukrainian refugees All workers Source: Deloitte own elaboration based on SEIS UNHCR survey and GUS data.\n\n**The Ukrainian refugee groups to earn**\n\n**the highest wages compared to the**\n\n**wages in the economy as a whole are**", "output": {"entities": {"named_data": ["SEIS UNHCR survey"], "organization": ["UNHCR", "Deloitte"]}, "relations": [{"has_organization": {"head": "SEIS UNHCR survey", "tail": "UNHCR"}}, {"used_by": {"head": "SEIS UNHCR survey", "tail": "Deloitte"}}]}, "_meta": {"entry_id": 1316, "dataset_name": "SEIS UNHCR survey"}}
+{"input": "The Renewable Energy Access Survey (REAS) conducted by the Global Energy Institute in 2022 provides critical insights into energy utilization in sub-Saharan Africa. The data from this survey is extensively used by local NGOs such as Energy for All to design effective community programs. Furthermore, the Urban Energy Transition Assessment Report (UETAR) published in 2023 by the Urban Development Agency focuses on energy transition strategies in urban areas worldwide. This assessment is cited by the International Renewable Energy Agency (IRENA) to inform policy recommendations in various countries.", "output": {"entities": {"named_data": ["Renewable Energy Access Survey", "Urban Energy Transition Assessment Report"], "organization": ["Global Energy Institute", "Energy for All", "Urban Development Agency", "International Renewable Energy Agency"], "acronym": ["REAS", "UETAR", "IRENA"], "year": ["2022", "2023"], "geography": ["sub-Saharan Africa", "urban areas", "various countries"]}, "relations": [{"has_organization": {"head": "Renewable Energy Access Survey", "tail": "Global Energy Institute"}}, {"used_by": {"head": "Renewable Energy Access Survey", "tail": "Energy for All"}}, {"has_organization": {"head": "Urban Energy Transition Assessment Report", "tail": "Urban Development Agency"}}, {"used_by": {"head": "Urban Energy Transition Assessment Report", "tail": "International Renewable Energy Agency"}}, {"has_acronym": {"head": "Renewable Energy Access Survey", "tail": "REAS"}}, {"has_acronym": {"head": "Urban Energy Transition Assessment Report", "tail": "UETAR"}}, {"has_acronym": {"head": "International Renewable Energy Agency", "tail": "IRENA"}}, {"has_timeframe": {"head": "Renewable Energy Access Survey", "tail": "2022"}}, {"has_timeframe": {"head": "Urban Energy Transition Assessment Report", "tail": "2023"}}, {"has_geography": {"head": "Renewable Energy Access Survey", "tail": "sub-Saharan Africa"}}, {"has_geography": {"head": "Urban Energy Transition Assessment Report", "tail": "urban areas"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "energy access and renewable transitions"}}
+{"input": "The 2020 Southeast Asia Environmental Survey, conducted by the Asian Development Bank, provides valuable insights into regional sustainability efforts. The dataset, commonly referred to as SEAES, covers key data points from 2018 to 2020 across multiple countries in the region, including Thailand, Vietnam, and Indonesia. Additionally, the Urban Green Spaces Database (UGSD) 2019 includes data specifically for urban areas in various cities, highlighting the impact of green initiatives on biodiversity. These datasets have been instrumental for researchers and policymakers looking to enhance environmental resilience in Southeast Asia.", "output": {"entities": {"named_data": ["Southeast Asia Environmental Survey", "Urban Green Spaces Database"], "organization": ["Asian Development Bank"], "acronym": ["SEAES", "UGSD"], "year": ["2020", "2018 to 2020", "2019"], "geography": ["Southeast Asia", "Thailand", "Vietnam", "Indonesia"]}, "relations": [{"has_acronym": {"head": "Southeast Asia Environmental Survey", "tail": "SEAES"}}, {"has_timeframe": {"head": "Southeast Asia Environmental Survey", "tail": "2018 to 2020"}}, {"has_geography": {"head": "Southeast Asia Environmental Survey", "tail": "Southeast Asia"}}, {"has_acronym": {"head": "Urban Green Spaces Database", "tail": "UGSD"}}, {"has_timeframe": {"head": "Urban Green Spaces Database", "tail": "2019"}}, {"has_geography": {"head": "Urban Green Spaces Database", "tail": "Thailand"}}, {"has_geography": {"head": "Urban Green Spaces Database", "tail": "Vietnam"}}, {"has_geography": {"head": "Urban Green Spaces Database", "tail": "Indonesia"}}, {"has_organization": {"head": "Southeast Asia Environmental Survey", "tail": "Asian Development Bank"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "environmental sustainability and natural resources"}}
+{"input": "23 86 % of the IDPs, 91 % of the refugees and 88 % of the returnees are confident or fully confident that a coalition like this would be capable of providing security. Source: Listening to Displaced People Survey, 2014. In an open-ended question on who can be trusted most when it comes to ensuring security in the North, survey results suggest that the majority of refugees in Mauritania (86 %) trust the armed rebel groups as opposed to the army or police. This does not hold for refugees in Niger of whom 75 % trust the army and police. Similar results hold for IDPs and returnees, who put much more confidence in state authorities when it comes to securing the North: most trust is placed in the army and police (72 % of the IDPs and 66 % of the returnees) while little to no trust is placed in armed rebel groups (3 % of IDPs, 1 % of returnees).", "output": {"entities": {"named_data": ["Displaced People Survey"], "organization": ["Listening to Displaced People Survey"]}, "relations": [{"has_organization": {"head": "Displaced People Survey", "tail": "Listening to Displaced People Survey"}}]}, "_meta": {"entry_id": 124, "dataset_name": "Displaced People Survey"}}
+{"input": "The recent Water Resource Assessment Report 2022, published by the International Water Management Institute (IWMI), provides critical insights into the status of freshwater resources across various regions. This dataset is being extensively used by the Environmental Protection Agency (EPA) for their ongoing research on water quality improvements in North America. Furthermore, the Biodiversity Monitoring Database 2021, developed by the Global Biodiversity Institute (GBI), offers extensive data that is integral to the EPA's assessments of ecosystem health in the same region. Both datasets are crucial for enabling informed decision-making in environmental policy development.", "output": {"entities": {"named_data": ["Water Resource Assessment Report 2022", "Biodiversity Monitoring Database 2021"], "organization": ["International Water Management Institute", "Environmental Protection Agency", "Global Biodiversity Institute"], "acronym": ["IWMI", "EPA", "GBI"], "year": ["2022", "2021"], "geography": ["North America"]}, "relations": [{"has_organization": {"head": "Water Resource Assessment Report 2022", "tail": "International Water Management Institute"}}, {"used_by": {"head": "Water Resource Assessment Report 2022", "tail": "Environmental Protection Agency"}}, {"has_organization": {"head": "Biodiversity Monitoring Database 2021", "tail": "Global Biodiversity Institute"}}, {"used_by": {"head": "Biodiversity Monitoring Database 2021", "tail": "Environmental Protection Agency"}}, {"has_timeframe": {"head": "Water Resource Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Biodiversity Monitoring Database 2021", "tail": "2021"}}, {"has_geography": {"head": "Water Resource Assessment Report 2022", "tail": "North America"}}, {"has_geography": {"head": "Biodiversity Monitoring Database 2021", "tail": "North America"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "environmental sustainability and natural resources"}}
+{"input": "The 2020 Africa Technology Adoption Survey (ATAS) revealed significant insights into the digital landscape across the continent. Conducted by the African Development Agency, this dataset provides a comprehensive overview of technology usage in various sectors, specifically in Ghana and Kenya. The report highlights trends from 2019 to 2020, showcasing the rapid growth in mobile internet access. In addition, the 2021 Digital Inclusion Assessment (DIA) focuses on the barriers faced by marginalized communities across South Asia, covering critical data collected in India and Bangladesh. This assessment, elaborated by the South Asian Network for Development, aims to inform policymakers about the digital divide issues in the region. The datasets serve as crucial tools for NGOs and governments engaging in digital transformation efforts.", "output": {"entities": {"named_data": ["Africa Technology Adoption Survey", "2021 Digital Inclusion Assessment"], "organization": ["African Development Agency", "South Asian Network for Development"], "acronym": ["ATAS", "DIA"], "year": ["2020", "2019 to 2020", "2021"], "geography": ["Ghana", "Kenya", "India", "Bangladesh"]}, "relations": [{"has_acronym": {"head": "Africa Technology Adoption Survey", "tail": "ATAS"}}, {"has_timeframe": {"head": "Africa Technology Adoption Survey", "tail": "2019 to 2020"}}, {"has_geography": {"head": "Africa Technology Adoption Survey", "tail": "Ghana"}}, {"has_geography": {"head": "Africa Technology Adoption Survey", "tail": "Kenya"}}, {"has_acronym": {"head": "2021 Digital Inclusion Assessment", "tail": "DIA"}}, {"has_timeframe": {"head": "2021 Digital Inclusion Assessment", "tail": "2021"}}, {"has_geography": {"head": "2021 Digital Inclusion Assessment", "tail": "India"}}, {"has_geography": {"head": "2021 Digital Inclusion Assessment", "tail": "Bangladesh"}}, {"has_organization": {"head": "Africa Technology Adoption Survey", "tail": "African Development Agency"}}, {"has_organization": {"head": "2021 Digital Inclusion Assessment", "tail": "South Asian Network for Development"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "digital development and technology adoption"}}
+{"input": "The International Organization for Migration (IOM) published the Global Migration Data Report 2022, which provides a comprehensive overview of migration trends and patterns. This dataset has been analyzed by numerous NGOs and researchers aiming to understand the dynamics of forced displacement and the challenges faced by migrants globally.", "output": {"entities": {"named_data": ["Global Migration Data Report 2022"], "organization": ["International Organization for Migration", "IOM"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Global Migration Data Report 2022", "tail": "International Organization for Migration"}}, {"used_by": {"head": "Global Migration Data Report 2022", "tail": "numerous NGOs and researchers"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "forced displacement, refugees, and migration"}}
+{"input": "8 agreements that did not fully satisfied either party (Ndayirukiye and Takeuchi 2014). This tension related to land, and who has a claim to the land, can lead to social tensions in communities with higher levels of return. Figure 3 – Refugees in Tanzania in 2005 by province of origin in Burundi Note: The number in brackets is the number of refugees in Tanzania in 2005 which was originally from the given province in Burundi. This information comes from (UNHCR 2021b). The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census. An important question for our hypotheses is the degree to which there is evidence of migration-related societal divisions in the country. There is no direct quantitative evidence on identity issues (i. e. returnees versus stayees), but we have data on attitudes towards emigration, remittances and return that can provide insights on these identities and even be a proxy for migration-related identity in some cases. Overall, attitudes towards emigration and return are mixed and show that there is scope for the existence of migration-related divisions. In Table 1 we report the share of respondents who agreed with different statements regarding emigration, remittances and return.", "output": {"entities": {"named_data": ["1990 Burundi Census"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "1990 Burundi Census", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 758, "dataset_name": "1990 Burundi Census"}}
+{"input": "Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka and\n\nGuillaume (2018a). The data are primarily based on sub-national GDP per capita data constructed by Gennaioli,\n\nIs the relationship between rainfall and GDP explained by agriculture? We use data from the ESA CCI project\n\n\nto determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split\n\ndifferent weights. Population is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).\n\nThe work compares food prices subnationally and finds that increasing prices are the most significant factor driving recent food insecurity but documents strong spatial heterogeneity in both market price dynamics, as well as relations to food insecurity, and provides examples of markets where prices are more sensitive to localized shocks. 4For instance, the International Finance Statistics (IFS) data base of the IMF reports monthly price data at Consumer Price Index (CPI) component level, but few developing countries report food price data without substantial delays.", "output": {"entities": {"named_data": ["HYDE 3.2"], "organization": ["we"]}, "relations": [{"used_by": {"head": "HYDE 3.2", "tail": "we"}}]}, "_meta": {"entry_id": 164, "dataset_name": "HYDE 3.2"}}
+{"input": "This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of confidence as refugees generally. 23 Cross-country data about conflict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conflicts. In other words, we attempt to answer several questions.", "output": {"entities": {"named_data": ["World Bank databases"], "organization": ["World Bank", "we"]}, "relations": [{"has_organization": {"head": "World Bank databases", "tail": "World Bank"}}, {"used_by": {"head": "World Bank databases", "tail": "we"}}]}, "_meta": {"entry_id": 14, "dataset_name": "World Bank databases"}}
+{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016).", "output": {"entities": {"named_data": ["Profile Global Registration System (ProGres)"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "Profile Global Registration System (ProGres)", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 270, "dataset_name": "Profile Global Registration System (ProGres)"}}
+{"input": "34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey.", "output": {"entities": {"named_data": ["Labor Force Survey"], "organization": ["Survey on Income and Living Conditions"]}, "relations": [{"used_by": {"head": "Labor Force Survey", "tail": "Survey on Income and Living Conditions"}}]}, "_meta": {"entry_id": 93, "dataset_name": "Labor Force Survey"}}
+{"input": "rents to GDP from WDI), we can implicitly compute 𝛾.\n\nSource:_\n_Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._\n\n\nFigure 2 plots the sources of growth for the Sub-Saharan Africa region as well as the different groups according\n\nSource: Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._\n\nFrankel, J. A., & Romer, D. H. (1999). \"Does trade cause growth?\" American Economic Review 89(3): 379-399.\n\nreported in the Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015). [15]", "output": {"entities": {"named_data": ["Penn World Tables 9.0"], "organization": ["Frankel, J. A., & Romer, D. H."]}, "relations": [{"used_by": {"head": "Penn World Tables 9.0", "tail": "Frankel, J. A., & Romer, D. H."}}]}, "_meta": {"entry_id": 469, "dataset_name": "Penn World Tables 9.0"}}
+{"input": "The recent findings from the National Education Assessment (NEA) demonstrate significant improvements in literacy rates among primary school students. This dataset, produced by the Ministry of Education, is crucial for policymakers as it provides valuable insights into the effectiveness of educational programs implemented over the past few years. Various international organizations, including UNESCO and UNICEF, have used the NEA data to assess progress in learning achievement and inform their initiatives.", "output": {"entities": {"named_data": ["National Education Assessment"], "organization": ["Ministry of Education", "UNESCO", "UNICEF"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "National Education Assessment", "tail": "Ministry of Education"}}, {"used_by": {"head": "National Education Assessment", "tail": "UNESCO"}}, {"used_by": {"head": "National Education Assessment", "tail": "UNICEF"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The Digital Technology Adoption Survey (DTAS) conducted by the International Telecommunication Union provides valuable insights into the usage of digital technologies across various sectors. This survey data, which reflects trends from 2020, helps policymakers understand the landscape of technology adoption and its implications for economic growth.", "output": {"entities": {"named_data": ["Digital Technology Adoption Survey", "survey data"], "organization": ["International Telecommunication Union"], "acronym": ["DTAS"], "year": ["2020"], "geography": []}, "relations": [{"has_organization": {"head": "Digital Technology Adoption Survey", "tail": "International Telecommunication Union"}}, {"has_acronym": {"head": "Digital Technology Adoption Survey", "tail": "DTAS"}}, {"has_timeframe": {"head": "survey data", "tail": "2020"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "digital development and technology adoption"}}
+{"input": "Recent evaluations illustrated significant trends in school enrollment and learning outcomes across various regions. The Global Education Monitoring Report (GEMR) released in 2022 highlighted alarming disparities in primary education access in Sub-Saharan Africa, particularly among marginalized groups. Following this, UNICEF utilized the GEMR findings to tailor its initiatives aimed at increasing enrollment rates in the region. Additionally, the National Learning Assessment (NLA) data from 2021 provided insights into student performance in mathematics and reading, which were subsequently analyzed by the Ministry of Education in Nigeria to inform curriculum reforms. This cross-referencing of data sources underscores the importance of collaboration between international organizations and local authorities to improve educational outcomes.", "output": {"entities": {"named_data": ["Global Education Monitoring Report", "National Learning Assessment"], "organization": ["UNICEF", "Ministry of Education", "Global Education Monitoring Report"], "acronym": ["GEMR", "NLA"], "year": ["2022", "2021"], "geography": ["Sub-Saharan Africa", "Nigeria"]}, "relations": [{"has_organization": {"head": "Global Education Monitoring Report", "tail": "Global Education Monitoring Report"}}, {"has_organization": {"head": "National Learning Assessment", "tail": "Ministry of Education"}}, {"used_by": {"head": "Global Education Monitoring Report", "tail": "UNICEF"}}, {"used_by": {"head": "National Learning Assessment", "tail": "Ministry of Education"}}, {"has_acronym": {"head": "Global Education Monitoring Report", "tail": "GEMR"}}, {"has_acronym": {"head": "National Learning Assessment", "tail": "NLA"}}, {"has_timeframe": {"head": "Global Education Monitoring Report", "tail": "2022"}}, {"has_timeframe": {"head": "National Learning Assessment", "tail": "2021"}}, {"has_geography": {"head": "Global Education Monitoring Report", "tail": "Sub-Saharan Africa"}}, {"has_geography": {"head": "National Learning Assessment", "tail": "Nigeria"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "The recent report highlights the increasing need for energy access in rural areas, particularly emphasizing the role of the Renewable Energy Utilization and Access Survey. This survey provides valuable insights into the challenges and opportunities faced by communities in adopting renewable energy solutions. Several case studies were conducted to illustrate the impact of localized energy initiatives on economic growth and social development.", "output": {"entities": {"named_data": ["Renewable Energy Utilization and Access Survey"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}
+{"input": "In 2022, the East African Fertility Assessment revealed significant insights into reproductive health trends across the region. This study, published by the African Population Institute, indicates that fertility rates in Kenya and Uganda have experienced a gradual decline over the past decade. The findings from this assessment highlight the need for targeted interventions in family planning and maternal health services. The report, referred to as EFA 2022, provides crucial data for policymakers and health organizations looking to implement effective strategies to improve population health outcomes.", "output": {"entities": {"named_data": ["East African Fertility Assessment"], "organization": ["African Population Institute"], "acronym": ["EFA"], "year": ["2022"], "geography": ["Kenya", "Uganda"]}, "relations": [{"has_acronym": {"head": "East African Fertility Assessment", "tail": "EFA"}}, {"has_timeframe": {"head": "East African Fertility Assessment", "tail": "2022"}}, {"has_geography": {"head": "East African Fertility Assessment", "tail": "Kenya"}}, {"has_geography": {"head": "East African Fertility Assessment", "tail": "Uganda"}}, {"has_organization": {"head": "East African Fertility Assessment", "tail": "African Population Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "demographics, fertility, and population growth"}}
+{"input": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n#### **2.2 Current occupational situation**\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland Administrative ZUS data can be used as a proxy for both average and _gross_ earnings percentages. On June 30, 2024, average bases for social contributions of Ukrainian refugees accounted for just 64% of those of Polish citizens. However, this is only relative to Polish citizens not all workers in the economy as a whole, and comes with other caveats of administrative instead of survey data sources – data given on a particular day instead of period average, the shadow economy unaccounted for, different contributions based on the type of contract, no data for farmers who belong to a separate social insurance scheme.\nAlso, the ZUS data, that is available, is much more limited that the SEIS survey data primarily used in this report.\n\n**Ukrainian refugees in Poland have**\n\n**clearly improved their economic**", "output": {"entities": {"named_data": ["SEIS survey data"], "organization": ["this report"]}, "relations": [{"used_by": {"head": "SEIS survey data", "tail": "this report"}}]}, "_meta": {"entry_id": 1346, "dataset_name": "SEIS survey data"}}
+{"input": "The Industrial Competitiveness Survey (ICS) 2022 provides critical insights into the performance of various sectors in the manufacturing industry across the European Union. Published by the European Industry Agency, this dataset covers diverse geographies including Germany, France, and Italy. Additionally, the Trade Performance Assessment Report (TPAR) 2021, used extensively by policymakers, provides a comprehensive overview of trade dynamics in the Asia-Pacific region. This report underscores the need for data-driven strategies, particularly in emerging markets such as Vietnam and Thailand. Moreover, the SME Growth Database (SME-GD), which spans the years 2019 to 2023, focuses on small and medium-sized enterprises in South America, offering valuable data for researchers and institutes interested in economic development.", "output": {"entities": {"named_data": ["Industrial Competitiveness Survey", "Trade Performance Assessment Report", "SME Growth Database"], "organization": ["European Industry Agency"], "acronym": ["ICS", "TPAR", "SME-GD"], "year": ["2022", "2021", "2019 to 2023"], "geography": ["European Union", "Germany", "France", "Italy", "Asia-Pacific", "Vietnam", "Thailand", "South America"]}, "relations": [{"has_acronym": {"head": "Industrial Competitiveness Survey", "tail": "ICS"}}, {"has_timeframe": {"head": "Industrial Competitiveness Survey", "tail": "2022"}}, {"has_geography": {"head": "Industrial Competitiveness Survey", "tail": "European Union"}}, {"has_geography": {"head": "Industrial Competitiveness Survey", "tail": "Germany"}}, {"has_geography": {"head": "Industrial Competitiveness Survey", "tail": "France"}}, {"has_geography": {"head": "Industrial Competitiveness Survey", "tail": "Italy"}}, {"has_acronym": {"head": "Trade Performance Assessment Report", "tail": "TPAR"}}, {"has_timeframe": {"head": "Trade Performance Assessment Report", "tail": "2021"}}, {"has_geography": {"head": "Trade Performance Assessment Report", "tail": "Asia-Pacific"}}, {"has_geography": {"head": "Trade Performance Assessment Report", "tail": "Vietnam"}}, {"has_geography": {"head": "Trade Performance Assessment Report", "tail": "Thailand"}}, {"has_acronym": {"head": "SME Growth Database", "tail": "SME-GD"}}, {"has_timeframe": {"head": "SME Growth Database", "tail": "2019 to 2023"}}, {"has_geography": {"head": "SME Growth Database", "tail": "South America"}}, {"used_by": {"head": "Trade Performance Assessment Report", "tail": "policymakers"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "trade, industry, and economic competitiveness"}}
+{"input": "18 the local data on physical geography and population from the raster data. These data can then be imported into statistical programs for analysis. We aggregate all data up to a grid of 8. 6x8. 6km squares. Each grid square is assigned attributes of the country it is in along with information from data disaggregated to the level of the individual squares. Figure 3 illustrates this grid as a fictive country somewhat smaller than the average size in our dataset (50x50 squares, or 430x430 km) with a fairly representative but stylized population distribution. The country has three major cities, one of which is the capital, and two smaller ones. A rebel group has its headquarters at the Eastern border. The ACLED data for the Central African conflicts were aggregated up to the 8. 6x8. 6km squares and merged with information on other explanatory variables aggregated to the same level.", "output": {"entities": {"named_data": ["ACLED data"], "organization": ["ACLED"]}, "relations": [{"has_organization": {"head": "ACLED data", "tail": "ACLED"}}]}, "_meta": {"entry_id": 257, "dataset_name": "ACLED data"}}
+{"input": "In recent years, the Education Improvement Report (EIR) has become a cornerstone for understanding educational outcomes in Nigeria. Published by the Nigerian Ministry of Education, it provides comprehensive data on student learning achievements across various regions. The United Nations Educational, Scientific and Cultural Organization (UNESCO) has utilized the EIR to assess progress in educational initiatives from 2019 to 2022. Furthermore, the assessment highlights significant disparities in school enrollment rates, particularly in rural versus urban areas, underscoring the need for targeted interventions. This dataset serves as a vital resource for policymakers and educational programs aiming to enhance access to quality education in Nigeria.", "output": {"entities": {"named_data": ["Education Improvement Report", "EIR"], "organization": ["Nigerian Ministry of Education", "United Nations Educational, Scientific and Cultural Organization", "UNESCO"], "acronym": ["EIR"], "year": ["2019 to 2022"], "geography": ["Nigeria"]}, "relations": [{"has_organization": {"head": "Education Improvement Report", "tail": "Nigerian Ministry of Education"}}, {"used_by": {"head": "Education Improvement Report", "tail": "UNESCO"}}, {"has_acronym": {"head": "Education Improvement Report", "tail": "EIR"}}, {"has_timeframe": {"head": "Education Improvement Report", "tail": "2019 to 2022"}}, {"has_geography": {"head": "Education Improvement Report", "tail": "Nigeria"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "education, learning achievement, and school enrollment"}}
+{"input": "14 displacement. 55 % of the returnees reported to have been employed before the crisis and 36 % in June 2014. Over time the employment situation among the displaced has improved steadily and by December 2014 more people reported being employed than prior to the crisis. All the returnees were able to regain employment after returning. The employment situation of IDPs, returnees, and refugees in Niger is steadily improving; only for refugees in Mauritania does one notice a steady decrease, with 100 % reporting no employment during January and February. Source: Listening to Displaced People Survey, 2014 and 2015. The ownership of livestock and consumer durables was reduced significantly as a consequence of the crisis. Table 7 demonstrates this by showing the Tropical Livestock Units (TLU) 12 owned prior to the crisis and in June 2014 as well as the percentage of ‘ yes ’ responses on a question whether a given asset was owned by the household. 13 The loss on livestock has been enormous particularly amongst IDPs and refugees who lost respectively more than 90 % and 75 % of their animals. 12 TLU is a common unit to describe livestock numbers of various species as a single figure that expresses the total amount of livestock present – irrespective of the specific composition. 13 This was a ‘ yes / no ’ question meaning that if 56 % of the", "output": {"entities": {"named_data": ["Displaced People Survey"], "organization": ["Listening to Displaced People Survey"]}, "relations": [{"has_organization": {"head": "Displaced People Survey", "tail": "Listening to Displaced People Survey"}}]}, "_meta": {"entry_id": 234, "dataset_name": "Displaced People Survey"}}
+{"input": "The Gender Equality Survey (GES) conducted by the National Bureau of Statistics in 2022 provides crucial insights into women's participation in the workforce across various regions. Additionally, the Women's Economic Empowerment Assessment (WEEA) utilizes data from multiple sources, including the 2021 Economic Census for Urban Areas, which was implemented to gauge women's economic activities specifically in urban contexts. The results from the GES reveal significant disparities in employment rates between genders, urging policymakers to consider these findings in their strategies. Source: National Bureau of Statistics elaboration based on GES and WEEA data.", "output": {"entities": {"named_data": ["Gender Equality Survey", "Women's Economic Empowerment Assessment", "2021 Economic Census for Urban Areas"], "organization": ["National Bureau of Statistics"], "acronym": ["GES", "WEEA"], "year": ["2022", "2021"], "geography": ["Urban Areas"]}, "relations": [{"has_acronym": {"head": "Gender Equality Survey", "tail": "GES"}}, {"has_acronym": {"head": "Women's Economic Empowerment Assessment", "tail": "WEEA"}}, {"has_timeframe": {"head": "Gender Equality Survey", "tail": "2022"}}, {"has_timeframe": {"head": "Women's Economic Empowerment Assessment", "tail": "2021"}}, {"has_timeframe": {"head": "2021 Economic Census for Urban Areas", "tail": "2021"}}, {"has_geography": {"head": "2021 Economic Census for Urban Areas", "tail": "Urban Areas"}}, {"has_organization": {"head": "Gender Equality Survey", "tail": "National Bureau of Statistics"}}, {"used_by": {"head": "Women's Economic Empowerment Assessment", "tail": "National Bureau of Statistics"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "gender equality and women's economic empowerment"}}
+{"input": "The Gender Equality in Economic Participation Survey (GEEPS) conducted in 2020 provides crucial insights into women's participation in the workforce across various sectors. This survey, covering countries in Sub-Saharan Africa, highlights significant barriers faced by women and suggests areas for policy intervention. In addition, the Women’s Empowerment Assessment Report 2021 (WEAR 2021), published by the International Labour Organization, offers a comprehensive overview of gender disparities in income and employment opportunities in Eastern Europe. Furthermore, data from the Global Gender Gap Index (GGGI) 2019 indicates ongoing challenges in achieving gender parity, particularly in South Asia. These datasets collectively underscore the importance of targeted efforts in policy-making to address gender inequalities.", "output": {"entities": {"named_data": ["Gender Equality in Economic Participation Survey", "Women’s Empowerment Assessment Report 2021", "Global Gender Gap Index"], "organization": ["International Labour Organization"], "acronym": ["GEEPS", "WEAR 2021", "GGGI"], "year": ["2020", "2021", "2019"], "geography": ["Sub-Saharan Africa", "Eastern Europe", "South Asia"]}, "relations": [{"has_acronym": {"head": "Gender Equality in Economic Participation Survey", "tail": "GEEPS"}}, {"has_timeframe": {"head": "Gender Equality in Economic Participation Survey", "tail": "2020"}}, {"has_geography": {"head": "Gender Equality in Economic Participation Survey", "tail": "Sub-Saharan Africa"}}, {"has_acronym": {"head": "Women’s Empowerment Assessment Report 2021", "tail": "WEAR 2021"}}, {"has_timeframe": {"head": "Women’s Empowerment Assessment Report 2021", "tail": "2021"}}, {"has_geography": {"head": "Women’s Empowerment Assessment Report 2021", "tail": "Eastern Europe"}}, {"has_acronym": {"head": "Global Gender Gap Index", "tail": "GGGI"}}, {"has_timeframe": {"head": "Global Gender Gap Index", "tail": "2019"}}, {"has_geography": {"head": "Global Gender Gap Index", "tail": "South Asia"}}, {"has_organization": {"head": "Women’s Empowerment Assessment Report 2021", "tail": "International Labour Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "gender equality and women's economic empowerment"}}
+{"input": "Vermeer, M. and S. Rahmstorf. 2009. Global sea level linked to global temperature. _Proceedings_ _of the National Academy of Sciences_ 106 (51), 21527-32. VLIZ. 2011. Maritime Boundaries Geodatabase, version 6.1. Available online at [http://www.vliz.be/vmdcdata/marbound. Consulted on 2011-05-12.](http://www.vliz.be/vmdcdata/marbound) Williams, M., 1990. Understanding Wetlands. In M. Williams (ed) _Wetlands: A Threatened_ _Landscape_ . Wiley-Blackwell. Woodward, R.T. and Y.S. Wui. 2001. The economic value of wetland services: A meta-analysis. _Ecological Economics_, 37, 257-270 World Bank, 2010. Boundaries of the World. Map Design Unit. The boundaries, colors, denominations and any other information shown on this map do not imply, on the part of the World Bank Group, any judgement on the legal status of any territory, or any endorsement or acceptance of such boundaries. 25", "output": {"entities": {"named_data": ["Maritime Boundaries Geodatabase"], "organization": ["VLIZ", "World Bank"]}, "relations": [{"has_organization": {"head": "Maritime Boundaries Geodatabase", "tail": "VLIZ"}}, {"used_by": {"head": "Maritime Boundaries Geodatabase", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1166, "dataset_name": "Maritime Boundaries Geodatabase"}}
+{"input": "The recent analysis of poverty trends in Sub-Saharan Africa was facilitated by the Poverty Assessment Survey (PAS) conducted by the African Development Bank (AfDB). This dataset has been instrumental for researchers and policymakers aiming to address disparities in income and access to resources across various regions. The insights derived from the PAS have also been cited by various NGOs working on poverty reduction strategies globally.", "output": {"entities": {"named_data": ["Poverty Assessment Survey"], "organization": ["African Development Bank", "NGOs"], "acronym": ["AfDB"], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Poverty Assessment Survey", "tail": "African Development Bank"}}, {"used_by": {"head": "Poverty Assessment Survey", "tail": "NGOs"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "poverty headcount and inequality measurement"}}
+{"input": "The Renewable Energy Access Assessment 2022, published by the Global Energy Initiative, provides comprehensive data on energy access trends across various regions. This dataset highlights the progress made towards sustainable energy solutions and is crucial for policymakers aiming to improve energy conditions in underserved areas.", "output": {"entities": {"named_data": ["Renewable Energy Access Assessment 2022"], "organization": ["Global Energy Initiative"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Renewable Energy Access Assessment 2022", "tail": "Global Energy Initiative"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}