electricsheepasia/asia-ilo-une-tune-sex-eco-nb-unemployment-of-previously-employed-persons-by-sex

Unemployment of previously employed persons by sex and former economic activity (thousands | Asia (ILOSTAT)

🌏 28,087 observations · 30 Asia countries · 1970–2025 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 28,087 observations of Unemployment data across 30 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: Unemployment

Methodology

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

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

Country	Rows	First year	Last year
CYP	2,905	1974	2024
TUR	2,638	1988	2023
KOR	2,088	1971	2010
ISR	2,053	1970	2015
IRN	1,992	2005	2024
PSE	1,974	2000	2025
SGP	1,900	1978	2015
MNG	1,589	1998	2024
KGZ	1,537	2002	2023
PAK	1,295	1985	2025
THA	1,222	2010	2024
JPN	902	2002	2015
VNM	889	2009	2018
GEO	734	2017	2024
PHL	700	2017	2023
...	15 more countries

Indicators (sample)

• UNE_TUNE_SEX_ECO_NB — Unemployment of previously employed persons by sex and former economic activity (thousands)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	ARE
ref_area.label	string	Country name in English	United Arab Emirates
source	string	ILOSTAT source code (e.g. labour force survey)	BA:716
source.label	string	Source name in English	LFS - Labour Force Survey
indicator	string	ILOSTAT indicator code	UNE_TUNE_SEX_ECO_NB
indicator.label	string	Indicator name in English	Unemployment of previously employed p…
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.)	ECO_SECTOR_TOTAL
classif1.label	string	—	Economic activity (Broad sector): Total
time	int64	Observation year	2019
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	49.166
obs_status	string	Observation status flag (e.g. provisional, unreliable)	U
obs_status.label	string	—	Unreliable
note_classif	string	—	C5:1023
note_classif.label	string	—	Nonstandard economic activity: Includ…
note_indicator	string	—	I11:264
note_indicator.label	string	—	Break in series: Methodology revised
note_source	string	—	R1:3513
note_source.label	string	—	Repository: ILO-STATISTICS - Micro da…

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-une-tune-sex-eco-nb-unemployment-of-previously-employed-persons-by-sex")
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"] == "UNE_TUNE_SEX_ECO_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_ECO_NB")

Pivot to country × year matrix

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

Citation

@misc{asia_ilo_une_tune_sex_eco_nb_unemployment_of_previously_employed_persons_by_sex_2025,
  title        = {Unemployment of previously employed persons by sex and former economic activity (thousands | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_ECO_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-une-tune-sex-eco-nb-unemployment-of-previously-employed-persons-by-sex}}
}

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 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_ECO_NB