electricsheepasia/asia-ilo-luu-xlu2-sex-age-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 and age (%) | Asia (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex and age (%) | Asia (ILOSTAT) 🌏 **14,371 observations** · **36 Asia countries** · **1990–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **14,371 observations** of `Other measures of labour underutilization` data across **36 Asia countries**, spanning **1990–2025**, 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_AGE_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_AGE_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 36 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,128 | 1999 | 2024 | | `TUR` | 943 | 2004 | 2024 | | `IRN` | 900 | 2005 | 2024 | | `AZE` | 858 | 2000 | 2022 | | `VNM` | 765 | 2007 | 2024 | | `THA` | 750 | 1991 | 2024 | | `KHM` | 749 | 1996 | 2023 | | `LKA` | 714 | 2009 | 2024 | | `KOR` | 630 | 2012 | 2025 | | `SGP` | 627 | 2009 | 2024 | | `MNG` | 596 | 2007 | 2024 | | `KGZ` | 592 | 2010 | 2023 | | `PAK` | 554 | 2000 | 2025 | | `PSE` | 430 | 2015 | 2025 | | `BRN` | 383 | 2014 | 2024 | | ... | _21 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_AGE_RT` — Combined rate of time-related underemployment and unemployment (LU2) by sex and age (%) ## 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_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+` | | `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 ```python from datasets import load_dataset ds = load_dataset("electricsheepasia/asia-ilo-luu-xlu2-sex-age-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_AGE_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_AGE_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_AGE_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu2_sex_age_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex and age (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_AGE_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu2-sex-age-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_AGE_RT_This dataset contains 14,371 observations of Other measures of labour underutilization data across 36 Asia countries, spanning 1990–2025, covering 1 distinct indicators, specifically the Combined rate of time-related underemployment and unemployment (LU2) by sex and age (%). It is sourced from ILOSTAT, the ILOs central statistics database, and repackaged by Electric Sheep Asia for ML-ready use in tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集以国际劳工组织（ILO）的ILOSTAT中央统计数据库为权威来源，通过ILOSTAT的REST API接口（https://rplumber.ilo.org/data/indicator?id=LUU_XLU2_SEX_AGE_RT）直接抽取原始指标数据，并严格依据亚洲ISO3国家代码进行筛选，从而限定地理范围为亚洲地区。ILOSTAT在数据处理中遵循国际劳工统计学家会议（ICLS）的定义与标准，对源自各国劳动力调查、住户收入调查等原始微观数据进行统一协调与标准化。经筛选后的数据由Electric Sheep Asia重新封装为HuggingFace数据集，保留来源标签以便追溯，最终形成涵盖36个亚洲国家、时间跨度为1990至2025年的结构化表格数据。

特点

该数据集共包含14,371条观测记录，覆盖36个亚洲国家，时间跨度自1990年至2025年，聚焦于与时间相关的就业不足与失业的综合比率（LU2）这一单一指标。数据按性别（总计、男性、女性）进行细分，并嵌入年龄等分类维度，提供ISO国家代码、指标代码、观测值及观测状态标志等字段。部分记录附带来源说明、指标注释和序列断裂提示，增强了数据的可追溯性。整体以表格形式组织，适用于分类、回归及时间序列预测等任务，遵循CC-BY-4.0开放许可。

使用方法

使用者可通过HuggingFace的datasets库以load_dataset()函数直接加载该数据集，并转换为Pandas数据框进行操作。例如，可筛选特定国家（如印度尼西亚）的数据子集，或针对单一指标按时间排序以绘制时间序列趋势图。此外，用户可利用透视表功能将数据重塑为国家与年份的矩阵形式，便于跨国的横向比较或纵向趋势分析。数据集亦支持进一步按性别或年龄维度进行分组研究，为劳动力市场分析、就业政策评估及相关机器学习建模提供便利的数据基础。

背景与挑战

背景概述

劳动力市场统计历来是衡量经济社会健康程度的关键标尺，国际劳工组织（ILO）自成立以来持续推动全球劳动统计的标准化与可比化。在此背景下，ILOSTAT作为ILO的核心统计数据库，汇聚了来自各国劳动力调查、住户收入调查及行政记录的微观数据，并依据国际劳工统计学家会议（ICLS）的定义进行调和，为跨国比较研究提供了权威基础。本数据集由Electric Sheep Asia于2025年从ILOSTAT REST API中提取并重新封装，聚焦亚洲36个国家1990至2025年间的时间相关就业不足与失业综合率（LU2），按性别与年龄组别细分，共计14,371条观测。这一指标超越了传统失业率单一维度，将工时不足纳入劳动未充分利用的测度框架，为审视亚洲地区劳动力市场结构性困境提供了更为全面的量化依据，对区域就业政策制定与SDG体面工作目标的监测具有重要参考价值。

当前挑战

该数据集所回应的核心领域问题在于劳动未充分利用的多维测度与跨国可比性。传统失业率仅捕捉完全无业人口，无法反映非自愿兼职及工时不足群体，LU2指标的构建本身即可视为对既有统计范式局限性的突破。数据整合过程中的挑战则更为具体：ILOSTAT虽以ICLS标准调和各国数据，但来源国的劳动力调查方法、抽样设计及调查周期存在显著异质性，部分国家数据仅覆盖特定年份或特定年龄组，导致面板结构不均衡；观测状态标记如“不可靠”或“系列中断”提示原始数据质量参差；此外，性别与年龄维度的交叉分类在某些国家存在缺失，进一步增加了时间序列建模与跨国比较分析的技术难度。

常用场景

经典使用场景

在劳动经济学与就业统计研究中，该数据集最为经典的运用场景在于构建跨国时间序列面板，以刻画亚洲地区劳动力未充分利用的演化轨迹。研究者可依托1990至2025年间36个亚洲国家的年度观测值，按性别与年龄组别对时间相关就业不足与失业的复合比率（LU2）进行纵向追踪，进而识别不同经济周期与结构性转型阶段中劳动力市场压力的积聚与释放特征。此类面板数据的时序连贯性与国别可比性，使其成为分析亚洲劳动力市场动态的基准性数据资源之一。

解决学术问题

该数据集有效回应了劳动力市场统计中度量口径不一致与跨国比较困难的学术难题。通过ILOSTAT依据国际劳工统计学家会议定义所进行的标准化调和，数据集降低了各国劳动力调查在概念界定与抽样设计上的异质性，使研究者得以在统一框架下考察时间相关就业不足与失业的复合影响。其意义在于突破了传统失业率指标对非自愿兼职与工时不足现象的遮蔽，为测度劳动力真实闲置程度提供了更为全面的量化基准。

衍生相关工作

围绕该数据集及其所属的ILOSTAT劳工未充分利用指标系列，已衍生出若干经典性研究工作。相关文献广泛涉及劳动力市场松紧程度的跨国比较、非自愿兼职与失业的关联机制分析，以及性别与年龄维度下就业不足的结构性差异探讨。这些工作多将LU2与其他劳动力未充分利用指标（如LU1、LU3、LU4）联合使用，构建综合性劳动力市场健康度评估框架，并借助面板计量方法检验宏观经济波动、产业结构变迁与制度安排对就业不足的差异化影响。