electricsheepasia/asia-ilo-ear-emtm-sex-nb-median-monthly-earnings-of-employees-by-sex-local

Median monthly earnings of employees by sex (local currency) | Asia (ILOSTAT)

🌏 831 observations · 27 Asia countries · 1996–2025 · Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

TL;DR

This dataset contains 831 observations of Earnings data across 27 Asia countries, spanning 1996–2025, 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: Earnings

Methodology

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

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

Indicators (sample)

• —EAR_EMTM_SEX_NB — Median monthly earnings of employees by sex (local currency)

Schema

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-ear-emtm-sex-nb-median-monthly-earnings-of-employees-by-sex-local")
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"] == "EAR_EMTM_SEX_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EAR_EMTM_SEX_NB")

Pivot to country × year matrix

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

Citation

@misc{asia_ilo_ear_emtm_sex_nb_median_monthly_earnings_of_employees_by_sex_local_2025,
  title        = {Median monthly earnings of employees by sex (local currency) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAR_EMTM_SEX_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ear-emtm-sex-nb-median-monthly-earnings-of-employees-by-sex-local}}
}

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