electricsheepasia/asia-ilo-luu-xlu2-sex-age-mts-rt-combined-rate-of-time-related-underemployment-and

Combined rate of time-related underemployment and unemployment (LU2) by sex, age and marit | Asia (ILOSTAT)

🌏 16,552 observations · 29 Asia countries · 1996–2025 · Repackaged by Electric Sheep Asia

TL;DR

This dataset contains 16,552 observations of Other measures of labour underutilization data across 29 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: Other measures of labour underutilization

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=LUU_XLU2_SEX_AGE_MTS_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

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

Country	Rows	First year	Last year
TUR	1,533	2004	2024
CYP	1,456	1999	2020
IRN	1,371	2005	2024
VNM	1,146	2010	2024
THA	1,037	2010	2024
KHM	1,008	1996	2023
KOR	998	2012	2025
LKA	998	2010	2024
PAK	845	2006	2025
MNG	748	2013	2024
PSE	677	2015	2025
PHL	588	2017	2023
IDN	576	2016	2023
BRN	573	2014	2024
JOR	499	2017	2024
...	14 more countries

Indicators (sample)

• LUU_XLU2_SEX_AGE_MTS_RT — Combined rate of time-related underemployment and unemployment (LU2) by sex, age and marital status (%)

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	LUU_XLU2_SEX_AGE_MTS_RT
indicator.label	string	Indicator name in English	Combined rate of time-related underem…
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	MTS_AGGREGATE_TOTAL
classif2.label	string	—	Marital status (Aggregate): Total
time	int64	Observation year	2021
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	12.91
obs_status	string	Observation status flag (e.g. provisional, unreliable)	U
obs_status.label	string	—	Unreliable
note_classif	string	—	—
note_classif.label	string	—	—
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 (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-luu-xlu2-sex-age-mts-rt-combined-rate-of-time-related-underemployment-and")
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"] == "LUU_XLU2_SEX_AGE_MTS_RT"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_AGE_MTS_RT")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "LUU_XLU2_SEX_AGE_MTS_RT"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{asia_ilo_luu_xlu2_sex_age_mts_rt_combined_rate_of_time_related_underemployment_and_2025,
  title        = {Combined rate of time-related underemployment and unemployment (LU2) by sex, age and marit | Asia (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_AGE_MTS_RT},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu2-sex-age-mts-rt-combined-rate-of-time-related-underemployment-and}}
}

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=LUU_XLU2_SEX_AGE_MTS_RT