Datasets:

ai4data
/

fcv-data-use-paper

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+---
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- token-classification
+- text-classification
+tags:
+- data-use
+- dataset-mention
+- named-entity-recognition
+- relation-extraction
+- text-classification
+- forced-displacement
+- fcv
+- synthetic-data
+- active-learning
+- world-bank
+- unhcr
+size_categories:
+- 1K-10K
+configs:
+- config_name: extraction
+  data_files:
+  - split: train
+    path: "extraction/train.jsonl"
+  - split: validation
+    path: "extraction/validation.jsonl"
+  - split: test
+    path: "extraction/test.jsonl"
+- config_name: relation
+  data_files:
+  - split: train
+    path: "relation/train.jsonl"
+  - split: validation
+    path: "relation/validation.jsonl"
+  - split: holdout
+    path: "relation/holdout.jsonl"
+- config_name: impact
+  data_files:
+  - split: train
+    path: "impact/train.jsonl"
+  - split: validation
+    path: "impact/validation.jsonl"
+  - split: holdout
+    path: "impact/holdout.jsonl"
+- config_name: synthetic
+  data_files:
+  - split: train
+    path: "synthetic/train.jsonl"
+- config_name: documents
+  data_files:
+  - split: all
+    path: "documents/all.jsonl"
+- config_name: sources
+  data_files:
+  - split: all
+    path: "sources/all.jsonl"
+---
+# Dataset Card for FCV Data-Use Paper
+Companion dataset for the paper **"Automated Tracking of Data Use in Fragile,
+Conflict, and Violence Settings: A Joint World Bank and UNHCR Multitask
+Extraction Framework"** (Macalaba, Solatorio, Brock).
+Three-model swarm over the forced-displacement / FCV corpus: dataset-mention
+extraction (Call 1), relation extraction (Call 1b, 5 types), and impact/usage
+classification (Call 2, 3 tasks) -- plus the seed-based synthetic training data,
+the per-document active-learning corpus, and the per-source original extractions.
+All splits are published verbatim exactly as used in the paper.
+## Configs
+| Config | Splits (records) |
+|---|---|
+| `extraction` | train: 2473, validation: 415, test: 1706 |
+| `relation` | train: 640, validation: 91, holdout: 21 |
+| `impact` | train: 604, validation: 151, holdout: 84 |
+| `synthetic` | train: 1794 |
+| `documents` | all: 4435 |
+| `sources` | all: 4634 |
+## Schemas
+- `extraction` / `synthetic` / `sources`: `{"input", "output": {"entities":
+  {"named_data", "descriptive_data", "vague_data"}, "entity_descriptions"}}`;
+  `extraction/test` additionally carries the model's `predictions`.
+- `relation`: `{"input", "output": {"entities": {"named_data",
+  "organization"}, "relations": [{"<type>": {"head", "tail"}}]}, "_meta"}`
+  with types `has_organization`, `used_by`, `has_acronym`, `has_timeframe`,
+  `has_geography`.
+- `impact`: `{"input", "output": {"classifications": [{"task", "labels",
+  "true_label"}]}}` with tasks `purpose_action`, `usage`, `typology`.
+- `documents`: one row per validated chunk with `document_id`,
+  `corpus_category`, `split`, `page_index`, `page_number`, `text`, `mentions`
+  (verdict, relations, impact fields).
+- `sources`: adds a `source` column (`prwp`, `reliefweb`, `seis`, `esmap`,
+  `refugee_pads`).
+## Citation
+```bibtex
+@misc{macalaba2026fcv,
+  title={Automated Tracking of Data Use in Fragile, Conflict, and Violence Settings},
+  author={Macalaba, Rafael and Solatorio, Aivin V. and Brock, Patrick Michael},
+  year={2026},
+  publisher={Hugging Face},
+  journal={ai4data/fcv-data-use-paper}
+}
+```

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+oid sha256:b06a749032287afbbce477b492d3d12ea41887ab244bb50b737161c8ae7f13ac
+size 10758522

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+{"input": "The expected payout is estimated using the APHRODITE dataset, for the period 1961–2004.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess the impact on Turkish employment and wages.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "Authors ’ presentation based on UNHCR Global Trends 2013 (UNHCR 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": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "To construct such estimate, we use the 1995 / 96 NLSS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "we use again information provided by the Penn World Table and World Bank databases.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "The valuation studies by Tol (2007) and Anthoff et al. (2010) are also based on wetland losses derived from the Global Vulnerability Analysis (Hoozemans et al. 1993).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "Deloitte own elaboration based on Statistics Poland and ZUS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "This paper combines district-level government spending data from Indonesia and natural disaster damage indices to analyze the extent to which districts are forced to reallocate their expenditures across categories after the incidence of floods, earthquakes, and volcanic eruptions.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
+{"input": "we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
+{"input": "This is consistent with the national figures from the 2007 DHS survey, which found labor force participation rates of 34 % for women aged 15-19 and 49 % for women ages 20-24.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "the World Bank’s Global Monitoring Database offers several poverty estimates, which are all derived from the latest available Living Standards Measurement Survey (LSMS) for the respective country.", "output": {"classifications": [{"task": "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": "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": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "according to SESRE data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "Data from this survey is presented under each of the six pathways in this document.", "output": {"classifications": [{"task": "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 Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "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": "Using detailed data on expenditures from a 2017/18 household budget survey and caloric information from the Brazilian Table of Food Composition, calorie intake is assigned to more than 1,400 items to estimate the cost per calorie for a representative group of the population.", "output": {"classifications": [{"task": "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 compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "Authors’ analysis based on data from BASIX and APHRODITE.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "In 2012, average inﬂows in the World Bank dataset were over 9. 5 billion USD and over 3 billion USD in the OECD data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "We now turn to Google's Community Mobility Reports, our second source of information on the intensity of pandemic-related disruptions in Mali.", "output": {"classifications": [{"task": "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": "Probability of having a household member abroad was estimated using a Linear Probability model and ALCS 2013–14 data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "The survey was used to draw a profile for skills and potential opportunities for refugees and host", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["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 indicator will track the percentage of administered COVID-19 vaccines which are captured in the national vaccination digital registry.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
+{"input": "according to the Liberian labor force survey from 2010, the ratio of girls to boys enrolled in primary school has risen from 72 in 2000 to 90 in 2009.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "Of the 3, 500 sources detailed in the overarching UN Global Migration Database, 1, 107 were suitable for analysis, once repeated censuses had been removed or combined.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
+{"input": "use two complementary geocoded household data sets to analyze outcomes in Ghana: the Demographic and Health Survey (DHS) and the Ghana Living Standard Survey (GLSS), which provide information on a wide range of welfare outcomes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
+{"input": "The 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": "ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "as per the SEIS 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": "Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "Similar results can be gleaned from the Multi-Sector Needs Assessment Poland 2023 survey results, which show that slightly more than 5% of respondent households receive income from selfemployment 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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "the household survey was designed in view of combining it with the nationally representative 1998 Vietnam Living Standards Survey (VLSS) to predict baseline consumption expenditures for SIRRV households", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "To construct such estimate, we use the 1995 / 96 NLSS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "we collapse the DHS mining data at the district 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": ["survey"]}]}}
+{"input": "This other World Bank study overlays the same GLOFRIS flood maps as above with geo-localized household surveys (using the Demographic and Health Surveys [8] ) to assess the exposure of poor people to river floods relative to the exposure of non-poor people.", "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": "Deloitte worked with disaggregated household-level data, ensuring comparability (converting all Ukrainian hryvnia incomes into Polish zloty based on daily exchange rates in the time of the interview, and 3-month into 1-month remittance 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": "The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
+{"input": "Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
+{"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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "Tol 2007 drew on the Global Vulnerability Analysis and other country studies for quantification of wetland loss from 1 m SLR for a global analysis", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "We have computed the per person, per month in-kind aid quantities into annual values using the same prices as other food items based on SESRE data, mapping them to the closest food item in SESRE (this was not straightforward as the items are different).", "output": {"classifications": [{"task": "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": "The Multidimensional Poverty Index (MPI) explores this multiple deprivation, capturing differences across three dimensions of well-being: health, education, and", "output": {"classifications": [{"task": "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 consumption and price data from the National Risk and Vulnerability Assessment (NRVA) 2007/08, conducted by the Government of Afghanistan Central Statistics Organization and the Ministry of Rural Rehabilitation and Development.", "output": {"classifications": [{"task": "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 population centers are from the GRUMP settlement points dataset (CIESIN et al. 2004a) and the World Gazetteer database (Helders 2005). Urban boundaries are from the GRUMP urban extents database (CIESIN et al. 2004b).", "output": {"classifications": [{"task": "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": "In the present analysis, we use 2, 530 of these.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "This article utilizes the mobile Vulnerability and Assessment Mapping Survey (mVAM)", "output": {"classifications": [{"task": "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": "Incorporation of controls from our cyclone panel database may permit more accurate estimation of the timing and magnitude of responses to these", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "The 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": "Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
+{"input": "we use information from the COVID-19 phone panel survey, our third source of information used to investigate the intensity of pandemic-related disruptions within Mali.", "output": {"classifications": [{"task": "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 implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "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": "we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "the adapted approach simulates the travel trajectories based on the multimodal transportation model and accounts for public transport waiting times, road speed limits, and designated origin and destination pairs acquired from the JICA commuter travel survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
+{"input": "we use again information provided by the Penn World Table and World Bank databases.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "Using the NLSS data we begin by estimating a regression of the form: yk s = δs + α (ak s − a) + βs (Ek s − Es) + χs (Hk s − Hs) + vk s (4)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "Authors ’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "In our analysis, we use Afrobarometer, EPR, and Murdock Atlas data; therefore, we can use LEDA functions to link the different ethnic groups to each other.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["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 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": ["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 UNEP/Risoe data reports total CERs by 2012 and 2020.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
+{"input": "we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
+{"input": "In robustness checks we also make use of the Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano, et al., 2010) that integrates evapotranspiration into the standard precipitation 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": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
+{"input": "in complement to routine data through DHIS2, in support of the country’s overall HMIS.", "output": {"classifications": [{"task": "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": "Third Party Monitor / PMU Responsible for Monitoring; Measures subcomponent 1.1 Under UNICEF", "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": ["other"]}]}}
+{"input": "We test the two parts of the redistribution hypothesis using the most recent data from Luxembourg Income Study for 20 OECD countries covering the period 1967-2005 (total number of country/years is 110).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
+{"input": "we additionally access a second source of administrative data stemming from the WHO's COVID19 vaccination dashboard (WHO 2020b).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
+{"input": "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"]}]}}

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+{"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"]}]}}

relation/holdout.jsonl ADDED Viewed

	@@ -0,0 +1,21 @@

+{"input": "Source: Deloitte own elaboration based on SEIS (May-June 2024) UNHCR (2024) survey.", "output": {"entities": {"named_data": ["SEIS"], "organization": ["UNHCR", "Deloitte"], "year": ["2024"]}, "relations": [{"has_organization": {"head": "SEIS", "tail": "UNHCR"}}, {"used_by": {"head": "SEIS", "tail": "Deloitte"}}, {"has_timeframe": {"head": "SEIS", "tail": "2024"}}]}, "_meta": {"source": "real_world_verified_pdf"}}
+{"input": "Source: Deloitte own elaboration based on Polish Border Guard Headquarters and PESEL data.", "output": {"entities": {"named_data": ["PESEL data"], "organization": ["Deloitte", "Polish Border Guard Headquarters"]}, "relations": [{"has_organization": {"head": "PESEL data", "tail": "Polish Border Guard Headquarters"}}, {"used_by": {"head": "PESEL data", "tail": "Deloitte"}}]}, "_meta": {"source": "real_world_verified_pdf"}}
+{"input": "Source: Deloitte own elaboration based on ZUS data and GUS population data as of mid-2024.", "output": {"entities": {"named_data": ["ZUS data", "GUS population data"], "organization": ["Deloitte"], "year": ["2024"]}, "relations": [{"used_by": {"head": "ZUS data", "tail": "Deloitte"}}, {"used_by": {"head": "GUS population data", "tail": "Deloitte"}}, {"has_timeframe": {"head": "GUS population data", "tail": "2024"}}]}, "_meta": {"source": "real_world_verified_pdf"}}
+{"input": "Multi-Sector Needs Assessment (MSNA) survey, conducted in July and August 2024, provides data on refugee vulnerabilities in Poland.", "output": {"entities": {"named_data": ["Multi-Sector Needs Assessment"], "acronym": ["MSNA"], "year": ["2024"], "geography": ["Poland"]}, "relations": [{"has_acronym": {"head": "Multi-Sector Needs Assessment", "tail": "MSNA"}}, {"has_timeframe": {"head": "Multi-Sector Needs Assessment", "tail": "2024"}}, {"has_geography": {"head": "Multi-Sector Needs Assessment", "tail": "Poland"}}]}, "_meta": {"source": "real_world_verified_pdf"}}
+{"input": "UNHCR data portal provides refugee statistics. The Labour Force Survey (LFS) from Eurostat is used for employment data.", "output": {"entities": {"named_data": ["UNHCR data portal", "Labour Force Survey"], "organization": ["UNHCR", "Eurostat"], "acronym": ["LFS"]}, "relations": [{"has_organization": {"head": "UNHCR data portal", "tail": "UNHCR"}}, {"has_organization": {"head": "Labour Force Survey", "tail": "Eurostat"}}, {"has_acronym": {"head": "Labour Force Survey", "tail": "LFS"}}]}, "_meta": {"source": "real_world_verified_pdf"}}
+{"input": "The data for this paper come from the European Social Survey (ESS) for the survey years 2002,\n\nobservations per country/year. The ESS covers 36 countries, 24 of which are included in our\n\nCard et al (2012) investigates the drivers of attitudes towards immigrants in Europe using,\n\n\namong others, the following variables of the ESS:", "output": {"entities": {"named_data": ["European Social Survey"], "acronym": ["ESS"]}, "relations": [{"has_acronym": {"head": "European Social Survey", "tail": "ESS"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "(henceforth CRU), provided by the Climatic Research Unit of the University of East\n\nand satellite-based observations. The data enable us to characterize historical climate\n\n<!-- PAGE 6 -->\n**2.1 Assignment of reliability weights to the eight GCMs, based on their**\n**historical \"goodness of fit\" to the CRU data**", "output": {"entities": {"named_data": ["CRU"], "organization": ["Climatic Research Unit"]}, "relations": [{"has_organization": {"head": "CRU", "tail": "Climatic Research Unit"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "The firm-level financial data for 1997 are primarily from the Worldscope database. The World\n\nin Australia and Canada, respectively. Using industry data from Mexico, Blomstrom and Persson\n\nCommission on an annual basis. We use [group-affiliation data from the 1994-1997 lists of business]\n\nby the level of growth of a sector or a country. Indeed, using firm level data, Haddad and Harrison", "output": {"entities": {"named_data": ["Worldscope database"]}, "relations": []}, "_meta": {"source": "real_world_verified"}}
+{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database", "UNHCR Global Trends"], "organization": ["UNHCR"], "year": ["2015"]}, "relations": [{"has_organization": {"head": "UNHCR Statistical Online Population Database", "tail": "UNHCR"}}, {"has_organization": {"head": "UNHCR Global Trends", "tail": "UNHCR"}}, {"has_timeframe": {"head": "UNHCR Global Trends", "tail": "2015"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "<!-- PAGE 9 -->\n|Figure 6a Share of population working primarily<br>in agriculture (%) in mining communes|Figure 6b Share of population working primarily<br>in extractives (%) in mining communes|\n|---|---|\n|||\n|_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.|_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.|", "output": {"entities": {"named_data": ["RGPH"], "organization": ["General Population and Housing Census"], "year": ["1998", "2009"]}, "relations": [{"has_organization": {"head": "RGPH", "tail": "General Population and Housing Census"}}, {"has_timeframe": {"head": "RGPH", "tail": "1998"}}, {"has_timeframe": {"head": "RGPH", "tail": "2009"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "National Poverty line 32% 84% Food security High food insecurity 26% 67% Food insecurity scale 4.0 8.1 Social cohesion Economic competition 33% 49% Increased insecurity 37% 39% Source: Pape et al. (2018) and World Bank Staff based on SESRE 2023.", "output": {"entities": {"named_data": ["SESRE"], "year": ["2023"], "organization": ["World Bank"]}, "relations": [{"has_timeframe": {"head": "SESRE", "tail": "2023"}}, {"has_organization": {"head": "SESRE", "tail": "World Bank"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "We use two main data sources for our estimation of a poverty line for Brazil: the 2017/18 Household Budget Survey (Pesquisa de Orçamentos Familares; POF) and the Brazilian Table of Food Composition (Tabela Brasileira de Composição de Alimentos; TBCA). POF is a nationally representative semiregular survey on income and expenditures in Brazil, conducted every six to nine years.", "output": {"entities": {"named_data": ["Household Budget Survey", "Brazilian Table of Food Composition"], "acronym": ["POF", "TBCA"], "year": ["2017/18"], "geography": ["Brazil"]}, "relations": [{"has_acronym": {"head": "Household Budget Survey", "tail": "POF"}}, {"has_acronym": {"head": "Brazilian Table of Food Composition", "tail": "TBCA"}}, {"has_timeframe": {"head": "Household Budget Survey", "tail": "2017/18"}}, {"has_geography": {"head": "Household Budget Survey", "tail": "Brazil"}}, {"has_geography": {"head": "Brazilian Table of Food Composition", "tail": "Brazil"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "effects is Calahorrano (2011). Using panel data for Germany between 1999 and 2008, she finds   that immigration concerns decrease over the life-cycle.\n\nsimilar to Calahorrano (2011). However, given the lack of comparable panel data surveys for a   large group of countries, we use pooled cross-sections from the European Social Survey (ESS)", "output": {"entities": {"named_data": ["British Household Panel Survey"]}, "relations": []}, "_meta": {"source": "real_world_verified"}}
+{"input": "To mitigate issues arising from the time interval between the 2007 Population Census and EMDHS 2014, only\n\nEMDHS at regional level. The table shows that measured undernutrition rates in EMDHS and the estimated rates\n\nregression of z-scores is estimated in the EMDHS with addition of the SAE estimates. The regression includes the", "output": {"entities": {"named_data": ["Population Census", "EMDHS"], "year": ["2007", "2014"]}, "relations": [{"has_timeframe": {"head": "Population Census", "tail": "2007"}}, {"has_timeframe": {"head": "EMDHS", "tail": "2014"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "6 However, this may reflect differences in rainfall or farming intensity.\n7 We use two poverty maps based on the General Population and Housing Censuses of 1998 and 2009. The poverty\nmap for 1998 was developed by GREAT (Applied and Theoretical Economics Research Group) and combines the\n1998 census and the household survey ELIM (Integrated Light Household Survey) of 2006.", "output": {"entities": {"named_data": ["General Population and Housing Censuses", "ELIM"], "year": ["1998", "2009", "2006"]}, "relations": [{"has_timeframe": {"head": "General Population and Housing Censuses", "tail": "1998"}}, {"has_timeframe": {"head": "General Population and Housing Censuses", "tail": "2009"}}, {"has_timeframe": {"head": "ELIM", "tail": "2006"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database", "UNHCR Global Trends"], "organization": ["UNHCR"], "year": ["2015"]}, "relations": [{"has_organization": {"head": "UNHCR Statistical Online Population Database", "tail": "UNHCR"}}, {"has_organization": {"head": "UNHCR Global Trends", "tail": "UNHCR"}}, {"has_timeframe": {"head": "UNHCR Global Trends", "tail": "2015"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "_Source: Authors' calculation based on Dantlait survey data._\n\n_Source: Dantlait survey_\n\nin 2009 and 2010 for the Livestock Climate and Society (ECliS) project (final report and", "output": {"entities": {"named_data": ["Dantlait survey data"], "year": ["2009", "2010"]}, "relations": [{"has_timeframe": {"head": "Dantlait survey data", "tail": "2009"}}, {"has_timeframe": {"head": "Dantlait survey data", "tail": "2010"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database", "UNHCR Global Trends"], "organization": ["UNHCR"], "year": ["2015"]}, "relations": [{"has_organization": {"head": "UNHCR Statistical Online Population Database", "tail": "UNHCR"}}, {"has_organization": {"head": "UNHCR Global Trends", "tail": "UNHCR"}}, {"has_timeframe": {"head": "UNHCR Global Trends", "tail": "2015"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "evidence is consistent with studies that used cross-section surveys, panel data and cross-country\n\nSome of the work with panel data has also gone further in an effort to establish a causality link\n\nOswald (2007) use information on lottery winnings in the British Household Panel Survey", "output": {"entities": {"named_data": ["British Household Panel Survey"]}, "relations": []}, "_meta": {"source": "real_world_verified"}}
+{"input": "3 As of December 2023, according to UNHCR, based on governmental sources [Situation Ukraine Refugee Situation (unhcr.org)](https://data.unhcr.org/en/situations/ukraine) 4 According to the active PESEL UKR database.\n5 According to the active PESEL UKR database in October 2023.\n6 Deloitte calculations based on Multi-Sector Needs Assessment Poland 2023 survey data provided by UNHCR.\n\n06", "output": {"entities": {"named_data": ["PESEL UKR database", "Multi-Sector Needs Assessment Poland 2023"], "year": ["2023"], "geography": ["Poland"], "organization": ["UNHCR"]}, "relations": [{"has_timeframe": {"head": "PESEL UKR database", "tail": "2023"}}, {"has_geography": {"head": "PESEL UKR database", "tail": "Poland"}}, {"has_timeframe": {"head": "Multi-Sector Needs Assessment Poland 2023", "tail": "2023"}}, {"has_organization": {"head": "Multi-Sector Needs Assessment Poland 2023", "tail": "UNHCR"}}, {"has_geography": {"head": "Multi-Sector Needs Assessment Poland 2023", "tail": "Poland"}}]}, "_meta": {"source": "real_world_verified"}}
+{"input": "1000 500 0 65+ 55-64 45-54 35-44 25-34 18-24 <18 25% 20% 15% 10% 5% 0% 5% 10% 15% 20% 25%\n\n**Source:** Deloitte own elaboration based on the PESEL database as of October 2023\n\n**Chart 5.** Composition of refugee households in Poland\n\n60% 50% Total entries-exits of the Polish-Ukrainian border Pesel data\n\n**Source:** Deloitte own elaboration based on Polish Border Guard Headquarter and PESEL data.", "output": {"entities": {"named_data": ["PESEL database"], "year": ["2023"], "geography": ["Poland"]}, "relations": [{"has_timeframe": {"head": "PESEL database", "tail": "2023"}}, {"has_geography": {"head": "PESEL database", "tail": "Poland"}}]}, "_meta": {"source": "real_world_verified"}}

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