electricsheepasia/asia-ilo-eap-teap-sex-edu-mts-nb-labour-force-by-sex-education-and-marital-status-t

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

🌏 120,106 observations · 34 Asia countries · 1970–2025 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 120,106 observations of Labour force data across 34 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_MTS_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

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

Country	Rows	First year	Last year
IDN	8,484	1996	2023
KOR	7,833	2000	2025
IRN	7,802	2005	2024
KHM	6,798	1996	2023
CYP	6,771	1999	2020
THA	6,551	2000	2024
MNG	6,056	2000	2024
VNM	5,853	2010	2024
PAK	5,674	2005	2025
ARM	5,186	2001	2023
ISR	4,887	2012	2024
IND	4,767	1994	2025
LKA	4,555	2010	2024
TUR	3,920	2000	2024
GEO	3,723	2009	2020
...	19 more countries

Indicators (sample)

• EAP_TEAP_SEX_EDU_MTS_NB — Labour force by sex, education and marital status (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_MTS_NB
indicator.label	string	Indicator name in English	Labour force by sex, education and ma…
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
classif2	string	Second classification variable where applicable	MTS_AGGREGATE_TOTAL
classif2.label	string	—	Marital status (Aggregate): 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)	U
obs_status.label	string	—	Unreliable
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-mts-nb-labour-force-by-sex-education-and-marital-status-t")
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_MTS_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EAP_TEAP_SEX_EDU_MTS_NB")

Pivot to country × year matrix

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

Citation

@misc{asia_ilo_eap_teap_sex_edu_mts_nb_labour_force_by_sex_education_and_marital_status_t_2025,
  title        = {Labour force by sex, education and marital status (thousands) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_TEAP_SEX_EDU_MTS_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eap-teap-sex-edu-mts-nb-labour-force-by-sex-education-and-marital-status-t}}
}

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

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