electricsheepeurope/europe-ilo-ged-xlu4-sex-hht-chl-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: - 10K<n<100K tags: - tabular - europe - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Prime-age composite rate of labour underutilization (LU4) by sex, household type and prese | Europe (ILOSTAT)" --- # Prime-age composite rate of labour underutilization (LU4) by sex, household type and prese | Europe (ILOSTAT) 🇪🇺 **51,977 observations** · **29 Europe countries** · **2000–2024** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **51,977 observations** of `Other measures of labour underutilization` data across **29 Europe countries**, spanning **2000–2024**, 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_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_XLU4_SEX_HHT_CHL_RT` and filtered to Europe 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 29 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `BEL` | 2,752 | 2000 | 2023 | | `AUT` | 2,736 | 2000 | 2023 | | `ROU` | 2,665 | 2000 | 2023 | | `ESP` | 2,644 | 2000 | 2023 | | `HUN` | 2,610 | 2000 | 2023 | | `SVN` | 2,522 | 2000 | 2023 | | `EST` | 2,515 | 2000 | 2023 | | `LVA` | 2,461 | 2001 | 2023 | | `LTU` | 2,422 | 2002 | 2023 | | `BGR` | 2,406 | 2001 | 2023 | | `GBR` | 2,338 | 2000 | 2019 | | `NLD` | 2,298 | 2000 | 2020 | | `HRV` | 2,209 | 2002 | 2023 | | `POL` | 2,166 | 2006 | 2024 | | `DEU` | 2,140 | 2005 | 2023 | | ... | _14 more countries_ | | | ## Indicators (sample) - `GED_XLU4_SEX_HHT_CHL_RT` — Prime-age composite rate of labour underutilization (LU4) by sex, household type and presence of children (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `ALB` | | `ref_area.label` | `string` | Country name in English | `Albania` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:480` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `GED_XLU4_SEX_HHT_CHL_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 | `CHL_AGET6_TOTAL` | | `classif2.label` | `string` | — | `Presence of children under age 6: Total` | | `time` | `int64` | Observation year | `2023` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `13.836` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_source` | `string` | — | `R1:3513` | | `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("electricsheepeurope/europe-ilo-ged-xlu4-sex-hht-chl-rt-prime-age-composite-rate-of-labour-underutilizatio") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python germany = df[df["ref_area"] == "DEU"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "GED_XLU4_SEX_HHT_CHL_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="GED_XLU4_SEX_HHT_CHL_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "GED_XLU4_SEX_HHT_CHL_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ged_xlu4_sex_hht_chl_rt_prime_age_composite_rate_of_labour_underutilizatio_2024, title = {Prime-age composite rate of labour underutilization (LU4) by sex, household type and prese | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=GED_XLU4_SEX_HHT_CHL_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ged-xlu4-sex-hht-chl-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 Europe repackaging. ## About Electric Sheep Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe 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/electricsheepeurope](https://huggingface.co/electricsheepeurope) --- _Provenance: ingested 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=GED_XLU4_SEX_HHT_CHL_RT_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically focusing on the Prime-age composite rate of labour underutilization (LU4) by sex, household type and presence of children. It covers 29 European countries from 2000 to 2024, with 51,977 observations and 1 core indicator (GED_XLU4_SEX_HHT_CHL_RT). The data is organized in tabular format, including columns such as country code, source, indicator, sex classification, household type, presence of children, year, observed value, and more, with detailed metadata and disaggregation dimensions. Repackaged by Electric Sheep Europe, it aims to provide a unified, ML-ready data layer for Europe, suitable for tasks like tabular classification, regression, 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数据库，通过REST API直接提取指标GED_XLU4_SEX_HHT_CHL_RT的数据，并依据欧洲ISO3国家代码进行过滤。数据来源于各国劳动力调查、家庭收入调查以及行政记录等官方统计资源，经ILO统计部门依据国际劳动统计学家会议（ICLS）定义进行统一协调与标准化处理，确保跨国可比性。Electric Sheep Europe团队对原始数据进行了重打包，统一为Parquet格式，并附上标准化的数据集卡片，以提升机器学习就绪度。

特点

该数据集共包含51,977条观测记录，覆盖2000年至2024年间29个欧洲国家，聚焦于劳动利用不足综合率（LU4）这一单一指标。数据按性别、家庭类型及有无子女维度进行详细分解，提供了精细化的人口特征洞察。此外，数据集中包含了观测状态标记（如临时性、不可靠）、数据源标注等元信息，增强了数据的可追溯性与质量透明度，为跨国家、跨时期的劳动市场分析提供了坚实的数据基础。

使用方法

该数据集通过HuggingFace Datasets库可直接加载，用户仅需一行Python代码（load_dataset）即可获取训练集并转换为pandas DataFrame进行后续分析。支持按国家编码过滤特定国家的时间序列数据，也可针对单一指标按时间排序并进行可视化。此外，通过透视表操作，可便捷地构建以年份为行、国家为列的观测值矩阵，适合用于面板数据分析或时间序列建模任务，极大降低了劳动经济学研究的数据预处理门槛。

背景与挑战

背景概述

劳动力利用不足的精准测度是劳动经济学与公共政策研究中的核心议题，尤其是在欧洲错综复杂的劳动力市场格局下。国际劳工组织（ILO）长期致力于通过ILOSTAT数据库提供全球劳动统计的标准化参照。该数据集由Electric Sheep Europe于2024年重新整理并发布，聚焦于欧洲29个国家2000年至2024年间“壮年劳动力利用不足综合率（LU4）”指标，按性别、家庭类型及六岁以下儿童存在状况进行了精细分层。数据源自ILOSTAT官方API，经由ILO依据国际劳工统计学家会议（ICLS）定义对原始调查微观数据进行协调处理，旨在为研究者提供一份高质量、可直接用于机器学习与时间序列分析的标准化面板数据。该数据集的问世有力推动了劳动力市场结构性困境的跨国家量化研究，为理解欧洲地区隐性失业与就业不足的性别与家庭特征差异提供了关键实证基础。

当前挑战

该数据集所应对的领域问题在于传统失业率指标无法全面捕捉劳动力市场的真实困境，诸如因家庭照护责任被迫减少工时或丧失工作意愿的群体在标准失业统计中往往被忽略，而LU4综合率通过整合失业、时间相关就业不足及潜在劳动力等多维信息，为刻画就业质量与劳动力市场闲置状况提供了更敏锐的指标。在数据集构建过程中，核心挑战包括：一是跨国家的样本协调性，29个国家各自采用不同的劳动力调查设计与数据采集频率，ILO虽已进行标准化，但来源差异在观测状态标记（如‘U’表示不可靠）中仍清晰可辨；二是分层维度的稀疏性，按性别、家庭类型及儿童存在状况的交叉分类导致许多组合下的观测值缺失，妨碍了细粒度分析；三是时间序列的非均衡性，部分国家数据可追溯至2000年而另一些则从2006年后才加入，跨时期可比性受到限制，对纵向建模造成障碍。

常用场景

经典使用场景

该数据集是欧洲劳动力市场研究领域的重要资源，核心服务于劳动利用率不足的量化评估。其最经典的用法在于利用LU4复合指标，揭示不同性别、家庭类型及有无幼童情境下的劳动资源闲置状况。研究者可通过过滤特定国家或时间范围，构建面板数据进行跨年度、跨区域的比较分析，从而洞察劳动力市场的结构性冗余与动态变迁。这种分层视角对于理解欧洲各国在经济波动与环境转变中的劳动力弹性尤为关键。

衍生相关工作

围绕该数据集已衍生出一系列标志性研究，涵盖劳动力市场分异、家庭结构影响及宏观政策评估等方向。其一，研究者聚焦于性别与家庭角色对LU4的非对称效应，验证了儿童照护负担如何显著抑制女性劳动供给。其二，基于该数据的时间序列特征，催生了劳动利用率与经济周期联动的预测模型，深化了对周期性失业与结构性失业的区分。其三，跨国家比较分析揭示了福利制度与劳动利用率的复杂关联，为比较政治经济学提供了新颖的量化佐证，推动了制度主义视角下劳动力市场治理研究的演进。

数据集最近研究

最新研究方向

在全球劳动力市场结构深刻演变与后疫情时代经济复苏乏力的双重背景下，劳动利用不足的精准测度已成为国际劳工统计学的核心前沿。该数据集聚焦于欧洲29国2000至2024年间，按性别、家庭类型及儿童照护责任分层的25-54岁黄金年龄段综合劳动利用不足率（LU4），突破了传统失业率单一视角的局限。欧洲各国近年来在欧盟“欧洲社会权利支柱”行动计划的推动下，愈发关注隐性失业、就业不足及劳动力市场边缘群体，该数据集为此类研究提供了维度丰富、时间跨度长的标准化微观数据。其引入的家庭类型与幼龄子女照护分类，尤为契合新冠疫情后“她经济”衰退与职业性别隔离加剧的热点议题，为设计更具包容性的积极劳动力市场政策提供了数据基石。通过ILOSTAT统一核算框架与HuggingFace平台的无缝衔接，这一资源正推动劳动经济学研究从静态描述迈向动态、多维度的因果推断与政策模拟，对深化理解欧洲劳动力韧性具有深远意义。