Datasets:
license: cc-by-4.0
language:
- en
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
multilinguality: monolingual
size_categories:
- 10K<n<100K
tags:
- tabular
- asia
- ilostat
- other-measures-of-labour-underutilization
- ilo
- labour
- employment
pretty_name: >-
Combined rate of time-related underemployment and unemployment (LU2) by sex,
education and | Asia (ILOSTAT)
Combined rate of time-related underemployment and unemployment (LU2) by sex, education and | Asia (ILOSTAT)
🌏 10,926 observations · 29 Asia countries · 1996–2025 · Repackaged by Electric Sheep Asia
TL;DR
This dataset contains 10,926 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_EDU_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 |
|---|---|---|---|
IRN |
1,028 | 2005 | 2024 |
TUR |
975 | 2004 | 2024 |
CYP |
921 | 1999 | 2020 |
VNM |
756 | 2010 | 2024 |
THA |
736 | 2010 | 2024 |
KHM |
647 | 1996 | 2023 |
KOR |
630 | 2012 | 2025 |
LKA |
630 | 2010 | 2024 |
PAK |
582 | 2006 | 2025 |
MNG |
533 | 2013 | 2024 |
PSE |
456 | 2015 | 2025 |
BRN |
400 | 2014 | 2024 |
IDN |
360 | 2016 | 2023 |
JOR |
360 | 2017 | 2024 |
BGD |
259 | 2013 | 2024 |
| ... | 14 more countries |
Indicators (sample)
LUU_XLU2_SEX_EDU_MTS_RT— Combined rate of time-related underemployment and unemployment (LU2) by sex, education 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_EDU_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.) | EDU_AGGREGATE_TOTAL |
classif1.label |
string |
— | Education (Aggregate levels): Total |
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 |
— | C3:2620 |
note_classif.label |
string |
— | Nonstandard education level: Includin… |
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-edu-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_EDU_MTS_RT"]
.sort_values("time"))
sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_EDU_MTS_RT")
Pivot to country × year matrix
matrix = (df[df["indicator"] == "LUU_XLU2_SEX_EDU_MTS_RT"]
.pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())
Citation
@misc{asia_ilo_luu_xlu2_sex_edu_mts_rt_combined_rate_of_time_related_underemployment_and_2025,
title = {Combined rate of time-related underemployment and unemployment (LU2) by sex, education and | Asia (ILOSTAT)},
author = {International Labour Organization (ILO)},
year = {2025},
url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_EDU_MTS_RT},
publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu2-sex-edu-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
Provenance: ingested 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_EDU_MTS_RT