impact/train.jsonl

{"input": "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;", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the DHS Program has collected, analyzed and disseminated data on population, health, HIV and nutrition through more than 300 surveys in over 90 countries.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "It is estimated that approximately 64 percent of the freshwater marsh, 66 percent of Global Lakes and Wetlands Database coastal wetlands, and 61 percent of brackish/saline wetlands are at risk.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "When the regressions are run with the village characteristics from the 2005 Indian census, the coefficients of interest do not change significantly.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "author's calculations using WITS, COMTRADE", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "In the present analysis, we use 2, 530 of these.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The sampling frame for refugee camps is based on UNHCR’s proGRES database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "Evidence from Harmonized Data World Bank.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Labor Force Surveys collect data on work-related issues and provide a basis for measuring employment and unemployment indicators.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "In our analysis, we use Afrobarometer, EPR, and Murdock Atlas data; therefore, we can use LEDA functions to link the different ethnic groups to each other.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Using the spatial distribution of rice production based on the 2010 SPAM, the latest district-level rice production data for the period of 2013-15 are disaggregated into 3,198 locations or pixels.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["geospatial"]}]}}
{"input": "We exploit only the data on “ refugees ”.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "The data come from the baseline of the Sri Lanka Longitudinal Survey of Enterprises SLLSE, collected by the authors between January and March 2008.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "is estimated by overlaying the inundation risk map with the population map for 2001 using", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "For Indonesia the building type information was compiled from a World Housing Encyclopedia (WHE) survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This analysis uses the relaxed definition to measure the current employment status of the host community and refugees.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "applying sampling weights is essential in order to obtain consistent estimates of δs.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "These shocks were calibrated to match the difference in data between refugees income from labour in UNHCR survey and average wage in Poland from Statistics Poland weighted by refugees share in total workforce.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "are used as a base for localized impact.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["geospatial"]}]}}
{"input": "The z-scores were calculated using EMDHS and the 2006 WHO growth standards [24].", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The migratory potential is based on a few geophysical characteristics of the coastline: coastal type, topography, tidal range, and other information when available (e.g., whether 14 GLWD coastal wetlands is a term used in this paper to distinguish coastal wetlands from the specific coastal wetlands type in the GLWD.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["geospatial"]}]}}
{"input": "ultimately, (iv) the implications of these design decisions on the computation and monitoring of SDGs 1.4.2 and 5.a.1.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Source: Deloitte own elaboration based on SEIS UNHCR survey and GUS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use data from the ESA CCI project to determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split the sample based on different shares of cropland ranging from less than 20 percent to more than 75 percent.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We combine the respondents from all four DHS standard surveys in Ghana for which there are geographic identifiers.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "She therefore compiles data from the Demographic and Health Surveys (DHS) carried out in 18 Sub-Saharan African countries, cautioning that these survey data suffer from defects such as recall bias.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Percentage of administered COVID-19 vaccine doses captured in the national vaccination digital registry;", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "Source: Authors’ calculation based on ALCS 2013–14", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The number of estimated women at reproductive age (WRA) is based on a calculation of the affected population [INGC] * % of WRA [2017 census].", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To have up-to-date information on the socio-economic outcomes and poverty levels of refugees and to allow comparison to Ethiopian host communities, the SESRE applied the same questionnaire and data collection methods as the HoWStat, with some modifications.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This indicator will measure the percentage of reported serious Adverse Events Following Immunization (AEFI) post COVID-19 vaccinations that have been reported to the Iraqi MOHE surveillance system, GRM and other channels that have been addressed and investigated within 48 hours of reporting to the total number of reported AEFIs.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "The data is based on satellite imaginary using the total column aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer satellite instruments, which is combined with chemical transport model simulations, and ground measurements from 79 countries to produce a global spatial data set with 0.1° × 0.1° resolution (Brauer et al., 2015).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Information on WMP categories for the Coastline was downloaded from the DIVA GIS database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "we use data on exports from COMTRADE to construct a measure of Germany's revealed comparative advantage (RCA) relative to Poland and to the world.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "selecting households was supplied by CAPMAS from its 1990/91 HIES survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "In addition, we use gridded population density data with a 1km resolution from Landscan (Geographic Information Science and Technology 2015).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Data source|DHIS2", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "According to the JD ‐ HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "We use data from the population census, 1998, as well as the census of private schooling, 2000, to provide estimates of madrassa, private, and government school enrollment in each district except for those in the province of FATA.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "the digital elevation (90m DEM V2) data we use in our analysis gives altitude in 1-meter increments, preventing us from sub-meter SLR modeling.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to the 2021 Polish National Census, one year before the outbreak of the full-scale war in Ukraine there were about one million Ukrainian citizens residing in Poland, almost all of them on a temporary basis.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "This is based on version 2. 0 of the Barro-Lee dataset for educational attainment among the total population 15 and older.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Section 3 employs the database to investigate trends in the incidence and power of Indian Ocean cyclonic storms from 1877 to 2016.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We draw our emissions estimates from the World Resources Institute’s Climate Analysis and Indicators database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "the high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The source for the localized wind speeds is the IBTrACS database that provides the strength and tracks every 6 hours of all typhoons that affected Southeast Asia during the period.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "For the purpose of this study, the standard World Bank definitions of poverty are used to determine the number of poor people in a given subnational administrative unit.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "This paper uses micro-data from two waves of the Somali High Frequency Survey to assess the impact of the severe drought that Somalia experienced in 2016/17 on poverty, hunger, and consumption.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "About 88 percent of refugees in Ethiopia remain in camps (based on SESRE data).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Country coastlines were extracted from the World Vector Shoreline, a standard National Geospatial Intelligence Agency (formerly Defense Mapping Agency) product at a nominal scale of 1:250,000.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "estimations based on Sudan MICS 2014/15 data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Several authors have relied on the general result by", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Own calculations using POF 2017/18.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The SESRE used a logistics plan similar to HoWStat.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Nicholls (1999) and Nicholls (2004) are based on wetland losses derived from the Global Vulnerability Analysis (Hoozemans et al. 1993)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Iraq performs poorly across most universal health coverage (UHC) index indicators, and the UHC effective coverage index stands at only 57.7.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "We sourced bilateral trade value data from WITS and bilateral tariff data from a medley of sources, presented in Appendix Table A1.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we choose road line type to use in the analysis.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Authors' calculations using the 2014 Household Budget Survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we show using the IPUMS dataset that children born in the United States to men born in China are more likely to be boys, but this finding does not hold for children born to women from China.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Two new and unique data sources are employed in the empirical analysis.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "elevation figures were included, taken from the GLOBE 1 kilometer elevation database", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Labor Force Surveys collect data on work-related issues and provide a basis for measuring employment and unemployment indicators.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We used stable light imagery of SSA derived from scores of orbits of the DMSP OLS in from 1992-2013 since this product inter-calibrated where fires and other ephemeral lights have been removed, although there are noteworthy blunders associated with over-glow effects where lighting spreads to neighboring pixels (and hence economic activity is wrongfully attributed to certain places).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The indicator will track the percentage of administered COVID-19 vaccines which are captured in the national vaccination digital registry.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "we have constructed the cyclone database to support research on household- and community-level adaptation to cyclone risks.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "we also added the drive as well as the pedestrian network leveraging the Open Street Map data set to account for commuters who walk or drive to work.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["geospatial"]}]}}
{"input": "the second type of data, based on household surveys, are different rounds of the Pakistan Integrated Household Survey (PIHS) carried out in 1991, 1998 and 2001.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Data on wetlands were extracted from all wetlands Global Lakes and Wetlands Database (GLWD-3) produced by the Center for Environmental Systems Research (CESR)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Percentage calculated based on GUS average for the economy as a whole in the months of the SEIS survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We use microdata from the DHS, obtained from standardized surveys across years and countries.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "PISA 2022 mean scores for Costa Rica in Reading are above LAC’s average (415 vs. 400), as well as mean scores in Mathematics (385 vs. 374) and Sciences (411 vs. 400).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to the OECD, the national Minimum Wages in Turkey equals to 70 percent of the median wage (see OECD.stat: https://stats.oecd.org/Index.aspx?DataSetCode=MIN2AVE).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Authors ’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Shock to population was calibrated to match data for residents of Poland from Statistics Poland and number of refugees based on PESEL UKR.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to Eurostat data, in September 2023, harmonized unemployment in Poland reached 2.8%, the same level as Malta.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Utilization of the budget will be monitored through quarterly Interim Financial Reports (IFRs) by analyzing overutilization and underutilizations and using the reports as management’s tool for decision-making.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["report"]}]}}
{"input": "We complete this data with exact geographic location data from MineAtlas (2013), where satellite imagery shows the actual mine boundaries, which allows us to identify and update the center point of each mine.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "A multi-partner effort led by WHO and UNICEF developed the Vaccine Introduction Readiness Assessment Tool (VIRAT) to support countries in developing a roadmap to prepare for vaccine introduction and identify gaps to inform areas for potential support.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Using the NLSS data we begin by estimating a regression of the form: yk s = δs + α (ak s − a) + βs (Ek s − Es) + χs (Hk s − Hs) + vk s (4)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To estimate �� and ��, we use the World Development Indicators database, which provides the income share of the bottom 20%.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "‘Lagoon’ from ArcWorld (ESRI, 1992: referenced in Lehner and Döll 2004);", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Using macro-economic information, and incorporating known imminent policy changes, we project the database forward in time to the year 2010 and beyond.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "the UNHCR Gap Analysis, [29] and the outcome of a stakeholder consultation process revealed that rehabilitation of settlement roads is the priority development need across all communities.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We used stable light imagery of SSA derived from scores of orbits of the DMSP OLS in from 1992-2013 since this product inter-calibrated where fires and other ephemeral lights have been removed, although there are noteworthy blunders associated with over-glow effects where lighting spreads to neighboring pixels (and hence economic activity is wrongfully attributed to certain places).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "information on renewable policy design was obtained mostly from IEA's renewable policy   database, and cross-checked with government websites, legislative texts and other related", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "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", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use the ENLACE panel to study the relationship between Grade 6 test scores", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Multidimensional Poverty Index (MPI) is reported to be higher among women than men", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "Our final data measures changes in NPP for each 0.1-degree gridcell (approximately 11x11 km at the equator) which contains a minimum level of cropland in the year 2000 (the first year of our dataset).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "Data on the number of natural disasters are obtained for 196 countries from the Emergency Events Database (EM-DAT)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "based on a 2014 Labour Force Survey adhoc module that includes refugee labour market outcomes and 2019 LFS with outcomes of recent non-EU migrants.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Country coastlines were extracted from the World Vector Shoreline, a standard National Geospatial Intelligence Agency (formerly Defense Mapping Agency) product at a nominal scale of 1:250,000.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Country-level pluvial and fluvial flood maps, developed by Fathom Ltd, are used in this analysis (Smith et al. 2015).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["geospatial"]}]}}
{"input": "Findings from such surveys will be used to improve the communication campaign and citizen engagement.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "authors’ estimates based on the data from Sudan MICS 2014* and School Census 2018**", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The matching process determines for each individual the number of sex- appropriate public basic and secondary schools per 1, 000 individuals available in the individual ’ s subdistrict of birth when the individual was of age to accede to this educational level (six years of age for the basic level and 15 years of age for the secondary level).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Refugee education data is integrated into the MoE’s Education Management Information System (EMIS) (UNHCR, 2020), and a separate chapter on refugee education is included in the annual education statistics report of the MoE.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "the wetland migratory potential (WMP) characteristic in the Dynamic Interactive Vulnerability Assessment (DIVA) database from the DINAS-COAST project has been used", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "These HCES are already being used to monitor global poverty trends", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "using data from the Demographic and Health Survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The analysis uses household and individual information from SESRE data and geospatial information.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use two complementary geocoded household data sets to analyze outcomes in Ghana: the Demographic and Health Survey (DHS) and the Ghana Living Standard Survey (GLSS), which provide information on a wide range of welfare outcomes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The PMU at MOHE will use the World Bank online procurement planning and tracking tool to prepare, clear and update its procurement plans and conduct procurement transactions as referred to in the Procurement Regulations Section V, article 5.9.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Our child health data come from the 2013 and 2018 Nigerian Demographic Health Survey (DHS).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The data used to created this table comes from the 2010 census.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "This is based on version 2. 0 of the Barro-Lee dataset for educational attainment among the total population 15 and older.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use two complementary geocoded household data sets to analyze outcomes in Ghana: the Demographic and Health Survey (DHS) and the Ghana Living Standard Survey (GLSS), which provide information on a wide range of welfare outcomes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "About 88 percent of refugees in Ethiopia remain in camps (based on SESRE data).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "This study estimates the location of people using the Global Human Settlement Layer (GHSL), produced by the EC JRC.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Authors ’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "These findings are based on high-resolution flood hazard and population maps that enable global coverage, as well as poverty estimates from the World Bank’s Global Monitoring Database of harmonized household surveys.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Most of the analysis presented in this chapter is based on detailed consumption data from the Socioeconomic Survey of Refugees in Ethiopia (SESRE) conducted between October 2022 and February 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Initially designed to assess the impact of the health crisis linked to the HIV-AIDS epidemic in the area, it used a stratified design to ensure relative appropriate sampling families with adult mortality.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Strong executive constraint is measured as a dummy indicating whether or not we have executive parity or subordination of the executive at the country level, a value 7 for “ xconst ” variable in Polity IV dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: 2019 Shona socioeconomic survey,", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "based on EPR-ER data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Authors’ analysis based on data from BASIX and APHRODITE.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "estimations based on Sudan MICS 2014/15 data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Data from the 2022 national census (which included a refugee module for the first time) and from the first round of a World Bank-led survey on refugee self-reliance showed that refugees generally enjoy good access to basic services such as education, health and water, but remain well short of self-reliance with respect to employment and income.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"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).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turk­ish Labour Force Survey to assess their labor market impact.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the project will use the Systematic Tracking of Exchanges in Procurement (STEP), a planning and tracking system that will provide data on procurement activities, establish benchmarks, monitor delays, and measure procurement performance.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "we use the food item description in POF to look up corresponding items and calorie intake per kilogram (kg) in TBCA.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use this map to identify areas that have historically supported mangrove habitats.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "implementation of a monitoring mechanism, such as the World Bank’s Education and Technology Readiness Index (ETRI);", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "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": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Annex 1 reproduces a graphic showing the 67 modules across Pre-School to 12 grades of education that comprises the PNFT curriculum.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We obtain ethnicities of refugees from the EPR-ER dataset,", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We extract daily event observations from the UCDP dataset if the location of the actual event is exactly known, the event location is within a radius of less than 25 km around a known point, or at least the administrative district where the event happened is known.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Data from the 2022 national census (which included a refugee module for the first time) and from the first round of a World Bank-led survey on refugee self-reliance showed that refugees generally enjoy good access to basic services such as education, health and water, but remain well short of self-reliance with respect to employment and income.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Indicator 2: Percentage of Grade 3 students who surpass Basic level in National Assessments for Reading and Mathematics;", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "In 2012, average inflows in the World Bank dataset were over 9. 5 billion USD and over 3 billion USD in the OECD data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use two poverty maps based on the General Population and Housing Censuses of 1998 and 2009.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "based on a 2014 Labour Force Survey adhoc module that includes refugee labour market outcomes and 2019 LFS with outcomes of recent non-EU migrants.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Using it to estimate individual wages likely underrepresents lowest and highest incomes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "In 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).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Access to political power is ranked on a scale from 1 to 7 in the GROWup dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Deloitte own elaboration based on mid-2024 SEIS UNHCR survey (Ukrainian refugees’ educational attainment)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we add information from the IBTrACS database maintained by the Global Data Center for Meteorology, operated by the US National Oceanic and Atmospheric Administration.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "This estimate would be most likely lower, if data allowed us to look at average or gross earnings.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The paper makes use a new dataset called ACLED (Armed Conflict Location and Events Dataset) to allow for this type of disaggregated analysis.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Authors’ analysis based on data from BASIX and APHRODITE.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the sample from EMDHS 2014 is used.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to the MSNA Poland 2023 survey, nearly half of all refugee households have a person with a chronic illness, while in nearly 10% there is a disabled person (Washington Group level 3 disability).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Food insecurity experience is measured based on a scale that ranges between 0 and 10 and calculated by adding household’s experience related to the following events in the past year: (i) worried about having enough food, (ii) unable to eat healthy/nutrition food, (iii) only ate a few kinds of food, (iv) had to skip a meal, (v) adults ate less, (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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Coastal wetlands in this analysis are defined as the following wetland types in a low elevation (with elevation 10 m or less from sea level) zone: freshwater marsh, swamp forest, GLWD coastal wetlands and brackish/saline wetlands, as delineated in the Global Lakes and Wetlands (GLWD-3) database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998 / 99, 1999 / 00 and 2000 / 01.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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.”", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use data from the Labor Force Surveys (LFS) of the West Bank and Gaza and we focus on 20-59 years old men.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the household survey was designed in view of combining it with the nationally representative 1998 Vietnam Living Standards Survey (VLSS) to predict baseline consumption expenditures for SIRRV households", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This report uses data from the SESRE extensively to analyze the Ethiopian refugee situation and to devise policy directions.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "According to the latest Demographic Health Survey [(2015),](https://dhsprogram.com/pubs/pdf/AIS12/AIS12.pdf) 24% of women aged 18 – 49 are estimated to have suffered physical violence since the age of 15.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "A commonly used global coastal flood risk map is used in this study: the Global Tide and Surge Reanalysis (GTSR) data set by Muis et al. 2016.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Probability of being in school was estimated using a Probit model and ALCS 2013–14 data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Source: Deloitte own elaboration based on the PESEL database as of September 2024.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Authors’ calculation based on the 2010/11 Ethiopian Social Accounting Matrix.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "In our analysis, we use Afrobarometer, EPR, and Murdock Atlas data; therefore, we can use LEDA functions to link the different ethnic groups to each other.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["geospatial"]}]}}
{"input": "Incorporation of controls from our cyclone panel database may permit more accurate estimation of the timing and magnitude of responses to these differences.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use the 2018 population census data as a sampling frame for the Colombian sample.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "calculations based on the PATSTAT database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Source: Authors’ calculation based on the 2010/11 Ethiopian Social Accounting Matrix.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we obtain annual country-level total passenger and freight transport traveled by road and rail from the World Road Statistics and the International Transport Forum databases.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "we use data on refugee camps provided by the UNHCR.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Since the JLMPS 2010 does not allow directly identifying Palestinian refugees who are now mostly Jordanian citizens, two indirect methods were employed to identify individuals who are likely to be Palestinian refugees.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The refugee sampling frame in Addis was based on UNHCR’s proGRES registration data, sorted by location.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "activity under the SPF grant also includes support for the Joint Recovery Needs Assessment (JRNA) for Fizuli, Agdam and Jabrayil", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "in this study we adopt these statistically representative subnational units which enable us to compare flood exposure estimates with socio-economic characteristics, such as income levels and poverty", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Size is computed as the log of total assets.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The total number of refugees was set according to the newest data from the PESEL registry", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "the matching process determines for each individual the number of sex- appropriate public basic and secondary schools per 1, 000 individuals available in the individual ’ s subdistrict of birth when the individual was of age to accede to this educational level", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "pseudo EAs were created from the proGRES database by grouping 150-200 households consecutively.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "During these phone interviews structured questions were asked about welfare of the household.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Percentage of completeness of reporting by facilities", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "From the Ethiopia DRDIP data set, we derive two measures of livelihood diversification and two measures of agricultural commercialization (all at household level).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The Systematic Tracking of Exchanges in Procurement (STEP) system will be used for all procurement activities;", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "Refugees are included in a Civil Registration and Vital Statistics Systems (CRVS) and the National Social and Behavior Strategy (awareness raising about the need for vital events registration).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "A tailored Refugee Self-Reliance Index (RSRI) prepared by the GoR, the World Bank and UNHCR measures refugee self-reliance in Rwanda.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Own calculations using POF 2017/18 data and TBCA, based on food items categorizations by IBGE.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the pre-simulation analysis implements the deep trade initiatives discussed by the Levant countries prior to the onset of the Syrian war in 2011.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Of the 3, 500 sources detailed in the overarching UN Global Migration Database, 1, 107 were suitable for analysis, once repeated censuses had been removed or combined.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "As a falsification experiment, we employ the same specification on similar data for Romanian firms, also from the Orbis database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The Socio-Economic Survey of Refugees in Ethiopia (SESRE) plays a crucial role in informing policy decisions by providing comprehensive data on the socioeconomic dimensions of refugees and host communities.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we also added the drive as well as the pedestrian network leveraging the Open Street Map data set to account for commuters who walk or drive to work.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["geospatial"]}]}}
{"input": "the questions are drawn from Haushofer and Shapiro (2016), which are themselves adapted from the Demographic Health Surveys.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The selection of countries is driven by data availability.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Equivalent shock in Eastern Europe was calculated using data for population in this region from World Population Prospects UN.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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 %).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "We extract daily event observations from the UCDP dataset if the location of the actual event is exactly known, the event location is within a radius of less than 25 km around a known point, or at least the administrative district where the event happened is known.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Multidimensional Poverty Index (MPI) is reported to be higher among women than men (27 percent vs. 18 percent in the 15-24 age group and 27 percent vs. 23 percent in the 25-39 age group)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "the PUMA data set interprets Google Earth imagery to produce two layers of potential slum areas (PUMA 2013): areas with defined borders (polygon-data) and potential slum areas without (point-data) defined borders.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The matching process determines for each individual the number of sex- appropriate public basic and secondary schools per 1, 000 individuals available in the individual ’ s subdistrict of birth when the individual was of age to accede to this educational level (six years of age for the basic level and 15 years of age for the secondary level).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to UNHCR mid-2022 statistics, South Sundanese, Somali and Eritrean refugees constitute 46, 29 and 18 percent of the total refugee populations in Ethiopia.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We conduct a field experiment in Sri Lanka de Mel, McKenzie, and Woodruff ( 2012 with the goal of generating data to test whether the exclusion or the exit view ) of informality better reflects reality.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "We sourced bilateral trade value data from WITS and bilateral tariff data from a medley of sources, presented in Appendix Table A1.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "authors' estimations based on ENLACE panel and Formato 911.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "author's calculations using WITS, COMTRADE", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "For our analysis, we use data from January 2007 to December 2020.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Once the food reported in an HCES is converted into calories, the household's calorie intake is compared", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The MoH maintains projects’ books of account using the integrated financial management information system (IFMIS) and manual ledgers.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "the note draws primary on data from the ECOSIT4 survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The number of estimated women at reproductive age (WRA) is based on a calculation of the affected population [INGC] * % of WRA [2017 census].", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "The WorldPop data sets have been used to identify settlement areas in Myanmar and Vietnam", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The expected payout is estimated using the APHRODITE dataset, for the period 1961–2004.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "food consumption expenditure in Tanzania based on the Household Budget Survey 2000/01.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Access to political power is ranked on a scale from 1 to 7 in the GROWup dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "For the city of Bogotá, we use the same database discussed earlier to examine if poor people are at greater risk from natural hazards – particularly earthquakes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "For the general population we used employment in the 15+ age group in Q2 2023 from the Eurostat Labour Force Survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We exploit only the data on “ refugees ”.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Shock to population was calibrated to match data for residents of Poland from Statistics Poland and number of refugees based on PESEL UKR.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The selection of countries is driven by data availability.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "According to the GLWD-3 and the DTM, 76 countries and territories in five World Bank regions have coastal wetlands which are currently at 1 m from sea level.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We draw our emissions estimates from the World Resources Institute’s Climate Analysis and Indicators database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the general population is taken from the 2022 Labour Force Survey for 15-74 (broadest available) age group", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The differentiation of the Pashtun and non-Pashtun districts does not extend to Pashtun and non-Pashtun households in the LEAPS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we compare the implications of the use of a proxy respondent versus the recommended self-respondent approach and the use of aggregated land data versus parcel-level land data (recommended).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the only available data that was spatially explicit was the headcount and headcount rate, which we use for the analysis.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "this paper combines newly available data on the 2014 distribution of 1. 6 million Syrian refugees across subregions of Turkey and the Turkish LFS, to assess the impact on Turkish labor market conditions.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The Civilian Impact Monitoring Project is a service under the United Nations Protection Cluster for the collection, analysis and dissemination of open source data on the civilian impact from armed violence in Yemen, to inform and complement protection programming.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "used the underlying NPP-VIIRS DNB Daily Data to analyze selected natural disasters.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Her third approach uses United States census data to look for patterns in the sex ratios of children born to Chinese immigrants, who are assumed to have levels of HBV prevalence similar to their place of origin.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Kotsadam and Tolonen (2016) use DHS data from Africa, and find that mine openings cause", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We use student identification variables to identify 20,187 twins in the ENLACE panel.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Own calculations using POF 2017/18.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Authors’ estimation based on 2018 School Census data and reported USD/SDG exchange rate (Economist).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "We have two estimates of eδs, one obtained using reported income data, and the other based on reported consumption data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "compare recall questions on food spending from the Canadian Food Expenditure Survey to data from expenditure diaries.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "This study estimates the location of people using the Global Human Settlement Layer (GHSL), produced by the EC JRC.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["geospatial"]}]}}
{"input": "Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "The indicator will track the percentage of administered COVID-19 vaccines which are captured in the national vaccination digital registry.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Climate sensitive planning for surveys will be used and questions on climate and health impacts will be included in the survey to generate relevant data to inform decision making.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Valuing quantities of WFP food aid with prices from SESRE suggests that if food aid quantities were received/reported, refugees’ food expenditure would be 9,440 birr slightly above the values we get in SESRE of 13,898 birr, but still low compared to hosts at 28,324 birr.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We exploit the specific panel structure of the LFS dataset, described in Section 3. 1 by focusing on one cohort of the same respon- dents who were surveyed in 2019Q1, 2019Q2, 2020Q1, 2020Q2, and finally 2020Q4.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "only the information on enrollment was used for the academic year 2023-24.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The survey was used to draw a profile for skills and potential opportunities for refugees and host", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "All data gathered through the LSMS is published online in the Bank ’ s Central Microdata Catalog.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["microdata"]}]}}
{"input": "we examine the flood extent both for all urban areas (the whole HCMC province) and for those areas defined as potential slums (from the PUMA data set) to examine how exposure to floods is different in slum areas.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "author's calculations using WITS, COMTRADE", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "we then calibrated refugee employment to match the NBP’s 2022 survey", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Access to political power is ranked on a scale from 1 to 7 in the GROWup dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The 2002 population census was used as a sample frame.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "For the purposes of computing TFP, annual data on all inputs, outputs and prices were collected at the district level during the period 1961-1994.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "Starting from the newly constructed database, the pre-simulation analysis implements the deep trade initiatives discussed by the Levant countries prior to the onset of the Syrian war in 2011.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "The Civilian Impact Monitoring Project is a service under the Protection Cluster for the collection, analysis and dissemination of open source data on the civilian impact from armed violence in Yemen, to inform and complement protection programming.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Based on national labor survey data (EPAM 2010), it appears that mean income from mining activity for each active worker is higher than the average income for all other activities, especially the agricultural and industrial sectors in the Sikasso region.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the 69% for Ukrainian refugees in the SEIS 2024 survey and 73% when adjusted for a different sex and age structure.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The regression results reveal that credit constraint status has a significant positive effect on employment growth among private and formal firms in Turkey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "by leveraging data from initiatives like SESRE and adopting a comprehensive approach that considers the needs of both refugees and host communities", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Author's calculations based on 2008 ILCS and 2009 GDP data", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "author's calculations using WITS", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we use pooled cross-sections from the European Social Survey (ESS)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we can use LEDA functions to link the different ethnic groups to each other.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The GBV Information Management System (GBVIMS) has recorded a marked rise in the number of reported incidents of violence in 2020.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "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": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We will base our later analysis on these population movements.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "The KHDS 1991-1994 serves as the baseline data for this paper.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Source: World Bank Staff based on SESRE", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The dataset takes the PRIO / Uppsala Armed Conflicts Dataset as its point of departure.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Enhance the capacity of GoE to include 1,000,000 refugees into the national Central Statistics Service (CSS)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The 2002 population census was used as a sample frame.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "National Learning Assessment conducted in all 18 states of Sudan found that Grade 3 students performed very poorly.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the World Bank (2010) estimates that to meet the growing demand for food between 2005 and 2055, agricultural productivity will need to rise by 64% under the assumptions of the “business-as-usual” scenario and by a further 80% to offset the projected stresses arising from climate change", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use only the number of killings to identify years and districts affected by the conflict.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "the survey finds positive outcomes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Authors’ analysis based on data from BASIX.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "MSNA Poland 2023 survey also shows that when asked whether they faced any challenges obtaining enough money to meet their needs, the refugees were nearly equally split (around 0.5% more reported no difficulties), with an additional 8% not knowing or refusing to answer.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Publicly available information on refugees comes from an AFAD survey of 2, 700 households in June and July 2013.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "our method of wage estimation based on SEIS household incomes", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Second, a country surface for wetlands was constructed from the polygons extracted from the Global Lakes and Wetlands database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Source: RHCS 2018, WB staff calculations.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: Deloitte own elaboration based on the PESEL database as of September 2024 and GUS population data as of mid-2024.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "When the regressions are run with the village characteristics from the 2005 Indian census, the coefficients of interest do not change significantly.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "an index of \" _proximity_ _in_ _sectoral_ _composition_ \" based on the World Development Indicators.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "average values from recent years for intake into Grade 1 of basic education (from the School Census) relative to population, and recent trends in promotion and retention in each grade of basic school.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Authors’ estimates based on MICS2014/15.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "This survey contained a similar module as the phone surveys and collected information on the vaccination status of all household members.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The indicator will track the percentage of administered COVID-19 vaccines which are captured in the national vaccination digital registry.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "We then use international data (EMDAT 2009) to estimate the relationship between storm damages and national income and population density (vulnerability).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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 %).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "Consequently, it yields sufficient madrassa enrollment to examine correlations with household attributes in a meaningful manner", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Tol 2007 drew on the Global Vulnerability Analysis and other country studies for quantification of wetland loss from 1 m SLR for a global analysis including small island nations.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Conflict incidence is measured through the number of battle-related deaths from UCDP / PRIO dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "allowing us to measure the local supply of each type of schools in each subdistrict in every year", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "we then calibrated refugee employment to match the NBP’s 2022 survey (NBP, 2024), the UNHCR’s 2023 MSNA, and the 2024 SEIS survey - implying their employment share rose from 1.5 percent to 2.4 percent of total employment in Poland.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we compared the GTFS transit feed mapped under dry and wet conditions and evaluated changes in headways, blockages of roads and rerouting, and travel speeds.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The best population data available is the active PESEL database as of 10th October 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "In our analysis, we use Afrobarometer, EPR, and Murdock Atlas data; therefore, we can use LEDA functions to link the different ethnic groups to each other.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We exploit the specific panel structure of the LFS dataset, described in Section 3. 1 by focusing on one cohort of the same respon- dents who were surveyed in 2019Q1, 2019Q2, 2020Q1, 2020Q2, and finally 2020Q4.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The project will use the Systematic Tracking of Exchanges in Procurement (STEP) system to provide data on planned procurement activities, establish benchmarks, monitor delays,", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "Poverty data: CAS, UNDP and MoSA Living Conditions and Household Budget Survey 2004-5,", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "According to the UNHCR (2023) survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The level of urbanization, and the value added from agriculture, manufacturing, and service sectors are all obtained from the World Bank World Development Indicators database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "I estimate random intercepts for 5I excluded Zimbabwe from the analysis because of missing data on key variables.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Most of the analysis presented in this chapter is based on detailed consumption data from the Socioeconomic Survey of Refugees in Ethiopia (SESRE) conducted between October 2022 and February 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "accommodation rent support has been calculated as the difference between the actual equivalized accommodation expense and the median equivalized market rent in the region", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: Deloitte own elaboration based on SEIS UNHCR survey conducted in May and June 2024.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "_Source:_ Own computation from 2005/06 UNHS III", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "of enumeration areas was based on the Population Census 2011, and a fresh household listing was conducted in each of the selected EAs to attain a current household sampling frame from which to randomly select 12 households in each EA.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "according to Ukrainian refugee’s median in the NBP’s 2024 survey", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Source: Deloitte own elaboration based on SEIS UNHCR survey conducted in May and June 2024.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This cohort will also benefit from the issuance of national ID cards which for the first time will place them on the Zambia national registry.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "Data source|DHIS2|", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We have computed the per person per month in-kind aid quantities into annual values using the household size and prices from SESRE and mapped them to the closest food item in SESRE (this was not straightforward as the items are different) considering food ration change periods.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "a brief introduction of the country context is presented alongside the poverty estimates by the $ 1. 90 / day measure and the global Multidimensional Poverty Index (MPI).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "WHO to provide data / TPM to verify", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["report"]}]}}
{"input": "We extract daily event observations from the UCDP dataset if the location of the actual event is exactly known, the event location is within a radius of less than 25 km around a known point, or at least the administrative district where the event happened is known.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: Authors’ calculation based on ALCS 2013–14", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "This is consistent with the national figures from the 2007 DHS survey, which found labor force participation rates of 34 % for women aged 15-19 and 49 % for women ages 20-24.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we use gridded population density data with a 1 km resolution from Landscan", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We also use the ENLACE panel to examine the relationship between Grade 6 test scores", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "only the information on enrollment was used for the academic year 2023-24.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use two complementary geocoded household data sets to analyze outcomes in Ghana: the Demographic and Health Survey (DHS) and the Ghana Living Standard Survey (GLSS), which provide information on a wide range of welfare outcomes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the DHS Program has collected, analyzed and disseminated data on population, health, HIV and nutrition through more than 300 surveys in over 90 countries.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we produce a Work Rights Index, composed of questions around whether respondents feel that women should be allowed to work inside or outside the home or the camp block.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Thus, we use the food item description in POF to look up corresponding items and calorie intake per kilogram (kg) in TBCA.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The dataset takes the PRIO / Uppsala Armed Conflicts Dataset as its point of departure.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to the Global Peace Index, in 2016 the country ranks the fourth less peaceful after Syria, South Sudan and Iraq.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "The variable is constructed from the global CRU TS3.21 dataset from the University of East Anglia, containing a long-term time series of monthly rainfall levels at 0.5x0.5 grid resolution, which was produced using statistical interpolation based on data from 4,000 weather stations (Harris et al., 2014).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use two main data sources for our estimation of a poverty line for Brazil: the 2017/18 Household Budget Survey (Pesquisa de Orçamentos Familares; POF) and the Brazilian Table of Food Composition (Tabela Brasileira de Composição de Alimentos; TBCA).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Authors calculation", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "We use annual series of income, consumption, and investment data from Penn World Tables 7.0, all in 2005 international dollars per capita terms.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we can see that incomes earned in Poland grew from 81% in 2023 to 90% in 2024.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The process is led by the Department of National Registration, Passports and Citizenship under the MoHAIS through the Integrated National Registration Information System (INRIS) project.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This was combined with data on four types of natural disasters - droughts earthquakes, floods, and hurricanes/storms - for 196 countries, taken from the Emergency Events Database (EM-DAT).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the DHS Program has collected, analyzed and disseminated data on population, health, HIV and nutrition through more than 300 surveys in over 90 countries.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "according to MSNA Poland 2023 survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "In addition, UNHCR also supports States through the joint use of the digital tools contained in UNHCR’s Population Registration and Identity Management Ecosystem (“PRIMES”), including its biometrics systems.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "To construct our instrument we use the Syrian Labor Force Survey for 2010 (the year before the beginning of the war).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "A tailored Refugee Self-Reliance Index (RSRI) prepared by the GoR, the World Bank and UNHCR measures refugee self-reliance in Rwanda.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ´ ın, Colombia.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The Socio-Economic Survey of Refugees in Ethiopia (SESRE) is a representative survey of the refugee population in Ethiopia and their host communities, the first of its kind.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Probability of being in school was estimated using a Probit model and ALCS 2013–14 data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Conflict incidence is measured through the number of battle-related deaths from UCDP / PRIO dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to the JD ‐ HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "the data source is the “Revised cash transfer value from Oct 2022_refugee camps” file received from the WFP document that helps to get information regarding the changes in cereal cash equivalent – data on cereal cash equivalent for cash camps which is used to calculate cereals provided in those camps and cash transfer value per year.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We complement the analysis with household data from the GLSS collected in the years—1998– 99, 2004–05, and 2012–13.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The data set this study uses is the Labor Force Survey (LFS) since there is an adequate availability of both migration and geographic variables.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: Deloitte own elaboration based on SEIS UNHCR survey and GUS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "Source: Deloitte own elaboration based of Eurostat data (Labour Force Survey).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The Socio-Economic Survey of Refugees in Ethiopia (SESRE) plays a crucial role in informing policy decisions by providing comprehensive data on the socioeconomic dimensions of refugees and host communities.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The 2008 ILCS data is used in the simulation of the impact of gasp price hike on April 1, 2010.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the PUMA data set interprets Google Earth imagery to produce two layers of potential slum areas", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We exploit only the data on “ refugees ”.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "UNHCR (2025b), using a different method than the one in this report, estimated average instead of median net wages based on the SEIS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Ideally, the ILCS data would be matched with household-level data from the utility companies on gas and other alternative energy consumption and payment to allow a more accurate analysis of the residential demand and the distributional consequences of price changes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the survey finds positive outcomes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we used the World Integrated Trade Solution (WITS) database on the SITC, Revision", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "1 UNHCR Niger montlhy PoC statistics, Juin 2023", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "A tailored Refugee Self-Reliance Index (RSRI) prepared by the GoR, the World Bank and UNHCR measures refugee self-reliance in Rwanda.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "We exploit the specific panel structure of the LFS dataset, described in Section 3. 1 by focusing on one cohort of the same respon- dents who were surveyed in 2019Q1, 2019Q2, 2020Q1, 2020Q2, and finally 2020Q4.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "We translate these residuals to emissions using the EDGAR global database of gridded CO2 emissions estimated from local activity measures and standard emissions parameters (Crippa et al.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "according to Ukrainian refugee’s median in the NBP’s 2024 survey", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "When ZUS data is aggregated into these occupational groups with the assumption that wage structure remains the same as in October 2022, the rate at which Ukrainian", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Third, migratory potential of wetlands were assigned from the WMP classification of the coastline from the DIVA database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "In the present analysis, we use 2, 530 of these.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "More generally, in this paper, we have used the Tornqvist-Theil index as an example to illustrate the problem with conventional growth accounting in the presence of biased technical change.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "When changes are compared to the 2023 edition, the MultiSector Needs Assessment (MSNA) survey, 5% of households include a person with disability, down from 10% previous year, and 48% include a chronically ill person, compared to 49% in 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "To construct our instrument we use the Syrian Labor Force Survey for 2010 (the year before the beginning of the war).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "use sampling weights derived from the 2018 EHCVM sampling frame and adjusted for response rates", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "This is derived from authors ’ own calculations using the 2016 Jordan Labor Market Panel Survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use GDP data from the Word Development Indicators of the World Bank.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "We combine data from BMD, IMD and IBTrACS to document all recorded cyclonic storms in the Indian Ocean region from 1877 to 2016.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "roughly 88 percent of refugees in Ethiopia remain in camps based on SESRE data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "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)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Finally, the GEMStat data from UNGEMS is used for the global analysis.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "For rivers and lakes, the CIA World Data Bank II (CIA 1972) was used.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Using the spatial distribution of rice production based on the 2010 SPAM, the latest district-level rice production data for the period of 2013-15 are disaggregated into 3,198 locations or pixels.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To test these hypothesis we use data on ethnic groups ’ access to executive power and night light intensity from the GROWup Research Front-End (RFE Release 2. 0) dataset and executive constraint data from the Polity IV dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the survey finds positive outcomes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "For all demand-driven activities (local consumption goods, hired agricultural labor, tractor services), market equilibrium is established by allocating demand to suppliers based on the market shares observed in the SAM.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "UNICEF to provide data / TPM to verify", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The poverty map for 2009 is the result of collaboration between the World Bank and INSTAT (National Statistics Institute) and uses data from the 2009 census and the 2010 ELIM.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Analytical report based on the land matrix database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "we use again information provided by the Penn World Table and World Bank databases.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use data from the Labor Force Surveys (LFS) of the West Bank and Gaza and we focus on 20-59 years old men.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we compared the GTFS transit feed mapped under dry and wet conditions and evaluated changes in headways, blockages of roads and rerouting, and travel speeds.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To construct such estimate, we use the 1995 / 96 NLSS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We use data from the Labor Force Surveys (LFS) of the West Bank and Gaza and we focus on 20-59 years old men.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the share of Ukrainian refugee household incomes derived from work in Poland has increased from 74% in the July-August 2023 MSNA survey to 76% in the May-June 2024 SEIS survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "PISA 2022 mean scores for Costa Rica in Reading are above LAC’s average (415 vs. 400), as well as mean scores in Mathematics (385 vs. 374) and Sciences (411 vs. 400).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ´ ın, Colombia.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we can unpack those risks in greater detail using the MPI.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to newly released 2021 Labor Force Survey (LFS) data, about 80 percent of Ethiopians live in rural areas, where roughly 75 percent work in agriculture.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Data source|DHIS2", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the second type of data, based on household surveys, are different rounds of the Pakistan Integrated Household Survey (PIHS) carried out in 1991, 1998 and 2001.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "main specification and run a regression of ENLACE test scores in a particular subject", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use rich geocoded data with information on households and mining production over time to evaluate the gold boom at the local and district levels in difference-in-differences analyses.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Pluvial and fluvial flood maps from the Fathom global flood model were first clipped to the bounding box [9] of the Kinshasa city and then mosaiced together using the maximum operator so that the maximum flood depth from both fluvial and pluvial flood estimates were preserved.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Our calculations based on the UNHCR MSNA Poland 2023 survey results show that 80% of refugee income comes from employment, with other sources on average playing a much lesser role.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Authors' calculations from ODHD (Sustainable Development Observatory) data 2003, 2006, 2008.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "For its empirical work the note draws primary on data from the ECOSIT4 survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We leverage a unique large-scale experiment on consumption measurement in Iraq designed for the Iraq Household and Socio-Economic Survey (IHSES) in 2012.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "In addition, UNHCR also supports States through the joint use of the digital tools contained in UNHCR’s Population Registration and Identity Management Ecosystem (“PRIMES”), including its biometrics systems.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The project will use the Systematic Tracking of Exchanges in Procurement (STEP) system to provide data on planned procurement activities, establish benchmarks, monitor delays, and measure procurement performance.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "the valuation studies by Tol (2007) and Anthoff et al. (2010) are also based on wetland losses derived from the Global Vulnerability Analysis (Hoozemans et al. 1993)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We complete this data with exact geographic location data from MineAtlas (2013), where satellite imagery shows the actual mine boundaries, which allows us to identify and update the center point of each mine.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the location of the slum areas in the PUMA data set are mainly restricted to the old town.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Deloitte own elaboration based on the PESEL database as of October 2023", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "In this study, we use the FAO's Food Insecurity Experience Scale (FIES) as primary outcome of interest.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Kenya Demographic Health Survey, 2022. Key Indicators Report", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "According to the active population in the PESEL database, over 30% of all PESEL UKR holders had them issued in the country’s 12 biggest cities.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "During these phone interviews structured questions were asked about welfare of the household.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "First, we try to replicate the results estimated with the DHS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "To test these hypothesis we use data on ethnic groups ’ access to executive power and night light intensity from the GROWup Research Front-End (RFE Release 2. 0) dataset and executive constraint data from the Polity IV dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We calculate the average capital productivity as output-side GDP divided by total reproducible capital within a country, both variables from Penn World Tables.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Using this information, we perform a simulation to quantify the magnitude of agricultural losses that saline water causes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "The coastlines are derived from the SRTM 90 meter digital elevation model (DEM) data files used as a mask for calculating country totals for wetlands.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Deloitte own elaboration based on ZUS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Conflict incidence is measured through the number of battle-related deaths from UCDP / PRIO dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Salaries and wages are taken from Statistics Poland BDL GUS database for 2022, but cover only the enterprise sector (firms with 10 or more employees).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "According to the Barro and Lee educational attainment dataset, seven out of the top 20 countries in terms of increase in number of years of schooling from 1980 to 2010 were Arab countries (Barro and Lee 2013).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "These were computed using the SRTM3 data (Jarvis et al. 2006) by subtracting the minimum elevation in a 1 kilometer grid from the maximum elevation in the 1 kilometer grid.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The paper extends the GTAP 8 database by separating Lebanon, Jordan, Syria, Iraq, and West Bank and Gaza from the rest of the Western Asia aggregate and Algeria and Libya from the rest of North Africa.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "using the spatial location of potential slums from the Platform for Urban Management and Analysis (PUMA) data set", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "This report aims to assess the livelihood situation of this population based on the 2024 round of data collected by the Socio-Economic Insights Survey (SEIS), which received responses from 8,723 households containing 19,803 individuals.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "calculate the total number of people exposed and add the results to the World Bank global administrative map shapefile", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "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": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "In our analysis, we use Afrobarometer, EPR, and Murdock Atlas data; therefore, we can use LEDA functions to link the different ethnic groups to each other.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We will base our later analysis on these population movements.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "We use microdata from the DHS, obtained from standardized surveys across years and countries.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This was combined with data on four types of natural disasters - droughts earthquakes, floods, and hurricanes/storms - for 196 countries, taken from the Emergency Events Database (EM-DAT).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "The 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).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "one obtained using reported income data, and the other based on reported consumption data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Conflict incidence is measured through the number of battle-related deaths from UCDP / PRIO dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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;", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To construct such estimate, we use the 1995 / 96 NLSS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We combine the DHS and GLSS with production data for 17 large-scale gold mines in Ghana.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the one that we concentrate on here, is to use HCES to derive hunger statistics", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The TA and financial support provided under the project will build on the successful World Bank financed Zambia Agribusiness and Trade Project (P156492) model.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The data for our study come from the 2005/2006 Uganda National Household Survey (UNHS), fieldwork for which was conducted by the Uganda Bureau of Statistics from May 2005 to April 2006.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we use the EDGAR gridded database of CO2 emissions estimated from sectoral activity data and standard emissions parameters", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "The matching process determines for each individual the number of sex- appropriate public basic and secondary schools per 1, 000 individuals available in the individual ’ s subdistrict of birth when the individual was of age to accede to this educational level (six years of age for the basic level and 15 years of age for the secondary level).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the questions are drawn from Haushofer and Shapiro (2016), which are themselves adapted from the Demographic Health Surveys.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Ukrainian refugees have been allocated to firm sizes based on the July-August 2023 UNHCR (2023) survey", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we use the EDGAR gridded database of CO2 emissions estimated from sectoral activity data and standard emissions parameters", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "we restrict our analysis to all origin – destination country pairs that are at a maximum distance ≤ 950 km from each other.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Monitoring COVID-19 Impact on Refugees in Ethiopia : Results from a High-Frequency Phone Survey of Refugees (English).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "As per the 2024 Socio-Economic Insights Survey conducted by UNHCR, this figure could be as high as 79% amongst working-age Ukrainian refugees.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "To test these hypothesis we use data on ethnic groups ’ access to executive power and night light intensity from the GROWup Research Front-End (RFE Release 2. 0) dataset and executive constraint data from the Polity IV dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We use only the number of killings to identify years and districts affected by the conflict.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we collapse the data to 10 deciles of daily per capita kilocalories available and then calculate the CV", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This is derived from authors ’ own calculations using the 2016 Jordan Labor Market Panel Survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We obtain ethnicities of refugees from the EPR-ER dataset,", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We used the data serie called \"NY.GDP.MKTP.KD\"", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the World Bank’s GMD database provides them at the subnational level for most countries.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "we then calibrated refugee employment to match the NBP’s 2022 survey (NBP, 2024), the UNHCR’s 2023 MSNA, and the 2024 SEIS survey - implying their employment share rose from 1.5 percent to 2.4 percent of total employment in Poland.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The number of communities selected in each province was based on information from the 2008 Census.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "which in 2022 and 2023 stood in Poland at 48% according to the European Commission’s AMECO database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "all entries for the two composite regions from the GTAP database were removed from the database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Our estimates take into account the exposure of wetlands derived from the recent GLWD-3 database to 1 m SLR and the estimated capacity of the coastline to retreat and for coastal wetlands ecosystems to move (or migrate) inland as the coastline is receding.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "The table uses GLSS data for Ghana for the survey years 1998, 2005, 2012.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "17.0 percent (2.8 million people) facing acute food insecurity, and 940,000 children 6-59 months and 145,000 pregnant or lactating women acutely malnourished.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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 %).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}