impact/validation.jsonl

{"input": "In this paper, we use our survey data to estimate the incidence of IAP exposure for family", "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": "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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Deloitte elaboration based on the MSNA Poland 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": "A new global coastal database for impact and vulnerability analysis to sea-level rise.", "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": "The information contained in these reports was then included into the HRVD 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": "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": ["survey"]}]}}
{"input": "First, only gridcells with at least 30 percent cropland according to the ESA dataset are included.", "output": {"classifications": [{"task": "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": "Using data from the Armenia Land Tenure and Area (ALTA) study and in the context of land tenure rights, this paper addresses questions on: (i) the data quality implications associated with respondent strategy; (ii) the data quality implications associated with level of data collection; (iii) the interaction of biases stemming from the use of proxy respondents and aggregated level data collection, as relevant; and 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": ["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 the FAO Food Composition Tables for the Near East to convert daily food quantities into kilocalories; we then divide by the effective household size to get per capita daily caloric intake.", "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": "Sources: 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": "Iraq ranks among the lowest in the region on Transparency International’s Corruption Perception Index.", "output": {"classifications": [{"task": "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 use an alternative linkage based on the relations between sets of language nodes associated with two groups.", "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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"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 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": "Educational Attainment and Enrollment Profiles: A Resource Book based on an Analysis of Demographic and Health Survey 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": "The paper uses the Romanian firm-level data from the Ministry of Finance covering enterprises of all sizes from 2011 to 2020, combined with the new World Bank Businesses of the State dataset, which tracks ownership of state business entities with at least 10 percent stake 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": ["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": ["primary"]}, {"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": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "ZUS (social security) statistics on insured refugees indicate that around 5% of them have set up a business or are freelancers.", "output": {"classifications": [{"task": "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 welfare of the displaced – and those who returned – is monitored by combining a baseline survey with structured monthly follow-up interviews carried out by mobile phone.", "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": ["survey"]}]}}
{"input": "In Table 6, we present the average disparities in percentile rank on the Peabody scale, revealing that Venezuelan children and adolescents, who are forcibly displaced, score approximately 12 p. p. lower than their Colombian counterparts.", "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 2024, a social-economic lens was added in assessing the needs of refugees in the SocioEconomic Insights Study (SEIS) conducted in ten countries (Bulgaria, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Republic of Moldova, Romania, and Slovakia).", "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": "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 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": "We compute R [2] 's between CRU and each of the eight GCMs for temperature and rainfall", "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": "Similar results can be gleaned from the MSNA Poland 2023 survey results, which show that slightly more than 5% of respondent households receive income from self-employment or similar 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": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "To guide the identification of slums, previous work has provided information on the appearance and geographical extent of slums in HCMC.", "output": {"classifications": [{"task": "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 DFO flood maps were overlaid with the location of the rivers taken from the Hydroshed 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": ["other"]}]}}
{"input": "Source: Authors’ calculation based on UNHCR and SIOCC-UNDSS 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 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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "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": "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": "to the MSNA Poland 2023 survey, 20% of Ukrainian refugee households earn less than 3 000 PLN, 41% earn between 3 000 and 6 000 PLN, and 12% earn more than 6 000 PLN, while 27% of respondents preferred not 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": "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 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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The data from the LEAPS census asked about ethnic and caste identity, and households that classified themselves as “ Pathan ” or “ Afghani ” were used to represent Pashtun households.", "output": {"classifications": [{"task": "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": "In the NBP (2023) survey conducted in November 2022 the net income of refugees oscillated between 2,000 and 3,000 PLN, while the net income of pre-2022 migrants was closer to between 3,000 and 4,000 PLN.", "output": {"classifications": [{"task": "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": "maps from the Gridded Population of the World v3 (CIESIN-CIAT 2005) to obtain", "output": {"classifications": [{"task": "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": "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": "Since the United Nations Trends in International Migrant Stock details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition.", "output": {"classifications": [{"task": "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 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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Poverty rates are calculated based on $2.15 in the 2017 PPP line using total consumption and pre-assistance income", "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": "Average commune household consumption is predicted using a consumption model calibrated to the 1998 VLSS.", "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": "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": ["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": "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": ["other"]}]}}
{"input": "Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007).", "output": {"classifications": [{"task": "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 ’ 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": "Productivity of firms by size is based on gross value added per person employed in industry, construction, and market services sectors (broadest available) in 2021 from Eurostat Structural Business Statistics.", "output": {"classifications": [{"task": "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": "as obtained from the World Bank household survey database, Section 3.4", "output": {"classifications": [{"task": "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 also use the Global Aridity Index and Potential Evapotranspiration Climate Database (Trabucco and Zomer 2019) to differentiate grid cells based on their aridity.", "output": {"classifications": [{"task": "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 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": ["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 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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t.", "output": {"classifications": [{"task": "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": "which is based on travel paths simulations using OD pairs from the JICA travel 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": "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": "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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"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": "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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["microdata"]}]}}
{"input": "First, the SRTM database was used to identify inundation zones.", "output": {"classifications": [{"task": "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 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": "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": ["background"]}, {"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": "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": ["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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "890,825 refugees enrolled in the Level 3 Registration and Biometric Identity Management System (BIMS).", "output": {"classifications": [{"task": "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": "We use the database for an analysis of cyclone strike locations and impact zones in seven historical periods: 1877-1900, 1901-1920, 1921-1940, 1941-1960, 1961-1980, 1981-2000 and 2001-2016.", "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": ["database"]}]}}
{"input": "we rely on geo-coded conflict fatality data provided by the Armed Conflict Location Event Dataset (ACLED)", "output": {"classifications": [{"task": "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": "Mean wages have been estimated by dividing household employment income by the total number of working hours and then computing a weighted average across households with weights proportional to total working hours.", "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": ["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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To carry out this analysis, the information available in the O * NET (Occupational Information Network) database was used in conjunction with the 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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"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": "The Oxford COVID-19 government's response tracker (Hale _et_ _al._, 2020) suggests that the Mali government, in order to control the spread of the virus, imposed restrictions as stringent as in North American and Western European countries (see Figure A1 in the Supplemental Appendix).", "output": {"classifications": [{"task": "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 on GDP and other indicators used in the analysis (surface of country territory, electricity consumption, etc.) are provided by the World Development Indicators (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": ["indicator"]}]}}
{"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 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": "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": "According to NRVA 2007–08 data, approximately 85 percent of Afghan households reported to have been negatively affected by a “large influx of returnees” during the 12 months preceding the survey.", "output": {"classifications": [{"task": "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 provides valuable information to development partners and governments to inform policies to facilitate refugees’ integration and improve their lives, along with refugee hosting 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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "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": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"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": ["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 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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Predicted marginal probabilities of being employed based on the labor force participation rate of the local market, tabulated by employment experience.", "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": "Clusters from  DHS 2000 were geomatched with DHS 2007 to estimate the exposure to unimproved sanitation when children in the 2007 sample were 1 or 2 years old.", "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 higher bound is the product of employment rates from surveys of refugees from Ukraine, and their working age population from the active PESEL UKR 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": "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": "First, nominal consumption is adjusted for price differences across survey domains using spatial deflators calculated using the Household Welfare Statistics (HoWStat 2021) survey 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 cartella sociale, or social file, records information related to the reception and social inclusion path of UASC in Italy.", "output": {"classifications": [{"task": "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": "using baseline data from the experimental intervention, which is discussed in more", "output": {"classifications": [{"task": "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 international data set was used to estimate the coefficients for vulnerability (income and population density).", "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 3B42RT daily derived product is what is used in 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": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To also capture riverine floods, which may occur downstream following heavy rainfall in the mountains or as a result of storm surges in coastal areas, riverine flood indicators will be constructed based on the satellite based flood data from the Dartmouth Flood Observatory (DFO) (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": ["other"]}]}}
{"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": ["other"]}]}}
{"input": "specifically through conducting relevant cross-sectional surveys including, but not limited to, the WHO STEPwise approach to non-communicable diseases (NCD) risk factor surveillance (STEPS) survey, and the Household Health Expenditure and Utilization Survey.", "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": ["survey"]}]}}
{"input": "The welfare of the displaced – and those who returned – is monitored by combining a baseline survey with structured monthly follow-up interviews carried out by mobile phone.", "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": "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": "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 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": "which made high quality data on their demographics easily 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": ["other"]}]}}
{"input": "Trends in monetary poverty are mirrored using the Multidimensional Poverty Index (MPI)—an index measuring deprivations across three dimensions of well-being: education, health, and standard of living (see Box 5.3).", "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": ["indicator"]}]}}
{"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": "Cole, R., and A. Dietrich. 2014. “SME Credit Availability Around the World: Evidence from the World Bank’s Enterprise Survey.” World Bank, Washington, DC.", "output": {"classifications": [{"task": "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 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": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "per capita total expenditure is calculated from the IFPRI survey data for each", "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 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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"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": "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": "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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "estimated based on the SEIS UNHCR survey in chapter 2", "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 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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Determinants of Stunting among under-five Children in Ethiopia: A Multilevel Mixed-effects Analysis of 2016 Ethiopian Demographic and Health Survey 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": "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 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": ["primary"]}, {"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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"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": ["survey"]}]}}
{"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": "the percentage of below Level 2 performers from the top quintile of the PISA index for economic, social and cultural status, was 8%; while for the bottom quintile, the figure was 49%.", "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": "Multi-Sectoral Needs Assessments (MSNA) were conducted under the RRP between June and September 2023 by UNHCR’s Regional Bureau for Europe and its Inter-Agency partners.", "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": "We use data from High-Frequency Phone Surveys (HFPS) in five countries in Sub-Saharan Africa: Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda.", "output": {"classifications": [{"task": "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": "These data were also based on online polls (U-Report on the Move) on the opinions UASC and former UASC on education and training courses received in Italy.", "output": {"classifications": [{"task": "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": ["report"]}]}}
{"input": "Many may opt for civil law contracts and self-employment to limit their social insurance contributions to the level of minimum wage.", "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": "The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers", "output": {"classifications": [{"task": "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": "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": "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": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"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": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"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": "The MPI presented here uses equal nested weights with all four dimensions considered to be equally important, and all indicators within a dimension receiving an equal share of the total weight.", "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 again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity 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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Ethiopia - Rural Income Diagnostics Study: Leveraging the Transformation in the Agri-Food System and Global Trade to Expand Rural Incomes.", "output": {"classifications": [{"task": "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 collect data on bilateral trade flows from the Observatory of Economic Complexity (MIT).", "output": {"classifications": [{"task": "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 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": "According to the Labour Force Survey, which better accounts for emigration than the prevalent population definition, the number of Polish citizens aged 20-64 peaked in early 2010, with 23.5 million people.", "output": {"classifications": [{"task": "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": "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": ["other"]}]}}
{"input": "For elevation, all coastal tiles of 90m Shuttle Radar Topography Mission (SRTM) data, which are 5 geographic degrees latitude and longitude (approximately 500 kilometers by 500 kilometers), were downloaded from http://srtm.csi.cgiar.org/.", "output": {"classifications": [{"task": "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 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": "Source: Based on UNEP Risoe CDM/JI Pipeline Analysis and Database, September 01, 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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "we use gridded population density data with a 1 km 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": ["geospatial"]}]}}
{"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": ["other"]}]}}
{"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": "we use an alternative linkage based on the relations between sets of language nodes associated with two groups.", "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 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": "Population data is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).", "output": {"classifications": [{"task": "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 novelty is that a new, more spatially and temporally detailed data set for nightlights, the Visible Infrared Imaging Radiometer Suite (VIIRS) product from the National Oceanic and Atmospheric Administration (NOAA), following the work of Elvidge et al. (2017), is employed.", "output": {"classifications": [{"task": "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": "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": "Peduzzi et al. (2009) use a global inventory of recorded flood events from EM-DAT to estimate an exposure indicator at the country 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": "Internationally comparable Programme for International Student Assessment (PISA) data for Costa Rica for 2018 shows that while 28 percent of children from families in the top quintile of the PISA index for economic, social and cultural status were below Level 2 on Reading in PISA (considered the minimum of adequate performance), 72 percent of children from families from the lowest quintile of the PISA index were below level 2.", "output": {"classifications": [{"task": "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"]}]}}