electricsheepeurope/europe-ilo-luu-xlu2-sex-edu-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 and education | Europe (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex and education | Europe (ILOSTAT) 🇪🇺 **27,555 observations** · **37 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **27,555 observations** of `Other measures of labour underutilization` data across **37 Europe countries**, spanning **1991–2025**, 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_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_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 37 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 1,114 | 1991 | 2025 | | `GBR` | 1,098 | 1999 | 2025 | | `FRA` | 1,024 | 1998 | 2024 | | `NLD` | 973 | 2000 | 2024 | | `PRT` | 944 | 1998 | 2025 | | `GRC` | 912 | 2006 | 2025 | | `ESP` | 902 | 1999 | 2025 | | `BEL` | 888 | 1999 | 2024 | | `SWE` | 873 | 2000 | 2024 | | `ITA` | 870 | 2002 | 2024 | | `AUT` | 861 | 1998 | 2025 | | `CZE` | 836 | 2002 | 2024 | | `DNK` | 825 | 2000 | 2024 | | `ROU` | 821 | 1999 | 2024 | | `HUN` | 812 | 1999 | 2024 | | ... | _22 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_EDU_RT` — Combined rate of time-related underemployment and unemployment (LU2) by sex and education (%) ## 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 | `LUU_XLU2_SEX_EDU_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` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `11.458` | | `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-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_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu2_sex_edu_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex and education | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu2-sex-edu-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_RT_This dataset contains labor market statistics from the International Labour Organization (ILO) ILOSTAT database, focusing on Other measures of labour underutilization, with the core indicator being Combined rate of time-related underemployment and unemployment (LU2) by sex and education. It covers 37 European countries from 1991 to 2025, comprising 27,555 observations. Data is sourced directly from the ILOSTAT REST API and filtered to European ISO3 country codes. The schema includes columns for country code, country name, data source, indicator code, indicator name, sex disaggregation (total, male, female), education classification, observation year, observed value, observation status flags, and related notes. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks, enabling analysis of combined underemployment and unemployment trends across European labor markets.

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，通过其REST API直接采集指标编码为LUU_XLU2_SEX_EDU_RT的原始数据，并依据欧洲ISO3国家代码进行地理过滤。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查微观数据进行统一协调处理，数据来源在source.label列中予以标注，确保了跨国的可比性与可追溯性。数据经系统性抽取与清洗后，以标准结构化表格形式封装，共包含27,555条观测记录，覆盖1991年至2025年的年度时间序列。

特点

本数据集最显著的特征在于其聚焦于劳动利用不足的复合度量——时间相关就业不足与失业的综合比率（LU2），且按性别与教育水平进行精细分层。其覆盖37个欧洲国家，时间跨度长达35年，提供了丰富的跨国家、跨时期比较视角。数据集中包含详尽的元数据列，如观测状态标志、系列中断注释以及来源标注，为用户提供了数据质量评估的透明度。此外，数据集采用CC-BY-4.0许可协议发布，便于学术研究与政策分析中的合法重用。

使用方法

数据集可通过HuggingFace Datasets库中的`load_dataset`函数便捷加载，返回的DataFrame可直接用于Python生态下的数据分析与建模。用户可依据国家代码（ref_area）过滤特定国家的子集，或针对单一指标按时间排序进行时序分析与可视化。利用pandas的pivot_table功能，可轻松将数据重塑为国家×年份的矩阵形式，便于进行面板数据分析或机器学习任务中的特征构造。该数据集原生支持表格分类、回归以及时间序列预测等多种任务场景。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于其ILOSTAT数据库中整理，经Electric Sheep Europe于2025年重新打包发布，聚焦于欧洲37个国家1991至2025年间的时间相关就业不足与失业综合率（LU2），并按性别与教育水平进行细致分层。作为衡量劳动力未被充分利用的关键指标，LU2比单一失业率更全面揭示劳动市场的隐性问题，尤其在欧洲经济结构转型和劳动力市场弹性变化的背景下，为研究者提供了理解就业质量与结构性矛盾的重要窗口。该数据集收录27,555条观测记录，经由ILO基于国际劳工统计学家会议（ICLS）定义对各国家庭调查与行政数据进行标准化整合，确保了跨国的可比性与权威性，对劳动经济学、社会政策分析及可持续发展目标（SDG）的监测产生深远影响。

当前挑战

该数据集所解决的领域问题在于，传统的失业率指标往往低估了劳动市场的真实压力，无法反映大量因工作时间不足而被迫非充分就业的群体，而LU2指标的引入能够更精准地捕捉这种隐性闲置。在构建过程中，数据集面临三重挑战：其一，各国劳动力调查在调查方法、问卷设计及数据采集频率上存在显著差异，ILO需通过复杂的统计调整实现数据的一致性；其二，时间跨度过长（1991至2025年）引入了统计制度变更与方法论修订，例如数据中的断点标记（Break in series）和来源的异质性，必须依赖详细的备注字段进行追溯；其三，数据标注存在不可靠值（obs_status为U），且部分观察值对应非标准教育分类，要求使用者在建模时谨慎处理缺失值与异常值的推断问题。

常用场景

经典使用场景

在劳动经济学与公共政策研究领域，该数据集被广泛用于分析欧洲各国劳动力未充分利用的综合状况。基于ILO国际劳工统计学家会议标准，研究者常利用此数据集探索时间相关就业不足与失业的联合比率（LU2），并考察其在不同性别与教育水平群体中的分布差异。借助跨37个国家、跨越三十余年的面板数据，学者能够稳健地估计劳动力市场结构性变化的长期趋势，进而为欧洲各国的就业政策评估与劳动力市场监测提供数据支撑。

衍生相关工作

基于此数据集，学术界涌现了一系列关于劳动力未充分利用指数（LU2）跨国面板分析的开创性工作。相关研究常将该数据与GDP增速、产业就业结构、社会保障支出等宏观变量联立，构建多层次回归模型以揭示经济发展水平与劳动力闲置程度的非线性关联。部分经典工作进一步利用该数据验证了受教育程度对性别就业差异的缓冲效应，推动了关于教育回报率与劳动力市场韧性的跨学科对话。这些衍生研究共同深化了人们对于欧洲劳动力市场异质性的理解。

数据集最近研究

最新研究方向

该数据集聚焦于欧洲劳动力市场非充分就业与失业的复合率（LU2），按性别与教育水平细分，覆盖1991至2025年37个欧洲国家，为研究后疫情时代劳动力结构性失衡、教育回报率差异及性别就业鸿沟等前沿议题提供了高颗粒度面板数据。当前热点方向包括利用时序预测模型（如Transformer、LSTM）刻画LU2率的短期波动与长期趋势，以及结合多源社会经济变量探究教育与性别在劳动力弹性中的交互效应。该数据集对推动欧洲劳动政策评估、精准锁定脆弱群体及实现体面劳动可持续发展目标（SDG 8）具有重要支撑意义，标准化格式亦便利了跨数据集的迁移学习与因果推断研究。