electricsheepeurope/europe-ilo-luu-xlu2-sex-age-geo-rt-combined-rate-of-time-related-underemployment-and

--- 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: "Combined rate of time-related underemployment and unemployment (LU2) by sex, age and rural | Europe (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex, age and rural | Europe (ILOSTAT) 🇪🇺 **46,908 observations** · **34 Europe countries** · **1998–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **46,908 observations** of `Other measures of labour underutilization` data across **34 Europe countries**, spanning **1998–2025**, 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_XLU2_SEX_AGE_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=LUU_XLU2_SEX_AGE_GEO_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 34 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `AUT` | 1,951 | 1998 | 2025 | | `PRT` | 1,883 | 1998 | 2024 | | `FRA` | 1,868 | 1998 | 2024 | | `FIN` | 1,822 | 1999 | 2024 | | `SWE` | 1,760 | 2000 | 2024 | | `DNK` | 1,724 | 2000 | 2024 | | `ESP` | 1,709 | 1999 | 2024 | | `NLD` | 1,705 | 2000 | 2024 | | `EST` | 1,684 | 1998 | 2024 | | `BEL` | 1,680 | 1999 | 2024 | | `LTU` | 1,651 | 2001 | 2024 | | `HUN` | 1,650 | 2001 | 2024 | | `ITA` | 1,650 | 2002 | 2024 | | `LVA` | 1,636 | 2001 | 2024 | | `GBR` | 1,615 | 1999 | 2019 | | ... | _19 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_AGE_GEO_RT` — Combined rate of time-related underemployment and unemployment (LU2) by sex, age and rural / urban areas (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AUT` | | `ref_area.label` | `string` | Country name in English | `Austria` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:103` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `LUU_XLU2_SEX_AGE_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Combined rate of time-related underem…` | | `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+` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `8.438` | | `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` | | `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-luu-xlu2-sex-age-geo-rt-combined-rate-of-time-related-underemployment-and") 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"] == "LUU_XLU2_SEX_AGE_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_AGE_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_AGE_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu2_sex_age_geo_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex, age and rural | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_AGE_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu2-sex-age-geo-rt-combined-rate-of-time-related-underemployment-and}} } ``` ## 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=LUU_XLU2_SEX_AGE_GEO_RT_This dataset contains 46,908 observations of Other measures of labour underutilization data across 34 Europe countries, spanning 1998–2025, covering 1 distinct indicator. The core indicator is Combined rate of time-related underemployment and unemployment (LU2) by sex, age and rural / urban areas (%). Data is sourced from ILOSTAT, the ILOs central statistics database and a leading global source for labour statistics, which compiles harmonized indicators on employment, unemployment, wages, and related topics. The dataset includes a detailed schema with columns such as country code, country name, source code, source label, indicator code, indicator label, sex disaggregation, age classification, rural/urban classification, observation year, observed value, observation status, and notes. Data is harmonized using International Conference of Labour Statisticians (ICLS) definitions and flagged with sources for traceability. It is suitable for tabular classification, regression, and time-series forecasting tasks, primarily for labour market analysis and research.

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，通过其REST API直接获取指标LUU_XLU2_SEX_AGE_GEO_RT的原始数据，并依据ISO 3166-1 alpha-3标准筛选出欧洲34个国家的观测值。数据经ILOSTAT依据国际劳工统计学家会议（ICLS）定义进行统一整合，确保不同国家来源数据具有可比性。Electric Sheep Europe团队将原始数据重新封装，以Parquet格式发布在HuggingFace平台上，便于机器学习和时间序列分析场景下的直接调用。

特点

该数据集包含46,908条观测记录，覆盖1998年至2025年间的34个欧洲国家，聚焦于时间相关就业不足与失业复合率（LU2）这一核心指标。数据按性别（总、男性、女性）、年龄段和城乡地域进行细致分层，提供了多维度的劳动利用不足视图。各观测值附带来源标签、数据状态标识及可能的序列断裂注释，便于用户评估数据质量。数据以CSV或Parquet格式存储，结构清晰，包含国家代码、指标代码、时间、数值等关键字段。

使用方法

用户可通过HuggingFace Datasets库的load_dataset函数一键加载该数据集，转换为Pandas DataFrame后即可进行探索性分析与建模。典型用法包括按国家筛选特定序列、按时间排序绘制指标变化趋势图，或利用透视表构建国家×年份的矩阵。数据集适用于分类、回归以及时间序列预测任务，尤其适合欧洲劳动力市场多国比较研究和政策评估。研究人员需在成果中同时引用ILO原始数据和Electric Sheep Europe的重新封装版本。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门创建，经Electric Sheep Europe重新整理后于2025年发布，聚焦于欧洲34个国家1998至2025年间的时间相关就业不足与失业综合率（LU2）指标。作为ILOSTAT数据库的核心组成部分，该数据集以性别、年龄及城乡地域为分层维度，提供了近4.7万条标准化观测记录，旨在刻画欧洲劳动力市场中隐性劳动力利用不足的复杂图景。在国际劳工统计学家会议（ICLS）定义框架下，数据通过各国劳动力调查等源头整合并统一标准化，为劳动经济学研究、跨国比较以及可持续发展目标（SDG）中体面工作指标的监测提供了关键数据基础。该数据集的出现显著提升了欧洲次国家级劳动力冗余度量研究的可获取性与可复现性，推动了政策评估与学术建模的精细化发展。

当前挑战

该数据集面临的领域挑战在于，时间相关就业不足与失业综合率（LU2）作为劳动力利用不足的多维测度，需克服传统失业率对隐性闲置劳动力的低估，捕捉部分就业者因工时不足而被迫接受非充分就业的状态。构建过程中，不同国家调查方法与统计口径的差异导致数据可比性面临考验，ILOSTAT需通过ICLS统一框架进行复杂协调，并保留来源标记以保障溯源性。此外，多年份跨度内抽样方式、问卷迭代与行政记录变化的递接，可能引发时序化的结构性断裂，如数据集注释中标记的方法修订（Methodology revised）即为例证；加之部分观测值被标注为不可靠（Unreliable），凸显了在跨时期空间汇总中维持数据一致性与置信度的长期挑战。

常用场景

经典使用场景

该数据集记录了1998年至2025年间34个欧洲国家的时间相关就业不足与失业综合率（LU2），并按性别、年龄及城乡维度进行精细分层。研究者可将其用于劳动力市场结构性特征的纵向追踪，通过构建面板数据模型，深入剖析不同人口群体在劳动力利用不足方面的动态差异。经典应用包括利用固定效应或随机效应模型，控制国家与时间效应后，识别性别、年龄与地理区位对LU2率的独立影响；亦可通过时间序列分解方法，提取趋势与周期成分，揭示经济波动与劳动力闲置之间的内在关联。该数据集为欧洲劳动经济学和比较社会政策研究提供了标准化的定量基础。

衍生相关工作

该数据集所依托的ILOSTAT统计体系，已催生了一系列经典学术工作。例如，诸多研究基于ILO的劳动力利用不足指标构建全球劳动市场脆弱性指数，分析其与宏观经济周期、政策干预的交互效应。另有学者利用LU2等指标，结合机器学习方法预测劳动市场疲软信号，为早期预警系统提供特征工程基础。在空间经济分析领域，基于该数据集的欧洲城乡LU2差异研究，为解释区域收入不平等与人口迁移模式提供了新的劳动市场维度。此外，数据集的标准化工序（如Electric Sheep Europe的重新封装）本身也成为数据科学社区中可复现数据流水线的参考案例。

数据集最近研究

最新研究方向

在劳动力市场研究领域，时间相关就业不足与失业的复合比率（LU2）作为劳动利用不足的综合度量指标，正日益成为解析欧洲劳动力市场韧性与结构性变迁的前沿焦点。伴随新冠疫情后弹性工作模式的普及与地缘经济格局的剧烈重构，研究者借助该数据集跨越1998至2025年间覆盖34个欧洲国家的长时序、多维度观测，深入探究性别、年龄及城乡维度下劳动利用不足的动态演变规律及其与社会保护政策的交互效应。该数据为评估国际劳工组织体面劳动目标推进、预警劳动力市场失衡以及校准区域就业干预策略提供了坚实的数据基础，其影响正延伸至可持续劳动转型与欧洲社会政策实证研究的核心地带。