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

--- 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](https://huggingface.co/electricsheepasia)*      ## 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](https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_EDU_MTS_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/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 ```python 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 ```python indonesia = df[df["ref_area"] == "IDN"] ``` ### Time-series for a single indicator ```python 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 ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_EDU_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @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](https://creativecommons.org/licenses/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](https://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_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Combined rate of time-related underemployment and unemployment (LU2) by sex, education and marital status. It covers 29 Asia countries from 1996 to 2025, with 10,926 observations. The data is structured in tabular format, including columns such as country code, indicator, sex, education level, marital status, year, observed value, etc., suitable for tabular classification, regression, and time-series forecasting tasks. The dataset is repackaged by Electric Sheep Asia and released under the CC-BY-4.0 license.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILO）的ILOSTAT中央统计数据库，以劳动力市场统计标准化框架为背景，通过ILOSTAT提供的REST API直接提取指标代码为LUU_XLU2_SEX_EDU_MTS_RT的原始数据，并依据ISO 3166-1 alpha-3国家代码筛选出29个亚洲经济体，最终由Electric Sheep Asia完成规范化重打包。原始数据源自各国劳动力调查、家庭收入调查、机构调查及行政记录，ILO统计部门依照国际劳工统计学家会议（ICLS）定义对微观数据进行协调，并在源标签列中标注来源以保障可追溯性。

特点

数据集聚焦于亚洲地区与时间相关的就业不足和失业综合比率（LU2），涵盖1996年至2025年间的10,926条观测记录，涉及29个亚洲国家。数据以表格形式组织，包含国家代码、来源信息、指标代码、性别、教育程度、婚姻状况、年份、观测值及各类状态和注释标签，支持按性别（总计、男性、女性）进行分解。时间序列为年度频率，当同一国家与年份存在多源数据时，采用ILO选定的最佳来源。所有数据以CC-BY-4.0许可发布，兼具长时段覆盖与多维分类特征，适合劳动经济学与计量分析。

使用方法

使用者可通过HuggingFace的datasets库以load_dataset函数直接加载数据集，并转换为Pandas数据框进行后续分析。典型操作包括筛选特定国家（如df[df['ref_area'] == 'IDN']）、提取单一指标的时间序列并排序绘图、以及利用透视表将数据重塑为国家与年份的矩阵形式。此外，可结合指标代码、性别和教育分类等字段进行分组统计或回归建模，以探究亚洲劳动力市场就业不足与失业的结构性特征。数据加载与处理流程简洁，便于研究人员快速复现和扩展分析。

背景与挑战

背景概述

国际劳工组织长期致力于全球劳动力市场统计监测，其ILOSTAT数据库乃劳动统计领域之权威基准。该数据集由Electric Sheep Asia于2025年重新封装发布，源自ILO统计部门经ILO REST API提取并筛选亚洲ISO3国家之原始数据，涵盖1996至2025年间29个亚洲国家逾万条观测记录，聚焦时间相关就业不足与失业的复合比率（LU2）。核心研究问题在于揭示亚洲地区劳动力未充分利用之结构性特征，按性别、教育程度与婚姻状况进行多维分解，为追踪可持续发展目标中体面劳动进展提供关键量化依据，对区域劳动政策制定与比较研究具有显著影响力。

当前挑战

该数据集所应对的领域问题在于精准刻画亚洲劳动力市场未充分利用之复杂图景，其挑战体现于多重维度：其一，劳动统计指标须统合时间相关就业不足与失业两种异质状态，概念界定与测量口径须符合国际劳工统计学家会议之严格标准，方保跨国可比性；其二，亚洲各国劳动力调查体系成熟度参差，数据来源覆盖年限参差不齐，部分国家仅有个别年份观测，时序连续性面临考验；其三，构建过程中须整合异源调查微数据与行政记录，处理方法学修订所引致之序列断裂，并对标记为不可靠或临时性之观测进行追溯与质量评估，以维护统计权威性。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉领域，针对亚洲地区劳动力市场结构性失衡的量化分析长期依赖国际可比的高质量数据。该数据集以国际劳工组织（ILO）第19届国际劳工统计学家会议（ICLS）决议为方法论框架，整合了29个亚洲国家1996至2025年间按性别、教育程度与婚姻状况分层的LU2综合不充分就业率，成为刻画亚洲劳动力未充分利用状态的经典面板数据来源。研究者通常将其用于构建跨国时间序列模型，以识别不同教育层级与性别群体在劳动力市场波动中的差异化暴露程度，进而揭示亚洲经济体在产业结构转型过程中劳动力供需错配的时空演化规律。

实际应用

在政策实践层面，该数据集为亚洲各国劳动行政部门及国际发展机构提供了劳动力市场监测与预警的量化依据。通过追踪不同教育与性别群体LU2比率的时序波动，政策制定者可识别经济下行周期中脆弱就业群体的规模扩张趋势，从而优化失业保险覆盖范围与积极劳动力市场政策的靶向精度。国际组织亦将其纳入可持续发展目标（SDG）中体面工作指标的国别进展评估体系，用以衡量亚洲区域在减少就业不足与提升就业质量方面的阶段性成效，并为跨国劳务合作与技能开发援助的优先序设定提供数据支撑。

衍生相关工作

围绕该数据集及其同源ILOSTAT系列，学术界衍生出若干经典研究脉络。其一为跨国面板计量分析，利用LU2指标检验奥肯定律在亚洲新兴经济体的适用性及其非线性特征；其二为分解研究，将LU2变动拆解为产业结构效应与人口结构效应；其三为机器学习预测建模，基于该数据训练时间序列预测模型以评估未来劳动力市场风险。此外，Electric Sheep Asia的标准化重打包工作促进了该数据集与亚洲其他社会经济数据层的融合，催生了多源异构数据驱动的劳动力脆弱性综合指数构建等前沿探索。