electricsheepasia/asia-ilo-ged-xlu3-sex-hht-chl-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: - 10K<n<100K 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) 🌏 **19,835 observations** · **24 Asia countries** · **2000–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **19,835 observations** of `Other measures of labour underutilization` data across **24 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_CHL_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_CHL_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 24 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `PHL` | 2,205 | 2003 | 2023 | | `CYP` | 1,914 | 2000 | 2020 | | `IRN` | 1,877 | 2008 | 2024 | | `ARM` | 1,835 | 2001 | 2023 | | `PAK` | 1,405 | 2006 | 2025 | | `LKA` | 1,269 | 2010 | 2022 | | `KHM` | 1,209 | 2003 | 2023 | | `TUR` | 1,173 | 2004 | 2013 | | `MNG` | 1,124 | 2003 | 2017 | | `VNM` | 1,035 | 2007 | 2024 | | `IND` | 897 | 2005 | 2024 | | `THA` | 665 | 2016 | 2021 | | `MMR` | 526 | 2015 | 2020 | | `BGD` | 465 | 2006 | 2017 | | `AFG` | 433 | 2014 | 2021 | | ... | _9 more countries_ | | | ## Indicators (sample) - `GED_XLU3_SEX_HHT_CHL_RT` — Prime-age combined rate of unemployment and potential labour force (LU3) by sex, household type and presence of children (%) ## 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_CHL_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 | `CHL_AGET6_YES` | | `classif2.label` | `string` | — | `Presence of children under age 6: Yes` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `19.289` | | `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-xlu3-sex-hht-chl-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_CHL_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="GED_XLU3_SEX_HHT_CHL_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "GED_XLU3_SEX_HHT_CHL_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_ged_xlu3_sex_hht_chl_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_CHL_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-ged-xlu3-sex-hht-chl-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_CHL_RT_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically focused on Asia. The core indicator is Prime-age combined rate of unemployment and potential labour force (LU3) by sex, household type and presence of children (%), which measures the combined rate of unemployment and potential labor force for prime-age individuals, disaggregated by sex, household type, and presence of children. It covers 24 Asian countries from 2000 to 2025, with 19,835 observations. The dataset is structured in tabular format, including columns such as country code, source, indicator code, sex classification (total, male, female), household type, children presence, observation year, observed value, and data status flags. It is suitable for tabular classification, regression, and time-series forecasting tasks. Data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and includes source flags for traceability.

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，经由Electric Sheep Asia重新整理打包而成。数据通过ILOSTAT REST API接口直接抓取，并依据ISO 3166-1 alpha-3国家代码筛选出亚洲24个国家的观测记录，时间跨度覆盖2000年至2025年。原始数据基于各国劳动力调查、家庭收入调查等来源，经ILO依据国际劳工统计学家会议（ICLS）定义进行统一整合，确保指标口径的一致性与可追溯性。数据以表格形式存储，包含国家代码、来源标识、指标代码、性别、家庭类型、儿童存在情况、观测年份及数值等字段，共计19,835条观测，每个观测均附有来源标签以供核验。

特点

该数据集聚焦于亚洲地区劳动参与不足的度量，具体涵盖黄金年龄段的失业与潜在劳动力综合比率（LU3），并按性别、家庭类型及是否有六岁以下子女进行维度拆解。数据覆盖24个亚洲国家，时间跨度长达26年，提供丰富的面板数据，便于进行跨国比较与时间序列分析。数据集的一大特色是其细粒度的分类维度，包括性别（总计、男性、女性）以及家庭类型和儿童存在情况等子分类，使研究者能够深入探究劳动市场的结构性特征。此外，数据质量经过严格标注，包括观测状态（如临时值、不可靠值）及来源注释，增强了数据的可信度与实用性。

使用方法

该数据集可通过HuggingFace的datasets库便捷加载，用户只需使用load_dataset()函数即可获取数据，并转换为Pandas DataFrame进行后续分析。数据集的典型应用包括：对特定国家进行子集筛选（如印度尼西亚），或对单一指标进行时间序列可视化，以揭示劳动市场趋势。此外，用户可利用pivot_table功能将数据重塑为国家×年份的面板矩阵，便于进行计量经济学分析或构建预测模型。由于数据集已标准化为统一架构，研究人员可直接应用于表格分类、回归及时间序列预测等任务，极大降低了数据清洗与整合的预处理成本。

背景与挑战

背景概述

在经济学与劳动统计领域，准确衡量劳动力市场的未充分利用程度对于制定有效就业政策至关重要。国际劳工组织（ILO）作为全球劳动统计的权威机构，其ILOSTAT数据库整合了200多个经济体的劳动力调查数据，提供了包括LU3在内的多项综合指标。本数据集由Electric Sheep Asia于2025年重新打包发布，聚焦亚洲24个国家，涵盖2000至2025年间总计19,835条观测记录，核心指标为按性别、家庭类型及子女状况划分的壮年劳动力失业与潜在劳动力综合比率（LU3）。该数据集通过ILOSTAT REST API获取原始数据，并依据国际劳工统计学家会议（ICLS）定义进行统一化处理，为研究亚洲区域劳动力市场结构、性别差异及家庭因素对就业的影响提供了高粒度、跨时间的宝贵资源。其发布不仅促进了劳动经济学领域的实证研究，也为政策制定者提供了数据支持，具有显著的学术与应用价值。

当前挑战

本数据集在构建与使用中面临多重挑战。首要挑战源于劳动力统计本身的复杂性，LU3指标旨在捕捉失业与潜在劳动力综合比率，其定义涉及对未充分利用状态的细致划分，要求数据来源具备高度一致性，而不同国家的调查设计、覆盖范围及统计口径差异可能导致可比性下降。其次，数据整合过程中，多源数据的融合需处理时序不连续、分类变量（如家庭类型、子女状况）标注标准不统一等问题，ILO虽采用‘最佳源’策略，但部分观测值仍被标记为不可靠（如‘U’状态），提示数据质量存在参差。此外，地理与时间覆盖的不均衡（如土耳其仅覆盖至2013年，泰国始于2016年）限制了跨区域纵向分析的稳健性，而附带的后设数据（如来源、注释）赋予的丰富性也增加了数据清洗与特征工程的复杂度。这些挑战要求使用者具备专业的劳动统计知识，并需采取审慎的方法论以确保研究的严谨性。

常用场景

经典使用场景

该数据集聚焦于亚洲24个国家2000至2025年间壮年劳动力（25-54岁）的综合失业与潜在劳动力率（LU3），按性别、家庭类型及有无六岁以下子女等维度进行精细分层。作为ILOSTAT的再封装产品，其高维细分结构为劳动经济学、人口学及发展研究提供了宝贵的面板数据，尤其适用于探究家庭责任与性别差异如何交织影响劳动力市场的边缘化状态，是进行跨国比较与纵向趋势分析的理想素材。

解决学术问题

在学术层面，该数据集有效回应了传统失业率指标在刻画劳动力闲置上的局限性。LU3指标融合失业与潜在劳动力，精准捕捉了因家庭照料而退出求职的隐性劳动力储备，从而为估算真实的劳动力利用不足程度提供了更为全面的视角。研究者可借此深入剖析亚洲各国在性别平等、家庭政策与劳动力市场弹性之间的复杂互动，弥补了既有研究中因数据颗粒度不足而导致的实证空缺。

衍生相关工作

该数据集的衍生工作旨在深化对劳动力利用不足现象的多维理解。一方面，研究者可将其与宏观经济变量（如GDP增长率、产业结构变迁）结合，构建计量模型以量化家庭结构变化对LU3的动态影响。另一方面，利用其时间序列特性，可开展状态空间模型或机器学习预测，模拟不同政策干预情景下的劳动力市场响应。此外，结合性别与家庭类型的交叉分类，为性别经济学中关于“育儿惩罚”与“潜在劳动力”的理论假说提供了亚洲区域的实证检验机会。