Datasets:

electricsheepasia
/

asia-ilo-eap-teap-sex-edu-nb-labour-force-by-sex-and-education-thousands

license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
multilinguality: monolingual
size_categories:
  - 10K<n<100K
tags:
  - tabular
  - asia
  - ilostat
  - labour-force
  - ilo
  - labour
  - employment
pretty_name: Labour force by sex and education (thousands) | Asia (ILOSTAT)

Labour force by sex and education (thousands) | Asia (ILOSTAT)

🌏 17,338 observations · 38 Asia countries · 1970–2025 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 17,338 observations of Labour force data across 38 Asia countries, spanning 1970–2025, 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: Labour force

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=EAP_TEAP_SEX_EDU_NB 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

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

Country	Rows	First year	Last year
`IDN`	1,254	1990	2023
`PSE`	1,121	2000	2025
`CYP`	1,066	1999	2024
`IRN`	1,024	2005	2024
`KHM`	1,000	1996	2023
`KOR`	996	2000	2025
`TUR`	840	2000	2024
`MNG`	814	2000	2024
`PAK`	741	2005	2025
`THA`	715	2000	2024
`GEO`	690	2009	2024
`VNM`	669	2010	2024
`ARM`	652	2001	2023
`ISR`	624	2012	2024
`LKA`	614	2010	2024
...	23 more countries

Indicators (sample)

EAP_TEAP_SEX_EDU_NB — Labour force by sex and education (thousands)

Schema

Column	Type	Description	Example
`ref_area`	`string`	ISO 3166-1 alpha-3 country code	`AFG`
`ref_area.label`	`string`	Country name in English	`Afghanistan`
`source`	`string`	ILOSTAT source code (e.g. labour force survey)	`BA:15715`
`source.label`	`string`	Source name in English	`LFS - Labour Force Survey`
`indicator`	`string`	ILOSTAT indicator code	`EAP_TEAP_SEX_EDU_NB`
`indicator.label`	`string`	Indicator name in English	`Labour force by sex and education (th…`
`sex`	`string`	Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female)	`SEX_T`
`sex.label`	`string`	—	`Total`
`classif1`	`string`	First classification variable (age, education, status, etc.)	`EDU_AGGREGATE_TOTAL`
`classif1.label`	`string`	—	`Education (Aggregate levels): Total`
`time`	`int64`	Observation year	`2021`
`obs_value`	`float64`	Observed indicator value (unit varies — see indicator definition)	`8141.885`
`obs_status`	`string`	Observation status flag (e.g. provisional, unreliable)	`B`
`obs_status.label`	`string`	—	`Break in series`
`note_classif`	`string`	—	`C3:2620`
`note_classif.label`	`string`	—	`Nonstandard education level: Includin…`
`note_indicator`	`string`	—	`I11:264`
`note_indicator.label`	`string`	—	`Break in series: Methodology revised`
`note_source`	`string`	—	`R1:3513_S3:8`
`note_source.label`	`string`	—	`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

from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-ilo-eap-teap-sex-edu-nb-labour-force-by-sex-and-education-thousands")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

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

Time-series for a single indicator

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

Pivot to country × year matrix

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

Citation

@misc{asia_ilo_eap_teap_sex_edu_nb_labour_force_by_sex_and_education_thousands_2025,
  title        = {Labour force by sex and education (thousands) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_TEAP_SEX_EDU_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eap-teap-sex-edu-nb-labour-force-by-sex-and-education-thousands}}
}

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 2026-05-25 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_TEAP_SEX_EDU_NB