electricsheepasia/asia-ilo-ged-xlu4-sex-hht-geo-rt-prime-age-composite-rate-of-labour-underutilizatio

--- 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: "Prime-age composite rate of labour underutilization (LU4) by sex, household type and rural | Asia (ILOSTAT)" --- # Prime-age composite rate of labour underutilization (LU4) by sex, household type and rural | Asia (ILOSTAT) 🌏 **6,056 observations** · **19 Asia countries** · **2000–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **6,056 observations** of `Other measures of labour underutilization` data across **19 Asia countries**, spanning **2000–2025**, 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=GED_XLU4_SEX_HHT_GEO_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=GED_XLU4_SEX_HHT_GEO_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 19 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,052 | 2000 | 2020 | | `PAK` | 632 | 2006 | 2025 | | `LKA` | 623 | 2010 | 2022 | | `KHM` | 574 | 2007 | 2023 | | `TUR` | 540 | 2004 | 2013 | | `VNM` | 486 | 2007 | 2024 | | `PHL` | 378 | 2017 | 2023 | | `THA` | 324 | 2016 | 2021 | | `MNG` | 324 | 2011 | 2017 | | `ARM` | 215 | 2014 | 2017 | | `AFG` | 190 | 2014 | 2021 | | `MMR` | 154 | 2018 | 2020 | | `BGD` | 107 | 2013 | 2017 | | `NPL` | 107 | 2008 | 2017 | | `LAO` | 103 | 2017 | 2022 | | ... | _4 more countries_ | | | ## Indicators (sample) - `GED_XLU4_SEX_HHT_GEO_RT` — Prime-age composite rate of labour underutilization (LU4) by sex, household type and rural / urban areas (%) ## 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 | `GED_XLU4_SEX_HHT_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Prime-age composite rate of labour un…` | | `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.) | `HHT_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Household type: Total` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `26.905` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-ged-xlu4-sex-hht-geo-rt-prime-age-composite-rate-of-labour-underutilizatio") 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"] == "GED_XLU4_SEX_HHT_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="GED_XLU4_SEX_HHT_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "GED_XLU4_SEX_HHT_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_ged_xlu4_sex_hht_geo_rt_prime_age_composite_rate_of_labour_underutilizatio_2025, title = {Prime-age composite rate of labour underutilization (LU4) by sex, household type and rural | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=GED_XLU4_SEX_HHT_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ged-xlu4-sex-hht-geo-rt-prime-age-composite-rate-of-labour-underutilizatio}} } ``` ## 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=GED_XLU4_SEX_HHT_GEO_RT_This dataset contains 6,056 observations across 19 Asia countries from 2000 to 2025, focusing on the indicator Prime-age composite rate of labour underutilization (LU4) by sex, household type and rural / urban areas (%). It is sourced from ILOSTAT, the International Labour Organizations (ILO) central statistics database, covering other measures of labour underutilization. The dataset includes detailed columns such as country code, country name, source code, source label, indicator code, indicator label, sex disaggregation (total, male, female), household type classification, rural/urban area classification, observation year, observed value, observation status, and more. Data is extracted via the ILOSTAT API and filtered for Asia countries, harmonized using International Conference of Labour Statisticians (ICLS) definitions, and suitable for tabular classification, regression, and time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

该数据集源自国际劳工组织（ILO）的ILOSTAT统计数据库，专注于亚洲区域劳动力利用不足的度量。数据通过ILOSTAT的REST API直接获取，并依据亚洲国家ISO3代码进行地理范围过滤。原始数据来自各国劳动力调查、家庭收入调查等官方统计，经ILO统计部门按照国际劳工统计学家会议（ICLS）定义进行标准化处理，涵盖2000年至2025年间19个亚洲国家的共6056条观测记录。数据集中包含单一核心指标——按性别、家庭类型和城乡分类的壮年劳动力利用不足综合率（LU4），并附有来源标记以保障可追溯性。

特点

该数据集的核心特点在于其精细的多维分类结构。数据不仅按性别（总、男、女）进行分解，还通过两个分类变量（classif1和classif2）进一步划分家庭类型和地理覆盖范围（如国家总体、城乡区域），使用户能够深入分析劳动力利用不足在不同群体间的异质性。数据覆盖时间跨度较长（2000-2025年），且包含多种观测状态标记（如“临时”“不可靠”），为时间序列分析和数据质量评估提供了便利。所有数据均遵循CC-BY-4.0许可，便于学术研究和政策分析。

使用方法

用户可通过HuggingFace Datasets库便捷加载该数据集，使用`load_dataset()`函数即可获得可直接转换为Pandas DataFrame的训练集。典型操作包括按国家代码筛选特定国家的观测（如`df[df['ref_area']=='IDN']`），或对单一指标进行时间序列可视化（如按年份排序并绘制`obs_value`曲线）。用户还可通过透视表功能将数据重塑为国家×年份矩阵，便于进行跨国的比较分析。数据集的列结构清晰，包含区域、来源、指标、时间、观测值及分类维度等字段，适合进行回归、分类或时间序列预测等机器学习任务。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年构建，并经Electric Sheep Asia重新打包发布，聚焦亚洲19个国家2000至2025年间劳动利用不足的综合指标。其核心研究问题在于通过性别、家庭类型及城乡维度，精准刻画劳动年龄人口中未充分就业、隐性失业等复合性劳动闲置状况，突破传统失业率指标的局限性。作为全球劳动统计的权威来源，ILOSTAT数据库整合了各国劳动力调查与行政记录，该数据集为区域劳动力市场研究提供了标准化、高时间分辨率的关键数据支撑，对推动亚洲发展中国家就业政策评估与可持续发展目标监测具有显著影响力。

当前挑战

该数据集首先应对的领域挑战在于，传统失业率无法捕捉潜在劳动力与边缘性就业群体，而LU4复合指标需整合时间相关不充分就业、潜在劳动力与失业人员等多维度信息，对统计口径的协同性要求极高。构建过程中面临的主要挑战包括：跨19国家源数据在调查设计、抽样框架与季节调整上的异质性需通过ILO统一协调；部分国家（如阿富汗、缅甸）的年度数据稀疏且标注为“不可靠”，影响时间序列分析的稳健性；此外，数据需在性别、家庭类型与城乡分类间交叉拆解，分层样本量不足易导致估计误差，对统计推断的科学性形成严峻考验。

常用场景

经典使用场景

该数据集收录了亚洲19个国家2000年至2025年间壮年劳动力利用不足综合率（LU4）的观测数据，按性别、家庭类型和城乡地域进行精细划分。其经典使用场景在于为劳动经济学、发展经济学及公共政策研究提供跨时空的标准化面板数据，支持学者通过统计建模与计量分析，系统探究劳动力市场结构性失衡的动态演变规律。研究者可基于时间序列分解、固定效应回归或工具变量法等工具，剖析性别差异与家庭结构对劳动力资源配置效率的异质性影响，进而为构建劳动力参与率预测模型与就业质量评估框架奠定坚实的数据基础。

解决学术问题

该数据集解决了在亚洲区域背景下，因劳动力统计口径不统一、地域覆盖碎片化而导致的宏观比较研究困局。它系统性地填补了在壮年群体中，综合衡量失业、时间相关不充分就业与潜在劳动力队伍等多元维度的利用不足度量空白。借助ILO严格依据国际劳工统计学家会议标准进行统一协调的高质量统计资料，学者得以澄清影响劳动力市场弹性的结构性因素，并为评估可持续发展目标中体面劳动议题在亚洲的进展提供了可重复验证的数据锚点，从而深化了对非正规就业与隐蔽失业等隐性社会问题的学术认知。

衍生相关工作

该数据集的发布催生了多项与劳动力统计协调、机器学习可解释性及多指标综合评价方法相关的衍生学术工作。基于其精细的维度划分，研究者拓展了将性别与家庭类型分类作为关键协变量的劳动力利用不足动态预测模型，并探索了随机森林与梯度提升树在识别高失业风险群体中的应用边界。此外，该数据集常被用作ILOSTAT全球统计体系亚洲子集的基准样本，支撑了关于不同聚类算法在劳动力市场结构分类中的比较研究，也推动了面向持久性劳动力利用不足评价的多源数据融合方法论创新。