electricsheepasia/asia-ilo-emp-2emp-sex-age-cla-nb-employment-by-sex-age-and-economic-class-ilo-model

Employment by sex, age and economic class -- ILO modelled estimates, Nov. 2025 (thousands) | Asia (ILOSTAT)

🌏 59,124 observations · 33 Asia countries · 1991–2025 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 59,124 observations of Working poverty data across 33 Asia countries, spanning 1991–2025, 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: Working poverty

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=EMP_2EMP_SEX_AGE_CLA_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

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

Country	Rows	First year	Last year
ARM	1,794	1991	2025
AZE	1,794	1991	2025
BGD	1,794	1991	2025
BTN	1,794	1991	2025
CHN	1,794	1991	2025
GEO	1,794	1991	2025
IDN	1,794	1991	2025
IND	1,794	1991	2025
IRN	1,794	1991	2025
IRQ	1,794	1991	2025
JOR	1,794	1991	2025
KAZ	1,794	1991	2025
KGZ	1,794	1991	2025
LAO	1,794	1991	2025
LKA	1,794	1991	2025
...	18 more countries

Indicators (sample)

• EMP_2EMP_SEX_AGE_CLA_NB — Employment by sex, age and economic class -- ILO modelled estimates, Nov. 2025 (thousands)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	ARM
ref_area.label	string	Country name in English	Armenia
source	string	ILOSTAT source code (e.g. labour force survey)	XA:1931
source.label	string	Source name in English	ILO - Modelled Estimates
indicator	string	ILOSTAT indicator code	EMP_2EMP_SEX_AGE_CLA_NB
indicator.label	string	Indicator name in English	Employment by sex, age and economic c…
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	CLA_ECOCLA_TOTAL
classif2.label	string	—	Economic class: Total
time	int64	Observation year	2025
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	1237.611
obs_status	string	Observation status flag (e.g. provisional, unreliable)	A
obs_status.label	string	—	Adjusted

Disaggregation dimensions

The following columns provide disaggregation dimensions:

• sex (3 unique values): SEX_T, SEX_M, SEX_F

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-emp-2emp-sex-age-cla-nb-employment-by-sex-age-and-economic-class-ilo-model")
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"] == "EMP_2EMP_SEX_AGE_CLA_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EMP_2EMP_SEX_AGE_CLA_NB")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "EMP_2EMP_SEX_AGE_CLA_NB"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{asia_ilo_emp_2emp_sex_age_cla_nb_employment_by_sex_age_and_economic_class_ilo_model_2025,
  title        = {Employment by sex, age and economic class -- ILO modelled estimates, Nov. 2025 (thousands) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_2EMP_SEX_AGE_CLA_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-2emp-sex-age-cla-nb-employment-by-sex-age-and-economic-class-ilo-model}}
}

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 2026-05-28 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_2EMP_SEX_AGE_CLA_NB