electricsheepasia/asia-ilo-ged-xlu3-sex-hht-geo-rt-prime-age-combined-rate-of-unemployment-and-potent

--- 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 combined rate of unemployment and potential labour force (LU3) by sex, household | Asia (ILOSTAT)" --- # Prime-age combined rate of unemployment and potential labour force (LU3) by sex, household | Asia (ILOSTAT) 🌏 **8,252 observations** · **22 Asia countries** · **2000–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **8,252 observations** of `Other measures of labour underutilization` data across **22 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_XLU3_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_XLU3_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 22 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,019 | 2000 | 2020 | | `PHL` | 918 | 2007 | 2023 | | `ARM` | 901 | 2001 | 2023 | | `PAK` | 616 | 2006 | 2025 | | `LKA` | 607 | 2010 | 2022 | | `TUR` | 540 | 2004 | 2013 | | `MNG` | 539 | 2003 | 2017 | | `IND` | 492 | 2005 | 2024 | | `KHM` | 492 | 2007 | 2023 | | `VNM` | 486 | 2007 | 2024 | | `THA` | 324 | 2016 | 2021 | | `MMR` | 241 | 2015 | 2020 | | `BGD` | 211 | 2006 | 2017 | | `AFG` | 187 | 2014 | 2021 | | `TLS` | 115 | 2010 | 2016 | | ... | _7 more countries_ | | | ## Indicators (sample) - `GED_XLU3_SEX_HHT_GEO_RT` — Prime-age combined rate of unemployment and potential labour force (LU3) 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_XLU3_SEX_HHT_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Prime-age combined rate of unemployme…` | | `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) | `21.311` | | `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`** (4 unique values): `SEX_T`, `SEX_M`, `SEX_F`, `SEX_O` ## 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-xlu3-sex-hht-geo-rt-prime-age-combined-rate-of-unemployment-and-potent") 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_XLU3_SEX_HHT_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="GED_XLU3_SEX_HHT_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "GED_XLU3_SEX_HHT_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_ged_xlu3_sex_hht_geo_rt_prime_age_combined_rate_of_unemployment_and_potent_2025, title = {Prime-age combined rate of unemployment and potential labour force (LU3) by sex, household | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=GED_XLU3_SEX_HHT_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ged-xlu3-sex-hht-geo-rt-prime-age-combined-rate-of-unemployment-and-potent}} } ``` ## 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_XLU3_SEX_HHT_GEO_RT_This dataset contains 8,252 observations across 22 Asia countries from 2000 to 2025, focusing on Other measures of labour underutilization, specifically the indicator Prime-age combined rate of unemployment and potential labour force (LU3) by sex, household type and rural / urban areas (%). Sourced from the International Labour Organization (ILO) ILOSTAT database, it is extracted via API and filtered to Asia ISO3 country codes, featuring structured columns such as country code, indicator code, sex classification, observation year, and observed values, suitable for labor market analysis and time-series forecasting.

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，聚焦亚洲地区劳动力利用不足的测量指标，具体为按性别、家庭类型和城乡区域划分的壮年劳动力失业与潜在劳动力综合比率（LU3）。数据通过ILOSTAT REST API直接提取，并依据ISO 3166-1 alpha-3国家代码筛选出22个亚洲国家的观测记录，时间跨度覆盖2000年至2025年。构建过程中，ILO依据国际劳工统计学家会议（ICLS）的各项定义对原始调查微观数据进行统一协调，并采用‘最佳来源’原则处理同一国家与年份的多源数据，确保指标的连贯性与可比性，最终形成包含8,252条观测的结构化表格数据集。

特点

该数据集的一个显著特点是其精细的维度划分，涵盖性别（总、男、女及其他）、家庭类型和地理覆盖（国家与城乡）等多个层次，为分析劳动力市场结构差异提供了颗粒度。数据时间跨度长达25年，覆盖亚洲主要经济体，如中国、印度、土耳其等，具备时间序列分析的潜力。此外，数据集中保留了来源代码、观测状态标志（如临时值、不可靠值）及注释信息，增强了数据的可追溯性与透明性，便于研究者评估数据质量并应用于严谨的学术分析。

使用方法

数据集以Parquet格式存储于HuggingFace平台，可通过`datasets`库的`load_dataset`函数便捷加载，并直接转换为Pandas DataFrame进行后续操作。用户可按国别（如‘ref_area’列）筛选特定国家，或依据指标代码对单一指标进行时间序列分析与可视化，还可透视生成国家×年份的矩阵面板数据，适用于统计建模与面板数据分析。示例代码展示了加载、筛选、排序与绘图等典型应用流程，降低了使用门槛，支持研究人员快速切入劳动力市场研究、政策评估及经济学实证分析等场景。

背景与挑战

背景概述

在劳动经济学领域，准确衡量劳动力市场的未充分利用状况对于制定有效的就业政策至关重要。传统的失业率指标往往无法全面反映潜在劳动力及非充分就业的复杂情况，为此国际劳工组织（ILO）设计了包括LU3在内的多项综合指标。该数据集由ILO统计部门基于其核心数据库ILOSTAT编制，经Electric Sheep Asia于2025年重新封装并发布于HuggingFace平台，聚焦亚洲22个国家和地区，时间跨度覆盖2000至2025年，包含8252条观测记录。数据以'黄金年龄'（prime-age）人口为对象，按性别、家庭类型及城乡区域细分，呈现失业与潜在劳动力之和的复合比率，为研究亚洲地区劳动力市场结构、性别差异及区域不平衡提供了珍贵资料。此数据集不仅丰富了ILOSTAT在亚洲区域的精细度，也为比较劳动经济学和社会政策分析提供了实证基础。

当前挑战

在领域层面，劳动力未充分利用的度量长期面临口径不一、数据可比性差等难题，不同国家采用不同调查方法及统计标准，导致跨国比较充满挑战；本数据集致力于通过ILO统一的ICLS定义和协调流程缓解此问题，但数据中仍存在缺失值和标记为不可靠（如'Unreliable'）的观测，体现了原始数据质量的参差不齐。构建过程中，数据经ILOSTAT REST API抓取并过滤至亚洲国家，需处理多源数据冲突（由ILO选择'最佳来源'），以及维度分类的复杂性（性别、家庭类型、城乡等），同时维护跨年月度或季度序列的遗漏问题。此外，样本不均衡显著，各国观测数从百至千余不等，且时间跨度差异大，如塞浦路斯数据始于2000而印度至2005年才纳入，这些因素加大了时间序列分析和面板模型的应用难度。

常用场景

经典使用场景

该数据集为亚洲地区劳动力市场研究提供了精细化的时空数据支撑，聚焦于壮年劳动力未充分利用的综合指标（LU3），按性别、家庭类型及城乡地域维度细化，覆盖22国、逾八千观测，时间跨度延伸至2025年。研究者可借此开展面板数据分析，构建多元回归模型以探究劳动力市场结构性变迁，亦可运用时间序列分析方法监测经济波动下的就业弹性，为比较劳动经济学与发展经济学领域提供可靠的实证基础。

衍生相关工作

基于该数据集，衍生出诸多前沿研究方向与成果。学界可构建劳动力市场脆弱性指数，评估经济危机冲击下的复原力；结合机器学习方法，可进行劳动力供需预测与早期预警系统开发。ILOSTAT本身被广泛用于国际劳工组织的旗舰报告，如《世界就业与社会展望》，其数据也支撑了多项关于非正规就业、青年失业及女性劳动参与的经典实证研究。此数据集进一步拓展了这些工作的亚洲区域深度。

数据集最近研究

最新研究方向

该数据集聚焦亚洲22国2000至2025年间青年及壮年劳动力市场未充分利用的综合测度，涵盖失业与潜在劳动力（LU3）按性别及家庭类型的细分。当前前沿研究方向主要集中在运用机器学习与时间序列预测模型，解析家庭结构差异对劳动力参与的影响，特别是在疫情后亚洲经济复苏与就业弹性评估中。该数据与世界银行、亚开行等机构发布的区域劳动力报告互动，为比较政策分析提供了SL与ILO标准对齐的数据基础，助力实现SDG第8项体面工作目标。其按地理区域、家庭类型及性别的多维离散化不仅提升了微观层面就业研究的精度，也推动了劳动统计指标在亚洲新兴经济体间的可比性研究，对洞察非正规就业、性别鸿沟及潜在劳动力规模变动的长期演变具有重要实证价值。