README.md

---
license: cc-by-4.0
language:
- en
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
multilinguality: monolingual
size_categories:
- 100K<n<1M
tags:
- tabular
- asia
- ilostat
- labour-force
- ilo
- labour
- employment
pretty_name: "Labour force by sex, age and education (thousands) | Asia (ILOSTAT)"
---

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

🌏 **327,502 observations** · **45 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*

![rows](https://img.shields.io/badge/rows-327,502-blue) ![countries](https://img.shields.io/badge/countries-45-green) ![years](https://img.shields.io/badge/years-1970–2025-orange) ![indicators](https://img.shields.io/badge/indicators-1-purple) ![license](https://img.shields.io/badge/license-cc-by-4.0-lightgrey)

## TL;DR

This dataset contains **327,502 observations** of `Labour force` data across **45 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](https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_TEAP_SEX_AGE_EDU_NB)
- **Publisher:** International Labour Organization (ILO)
- **License:** [cc-by-4.0](https://creativecommons.org/licenses/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_AGE_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

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

| Country | Rows | First year | Last year |
|---------|-----:|-----------:|----------:|
| `IDN` | 19,847 | 1990 | 2023 |
| `KOR` | 17,428 | 1991 | 2025 |
| `PSE` | 16,786 | 2000 | 2025 |
| `ISR` | 16,653 | 1996 | 2024 |
| `CYP` | 16,383 | 1992 | 2025 |
| `IRN` | 15,385 | 2005 | 2024 |
| `KHM` | 14,100 | 1996 | 2023 |
| `TUR` | 13,461 | 2000 | 2025 |
| `THA` | 12,736 | 2000 | 2024 |
| `MNG` | 12,448 | 2000 | 2024 |
| `PAK` | 11,679 | 2002 | 2025 |
| `GEO` | 10,781 | 1999 | 2024 |
| `LKA` | 10,650 | 2000 | 2024 |
| `VNM` | 10,582 | 2010 | 2024 |
| `ARM` | 9,350 | 2001 | 2023 |
| ... | _30 more countries_ | | |

## Indicators (sample)

- `EAP_TEAP_SEX_AGE_EDU_NB` — Labour force by sex, age 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_AGE_EDU_NB` |
| `indicator.label` | `string` | Indicator name in English | `Labour force by sex, age and educatio…` |
| `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.) | `AGE_YTHADULT_YGE15` |
| `classif1.label` | `string` | — | `Age (Youth, adults): 15+` |
| `classif2` | `string` | Second classification variable where applicable | `EDU_AGGREGATE_TOTAL` |
| `classif2.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) | `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

```python
from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-ilo-eap-teap-sex-age-edu-nb-labour-force-by-sex-age-and-education-thousands")
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"] == "EAP_TEAP_SEX_AGE_EDU_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EAP_TEAP_SEX_AGE_EDU_NB")
```

### Pivot to country × year matrix

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

## Citation

```bibtex
@misc{asia_ilo_eap_teap_sex_age_edu_nb_labour_force_by_sex_age_and_education_thousands_2025,
  title        = {Labour force by sex, age and education (thousands) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_TEAP_SEX_AGE_EDU_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eap-teap-sex-age-edu-nb-labour-force-by-sex-age-and-education-thousands}}
}
```

## License

Released under [cc-by-4.0](https://creativecommons.org/licenses/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](https://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_AGE_EDU_NB_