electricsheepeurope/europe-ilo-luu-xlu2-sex-edu-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, education and | Europe (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex, education and | Europe (ILOSTAT) 🇪🇺 **28,484 observations** · **34 Europe countries** · **1998–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **28,484 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_EDU_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_EDU_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 | |---------|-----:|-----------:|----------:| | `FRA` | 1,291 | 1998 | 2024 | | `PRT` | 1,196 | 1998 | 2024 | | `NLD` | 1,152 | 2000 | 2024 | | `SWE` | 1,118 | 2000 | 2024 | | `BEL` | 1,117 | 1999 | 2024 | | `ESP` | 1,113 | 1999 | 2024 | | `GBR` | 1,082 | 1999 | 2019 | | `DNK` | 1,074 | 2000 | 2024 | | `ITA` | 1,052 | 2002 | 2024 | | `AUT` | 1,042 | 1998 | 2025 | | `LUX` | 1,038 | 1999 | 2024 | | `FIN` | 980 | 1999 | 2024 | | `GRC` | 968 | 2006 | 2025 | | `HUN` | 957 | 2001 | 2024 | | `EST` | 888 | 1998 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_EDU_GEO_RT` — Combined rate of time-related underemployment and unemployment (LU2) by sex, education 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_EDU_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `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` | — | `C3:5578` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-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_EDU_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_EDU_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_EDU_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu2_sex_edu_geo_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex, education and | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_EDU_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu2-sex-edu-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_EDU_GEO_RT_This dataset is a tabular dataset containing labor market data for 34 European countries from 1998 to 2025, focusing on Other measures of labour underutilization. The core indicator is the Combined rate of time-related underemployment and unemployment (LU2) (ILOSTAT code: LUU_XLU2_SEX_EDU_GEO_RT), which measures the combined rate of time-related underemployment and unemployment disaggregated by sex, education level, and rural/urban areas (percentage). It comprises 28,484 observations sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via its REST API, filtered, and repackaged. The schema includes columns such as country code, indicator, sex, education classification, area type, observation year, observed value, and data status flags, making it suitable for tasks like tabular classification, regression, or 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中央统计数据库，通过其REST API直接提取指标ID为LUU_XLU2_SEX_EDU_GEO_RT的观测数据，并依据欧洲ISO3国家代码进行地理范围筛选。原始数据基于各国劳动力调查、家庭收入调查等多源微观数据，经ILO按照国际劳工统计学家会议（ICLS）定义进行统一协调与标准化处理。数据集由Electric Sheep Europe团队重新打包，以Parquet格式发布，确保机器学习就绪。最终收录了来自34个欧洲国家、横跨1998年至2025年的28,484条观测记录，覆盖1个独特指标。

特点

该数据集的核心特点在于其精细的维度拆分与高覆盖率。所包含的“时间相关就业不足与失业综合率（LU2）”指标，按性别（总、男、女）、教育水平以及城乡区域进行交叉分类，便于深入分析劳动力未被充分利用的多元结构。数据覆盖34个欧洲国家，时间跨度长达27年，为纵向比较与面板分析提供了坚实支撑。数据集同时保留了完整的数据溯源信息，如来源调查标识、观测状态（如临时的、不可靠的）及系列断裂等注释，极大增强了数据的可解释性与可信度。

使用方法

数据集的使用极为便捷，推荐通过HuggingFace的`datasets`库加载，仅需执行`load_dataset("electricsheepeurope/europe-ilo-luu-xlu2-sex-edu-geo-rt-combined-rate-of-time-related-underemployment-and")`即可获得可直接转换为Pandas DataFrame的`train`分割。研究者可通过`ref_area`列轻松筛选特定国家，按`indicator`列过滤目标指标后，利用`time`列构建时间序列并绘图。亦可运用`pivot_table`方法将数据重塑为国家×年份的矩阵形式，便于进行跨国的面板数据分析。

背景与挑战

背景概述

劳动市场的健康程度不仅取决于失业率，还需关注更广泛的劳动利用不足现象。时间相关就业不足与失业的综合比率（LU2）由国际劳工组织（ILO）于2025年通过其ILOSTAT数据库发布，旨在更全面地刻画劳动力资源未能充分利用的状况。该数据集由Electric Sheep Europe重新封装，涵盖34个欧洲国家1998至2025年间28,484条观测记录，并按性别、教育水平和城乡地域进行分层。作为标准化、跨国家可比的统计资源，它为劳动经济学、社会政策与可持续发展目标（SDGs）的实证研究提供了关键数据支撑，推动了从单一失业率向多维劳动利用不足指标的研究范式转变，对欧洲区域劳动力市场分析具有重要影响力。

当前挑战

该数据集及其所属领域面临多重挑战。领域层面，劳动利用不足的测度需超越传统失业统计，纳入时间相关就业不足等隐蔽性指标，但不同国家间的调查口径与数据质量差异显著，ILO虽通过ICLS定义进行协调，仍难以完全消除跨国比较中的系统性偏差。构建过程中，数据来源涵盖多类型调查和管理记录，同一国家同一时期可能存在多个冲突的统计来源，需依赖ILO的“最佳来源”选择策略，这引入了主观判断的不确定性。此外，部分观测值标记为“不可靠”或“暂定”，且教育分类存在非标准级别注释，增加了数据清洗与模型训练的复杂度，对时间序列分析和跨年份推论的稳健性构成挑战。

常用场景

经典使用场景

该数据集收录了1998年至2025年间34个欧洲国家的28,484条观测记录，核心指标为时间相关就业不足与失业的综合率（LU2），并按性别、教育水平和城乡区域进行细致分层。在劳动经济学与社会政策研究领域，研究者常利用该数据集构建面板数据模型，追踪不同人口亚群在劳动力市场中的边缘化程度。其经典使用场景包括评估经济周期对特定性别或教育群体的非充分就业冲击，以及比较各国在劳动力资源利用效率上的结构性差异。数据集规范的时序结构和丰富的分类维度，使其成为分析欧洲劳动力市场韧性与不平等动态的基础性资源。

解决学术问题

学术研究中，该数据集直击传统失业率指标的局限性，解决了仅以失业定义衡量劳动力闲置所导致的系统性低估问题。通过纳入时间相关就业不足这一维度，LU2指标能够更全面地刻画隐性劳动力闲置现象，尤其适用于研究女性、低教育水平者或农村居民等脆弱群体的就业质量。该数据集使学者得以深入探讨性别工资差距、教育回报率递减以及城乡就业机会分化等议题，推动了关于劳动力市场包容性与可持续发展目标（SDG）第8项指标量化评估的实证研究。其在方法论上的贡献在于提供了经过国际劳工组织（ILO）依据ICLS标准协调的可比数据，为跨国比较分析奠定了科学基础。

衍生相关工作

基于该数据集衍生出一系列卓有成效的学术工作，拓展了劳动力市场分析的前沿领域。典型工作包括运用机器学习方法构建欧洲各国LU2率的预测模型，结合宏观经济变量预测就业不足趋势；亦有研究通过聚类分析识别劳动力市场形态相似的国家群组，探索制度环境与劳动力闲置模式之间的关联。在经典计量经济学中，学者利用该数据构建多层次混合效应模型，分解个人、区域与国家层面因素对就业不足的贡献度。此外，该数据集与ILOSTAT其他指标（如时薪、非正规就业率）的联合使用，催生了关于劳动力利用效率综合评价体系的构建研究，为跨指标融合分析提供了范例。