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electricsheepasia/asia-ilo-eip-dwap-sex-edu-dsb-rt-inactivity-rate-by-sex-education-and-disability-st

Inactivity rate by sex, education and disability status (%) | Asia (ILOSTAT) 🌏 6,299 observations · 20 Asia countries · 1996–2024 · Repackaged by Electric Sheep Asia TL;DR This dataset contains 6,299 observations of Other measures of labour underutilization data across 20 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-edu-dsb-rt-inactivity-rate-by-sex-education-and-disability-st.

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Dataset Card

Inactivity rate by sex, education and disability status (%) | Asia (ILOSTAT)

🌏 6,299 observations · 20 Asia countries · 1996–2024 · Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

rows countries years indicators license

TL;DR

This dataset contains 6,299 observations of Other measures of labour underutilization data across 20 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 — 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.

  • —Source: ILOSTAT
  • —Publisher: International Labour Organization (ILO)
  • —License: cc-by-4.0
  • —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_EDU_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

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

CountryRowsFirst yearLast year
CYP97820052024
MNG83720062024
ARM74420072023
KHM60019962023
ISR43220162023
IDN41120102023
LKA37520182024
THA27020072019
BGD23220112024
PSE22520182022
TLS19420152022
IRQ15620072021
AFG15320172021
LAO15320152022
TJK13520032016
...5 more countries

Indicators (sample)

  • —EIP_DWAP_SEX_EDU_DSB_RT — Inactivity rate by sex, education 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_EDU_DSB_RT
indicator.labelstringIndicator name in EnglishInactivity rate by sex, education and…
sexstringDisaggregation by sex (SEXT = total, SEXM = male, SEX_F = female)SEX_T
sex.labelstring—Total
classif1stringFirst classification variable (age, education, status, etc.)EDU_AGGREGATE_TOTAL
classif1.labelstring—Education (Aggregate levels): Total
classif2stringSecond classification variable where applicableDSB_STATUS_TOTAL
classif2.labelstring—Disability status: Total
timeint64Observation year2021
obs_valuefloat64Observed indicator value (unit varies — see indicator definition)50.27
obs_statusstringObservation status flag (e.g. provisional, unreliable)U
obs_status.labelstring—Unreliable
note_classifstring—C3:2620
note_classif.labelstring—Nonstandard education level: Includin…
note_indicatorstring—I11:264
note_indicator.labelstring—Break in series: Methodology revised
note_sourcestring—R1:3513_S3:8
note_source.labelstring—Repository: ILO-STATISTICS - Micro da…

Disaggregation dimensions

The following columns provide disaggregation dimensions:

  • —`sex` (4 unique values): SEX_T, SEX_M, SEX_F, SEX_O

Data quality & caveats

  • —Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here.
  • —When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used.
  • —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-edu-dsb-rt-inactivity-rate-by-sex-education-and-disability-st")
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_EDU_DSB_RT"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_EDU_DSB_RT")

Pivot to country × year matrix

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

Citation

bibtex
@misc{asia_ilo_eip_dwap_sex_edu_dsb_rt_inactivity_rate_by_sex_education_and_disability_st_2024,
  title        = {Inactivity rate by sex, education and disability status (%) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2024},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_EDU_DSB_RT},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-edu-dsb-rt-inactivity-rate-by-sex-education-and-disability-st}}
}

License

Released under cc-by-4.0.

Original data © 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=EIPDWAPSEXEDUDSBRT_