electricsheepasia/asia-ilo-luu-xlu4-sex-age-rt-composite-rate-of-labour-underutilization-lu4-by-s

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Composite rate of labour underutilization (LU4) by sex and age (%) | Asia (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex and age (%) | Asia (ILOSTAT) 🌏 **9,463 observations** · **34 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **9,463 observations** of `Other measures of labour underutilization` data across **34 Asia countries**, spanning **1999–2025**, 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_XLU4_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_XLU4_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 34 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,150 | 1999 | 2024 | | `TUR` | 976 | 2004 | 2024 | | `VNM` | 765 | 2007 | 2024 | | `LKA` | 630 | 2010 | 2024 | | `THA` | 585 | 2010 | 2024 | | `KGZ` | 571 | 2011 | 2023 | | `PSE` | 432 | 2015 | 2025 | | `BRN` | 403 | 2014 | 2024 | | `JOR` | 348 | 2017 | 2024 | | `ARM` | 327 | 2008 | 2017 | | `PHL` | 315 | 2017 | 2023 | | `IDN` | 315 | 2016 | 2023 | | `GEO` | 270 | 2019 | 2024 | | `MNG` | 264 | 2019 | 2024 | | `BGD` | 225 | 2013 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_AGE_RT` — Composite rate of labour underutilization (LU4) 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_XLU4_SEX_AGE_RT` | | `indicator.label` | `string` | Indicator name in English | `Composite rate of labour underutiliza…` | | `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) | `19.229` | | `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-xlu4-sex-age-rt-composite-rate-of-labour-underutilization-lu4-by-s") 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_XLU4_SEX_AGE_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_AGE_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_AGE_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_luu_xlu4_sex_age_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex and age (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_AGE_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-luu-xlu4-sex-age-rt-composite-rate-of-labour-underutilization-lu4-by-s}} } ``` ## 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 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_AGE_RT_This dataset contains data on Other measures of labour underutilization, specifically focusing on the Composite rate of labour underutilization (LU4) by sex and age (%), covering 34 Asia countries from 1999 to 2025. It includes 9,463 observations and 1 distinct indicator (LUU_XLU4_SEX_AGE_RT). The data is sourced from ILOSTAT, the International Labour Organizations (ILO) central statistics database, which is a leading global source for labour statistics, compiling indicators across employment, unemployment, wages, working time, and more. The data is pulled directly from the ILOSTAT REST API and filtered to Asia ISO3 country codes, with harmonization based on International Conference of Labour Statisticians (ICLS) definitions by the ILOs Department of Statistics. The dataset features columns such as country code, country name, source, indicator code, sex disaggregation, age classification, observation year, observed value, observation status, and other metadata. Data quality notes include annual frequency, use of the ILO-selected best source for multiple sources, and non-null disaggregation columns only when breakdowns are published. It is suitable for tabular classification, tabular regression, and time-series forecasting tasks, designed to provide ML-ready data for labour market research in Asia.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织（ILO）的ILOSTAT核心统计数据库，经由Electric Sheep Asia重新封装，聚焦于亚洲地区劳动力利用不足的综合率（LU4）。构建过程严格遵循ILOSTAT REST API的原始数据提取，依据国际劳工统计学家会议（ICLS）的统一定义，对各国劳动力调查的微观数据进行协调与标准化。数据筛选限定于34个亚洲国家的ISO 3166-1 alpha-3代码，时间跨度覆盖1999至2025年，共计9463条观测记录。每条观测均包含国家、来源、指标、性别及年龄等分类维度，并附有ILO官方指定的最佳来源标识与标志性注释，确保了数据的可追溯性与可比性。

特点

该数据集的核心特征在于其高度的结构化与多维分解能力。它提供了按性别（总、男、女）及年龄类别（如青年与成人15岁以上）细分的复合劳动力利用不足率，使研究者能够洞察劳动力市场的结构性差异。数据以年度频率呈现，且针对每个国家-年份组合优先采用ILO甄选的权威数据源，剔除了重复与低质记录。此外，数据集中内置了来源标签、观测状态标志（如临时或不可靠）以及方法论修订的注释，这种丰富的元数据增强了数据的可信度，为跨国时间序列分析及劳动力市场监测提供了坚实的数据基础。

使用方法

数据集的使用简便高效，支持通过HuggingFace的datasets库直接加载，可无缝转换为Pandas DataFrame以进行后续分析。用户可按国家代码（如'IDN'）过滤子集，亦或针对特定指标（如'LUU_XLU4_SEX_AGE_RT'）进行时间序列排序与可视化。数据集还支持透视表操作，便于构建国家×年份的宽表矩阵，适合面板数据回归或趋势分析。所有观测均附带详细的分类变量与注释字段，研究者可结合自身需求灵活选取维度，进行深度的劳动力市场实证研究，同时需遵循CC-BY-4.0许可协议，并正确引用原始数据来源及打包方Electric Sheep Asia。

背景与挑战

背景概述

该数据集由Electric Sheep Asia于2025年重新整理发布，源自国际劳工组织（ILO）的ILOSTAT数据库，聚焦亚洲地区劳动力利用不足的综合率（LU4）指标，按性别和年龄细分，涵盖1999至2025年间34个亚洲国家的9,463条观测记录。ILOSTAT作为全球劳动统计的权威来源，其数据源于各国劳动力调查等，经ILO统计部门按国际劳工统计学家会议（ICLS）标准进行统一协调。该数据集的创建旨在为研究亚洲劳动力市场动态、性别差异及年龄结构对劳动力利用不足的影响提供标准化、机器可读的时序数据，对劳动经济学、发展经济学及区域政策制定具有重要参考价值，尤其适用于跨国比较和面板数据分析。

当前挑战

该数据集面临的挑战包括：其一，劳动力利用不足概念本身具有多维性，LU4综合率涵盖时间相关就业不足、失业及潜在劳动力，其测度依赖于复杂的调查定义和统计口径，整合多源数据时需确保一致性和可比性；其二，数据覆盖存在不平衡，如塞浦路斯、土耳其等国的观测年份较长，而阿富汗等国数据稀疏，可能导致跨国分析的偏差和估计的不稳定性；其三，ILOSTAT数据通常包含不同的观测状态标记（如不可靠值）和方法修订导致的时间序列断点，需要用户进行严格的质量筛选和预处理；其四，数据以年度频率汇总，丢失了月度或季度内变化的信息，限制了高频动态分析；最后，数据按ILO选择的'最佳来源'处理同一国家年份的多源数据，可能引入源间差异，影响数据的一致性和可比性。

常用场景

经典使用场景

该数据集是国际劳工组织（ILO）ILOSTAT数据库中有关亚洲地区劳动利用不足综合率（LU4）的权威时间序列数据，覆盖了34个亚洲国家、跨越1999年至2025年。其经典使用场景聚焦于劳动力市场结构分析，研究者常借助该数据开展跨国比较研究，追踪不同性别和年龄群体在劳动利用不足方面的动态变化，进而评估区域经济发展阶段与劳动力吸纳能力的内在关联。数据以年度频率呈现，便于构建面板数据进行纵向趋势挖掘，并为劳动经济学中的失业度量拓展研究提供关键的经验证据，尤其适用于探索隐性失业、就业不足等复杂劳动市场现象的时空演化规律。

实际应用

在实际应用中，该数据集是国际组织、国家劳工部门、研究智库及社会政策制定者进行劳动力市场监测与政策设计的重要资源。通过追踪LU4率的年度变化，政府机构能够及时发现劳动力市场中的结构性失衡，评估现有就业政策的实际效果，并针对特定性别或年龄群体制定精准的干预策略。在区域经济合作与发展规划中，该数据能辅助识别劳动力资源错配与潜能释放的优先领域，为外商投资选址、人力资源开发及社会安全保障制度的完善提供数据驱动的决策依据。此外，数据集中详尽的数据来源标注与质量标识，也使其成为数据新闻、劳动统计报告以及国际可持续发展目标（SDG）进展评估中可靠的引用来源。

衍生相关工作

该数据集因其规范的架构与权威的来源，衍生了一系列具有影响力的学术研究与实践应用工作。基于其时间序列属性，研究者构建了多种亚洲劳动力市场状态预测模型，并与经济周期指标相结合，探索劳动利用不足率在经济波动中的敏感性。围绕数据集的性别与年龄细分维度，衍生出多篇聚焦女性劳动参与障碍、青年就业困境比较的专题研究，丰富了性别经济学与青年就业领域的理论成果。在开放数据生态中，该数据集也常被集成至更广泛的劳动统计知识图谱或综合数据平台中，服务于自动化的跨国数据比对和可持续报告生成，其数据治理与平铺设计也常被视为区域劳动数据整合的示范样本。