electricsheepasia/asia-ilo-ilr-tumt-noc-rt-trade-union-density-rate

Trade union density rate (%) | Asia (ILOSTAT)

🌏 257 observations · 28 Asia countries · 2000–2020 · Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

TL;DR

This dataset contains 257 observations of Industrial relations data across 28 Asia countries, spanning 2000–2020, 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: Industrial relations

Methodology

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

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

Indicators (sample)

• —ILR_TUMT_NOC_RT — Trade union density rate (%)

Schema

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-ilr-tumt-noc-rt-trade-union-density-rate")
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"] == "ILR_TUMT_NOC_RT"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="ILR_TUMT_NOC_RT")

Pivot to country × year matrix

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

Citation

@misc{asia_ilo_ilr_tumt_noc_rt_trade_union_density_rate_2020,
  title        = {Trade union density rate (%) | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2020},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=ILR_TUMT_NOC_RT},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ilr-tumt-noc-rt-trade-union-density-rate}}
}

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=ILRTUMTNOCRT_