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electricsheepasia/asia-ilo-eip-dwap-sex-geo-dsb-rt-inactivity-rate-by-sex-rural-urban-areas-and-disab

Inactivity rate by sex, rural / urban areas and disability status (%) | Asia (ILOSTAT) 🌏 3,170 observations Β· 17 Asia countries Β· 1996–2024 Β· Repackaged by Electric Sheep Asia TL;DR This dataset contains 3,170 observations of Other measures of labour underutilization data across 17 Asia countries, spanning 1996–2024, covering 1 distinct indicators. About the source ILOSTAT is the ILO's central statistics database, the leading global source for… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-geo-dsb-rt-inactivity-rate-by-sex-rural-urban-areas-and-disab.

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Inactivity rate by sex, rural / urban areas and disability status (%) | Asia (ILOSTAT)

🌏 3,170 observations Β· 17 Asia countries Β· 1996–2024 Β· Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

rows countries years indicators license

TL;DR

This dataset contains 3,170 observations of Other measures of labour underutilization data across 17 Asia countries, spanning 1996–2024, covering 1 distinct indicators.

About the source

ILOSTAT is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets β€” drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation.

  • β€”Source: ILOSTAT
  • β€”Publisher: International Labour Organization (ILO)
  • β€”License: cc-by-4.0
  • β€”Topic: Other measures of labour underutilization

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=EIP_DWAP_SEX_GEO_DSB_RT and filtered to Asia ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the source.label column for traceability.

Geographic coverage

17 Asia countries Β· top rows shown below, sorted by row count:

CountryRowsFirst yearLast year
CYP54020052024
MNG48620062024
ARM45920072023
KHM35119962023
IDN24320102023
LKA18920182024
PSE18020182022
THA13520072019
TLS10720152022
BGD9320112024
AFG9020172021
LAO8120152022
IRQ5420122021
TJK5420032007
PAK5420202021
...2 more countries

Indicators (sample)

  • β€”EIP_DWAP_SEX_GEO_DSB_RT β€” Inactivity rate by sex, rural / urban areas and disability status (%)

Schema

ColumnTypeDescriptionExample
ref_areastringISO 3166-1 alpha-3 country codeAFG
ref_area.labelstringCountry name in EnglishAfghanistan
sourcestringILOSTAT source code (e.g. labour force survey)BA:15715
source.labelstringSource name in EnglishLFS - Labour Force Survey
indicatorstringILOSTAT indicator codeEIP_DWAP_SEX_GEO_DSB_RT
indicator.labelstringIndicator name in EnglishInactivity rate by sex, rural / urban…
sexstringDisaggregation by sex (SEXT = total, SEXM = male, SEX_F = female)SEX_T
sex.labelstringβ€”Total
classif1stringFirst classification variable (age, education, status, etc.)GEO_COV_NAT
classif1.labelstringβ€”Area type: National
classif2stringSecond classification variable where applicableDSB_STATUS_TOTAL
classif2.labelstringβ€”Disability status: Total
timeint64Observation year2021
obs_valuefloat64Observed indicator value (unit varies β€” see indicator definition)50.27
obs_statusstringObservation status flag (e.g. provisional, unreliable)B
obs_status.labelstringβ€”Break in series
note_classifstringβ€”C14:6260
note_classif.labelstringβ€”Nonstandard definition of disability:…
note_indicatorstringβ€”I11:264
note_indicator.labelstringβ€”Break in series: Methodology revised
note_sourcestringβ€”R1:3513_S3:8
note_source.labelstringβ€”Repository: ILO-STATISTICS - Micro da…

Disaggregation dimensions

The following columns provide disaggregation dimensions:

  • β€”`sex` (4 unique values): SEX_T, SEX_M, SEX_F, SEX_O

Data quality & caveats

  • β€”Data is annual frequency. Some indicators also publish monthly or quarterly series β€” those are not included here.
  • β€”When an indicator has multiple sources for the same countryΓ—year, the ILO-selected 'best source' is used.
  • β€”Disaggregation columns (sex, classif1, classif2) are non-null only when the indicator publishes that breakdown.

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-ilo-eip-dwap-sex-geo-dsb-rt-inactivity-rate-by-sex-rural-urban-areas-and-disab")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python
indonesia = df[df["ref_area"] == "IDN"]

Time-series for a single indicator

python
sample = (df[df["indicator"] == "EIP_DWAP_SEX_GEO_DSB_RT"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_GEO_DSB_RT")

Pivot to country Γ— year matrix

python
matrix = (df[df["indicator"] == "EIP_DWAP_SEX_GEO_DSB_RT"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

bibtex
@misc{asia_ilo_eip_dwap_sex_geo_dsb_rt_inactivity_rate_by_sex_rural_urban_areas_and_disab_2024,
  title        = {Inactivity rate by sex, rural / urban areas and disability status (%) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2024},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_GEO_DSB_RT},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-geo-dsb-rt-inactivity-rate-by-sex-rural-urban-areas-and-disab}}
}

License

Released under cc-by-4.0.

Original data Β© International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Asia repackaging.

About Electric Sheep

Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepasia


Provenance: ingested 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EIPDWAPSEXGEODSBRT_