electricsheepeurope/europe-ilo-luu-xlu3-sex-geo-mts-rt-combined-rate-of-unemployment-and-potential-labour

--- 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 unemployment and potential labour force (LU3) by sex, rural / urban area | Europe (ILOSTAT)" --- # Combined rate of unemployment and potential labour force (LU3) by sex, rural / urban area | Europe (ILOSTAT) 🇪🇺 **17,731 observations** · **35 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **17,731 observations** of `Other measures of labour underutilization` data across **35 Europe countries**, spanning **1987–2025**, 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_XLU3_SEX_GEO_MTS_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_XLU3_SEX_GEO_MTS_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 35 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 918 | 1987 | 2020 | | `AUT` | 756 | 1998 | 2025 | | `FIN` | 704 | 1998 | 2020 | | `MDA` | 697 | 2000 | 2025 | | `GBR` | 662 | 1998 | 2019 | | `LUX` | 623 | 1998 | 2020 | | `BEL` | 621 | 1998 | 2020 | | `NLD` | 621 | 1998 | 2020 | | `ESP` | 621 | 1998 | 2020 | | `DEU` | 621 | 1998 | 2020 | | `DNK` | 621 | 1998 | 2020 | | `SWE` | 621 | 1999 | 2020 | | `ITA` | 621 | 1998 | 2020 | | `PRT` | 621 | 1998 | 2020 | | `FRA` | 587 | 2005 | 2024 | | ... | _20 more countries_ | | | ## Indicators (sample) - `LUU_XLU3_SEX_GEO_MTS_RT` — Combined rate of unemployment and potential labour force (LU3) by sex, rural / urban area and marital status (%) ## 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) | `BB:7401` | | `source.label` | `string` | Source name in English | `HIES - Living Standards Survey` | | `indicator` | `string` | ILOSTAT indicator code | `LUU_XLU3_SEX_GEO_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Combined rate of unemployment and pot…` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2012` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `34.187` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-xlu3-sex-geo-mts-rt-combined-rate-of-unemployment-and-potential-labour") 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_XLU3_SEX_GEO_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU3_SEX_GEO_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU3_SEX_GEO_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu3_sex_geo_mts_rt_combined_rate_of_unemployment_and_potential_labour_2025, title = {Combined rate of unemployment and potential labour force (LU3) by sex, rural / urban area | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU3_SEX_GEO_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu3-sex-geo-mts-rt-combined-rate-of-unemployment-and-potential-labour}} } ``` ## 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_XLU3_SEX_GEO_MTS_RT_This dataset contains 17,731 observations of the combined rate of unemployment and potential labour force (LU3) across 35 European countries from 1987 to 2025. Sourced from the International Labour Organizations (ILO) ILOSTAT database, the topic is Other measures of labour underutilization. The specific indicator is Combined rate of unemployment and potential labour force (LU3) by sex, rural/urban area and marital status (%). Data is disaggregated by country, year, sex (total, male, female), rural/urban classification, and other dimensions, with annual frequency, and includes source information, observation status, and notes. Repackaged by Electric Sheep Europe, it is suitable for tasks such as tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

本数据集源自国际劳工组织（ILO）的ILOSTAT核心统计数据库，经由Electric Sheep Europe团队从ILO官方REST API直接抽取并重新封装而成。原始数据以国际劳工统计学家会议（ICLS）定义为准绳，对各国劳动力调查微观数据进行系统协调与标准化处理。数据涵盖1987年至2025年间35个欧洲国家的17,731条观测记录，聚焦于失业与潜在劳动力综合比率（LU3）这一关键指标，并按照性别、城乡地域及婚姻状况进行多维细分。所有观测值均附带数据来源标签，确保每一行数据的可追溯性与透明度。

特点

该数据集最为突出的特色在于其高度的结构一致性与细粒度维度设计。核心指标LUU_XLU3_SEX_GEO_MTS_RT提供了统一、可比的跨国时间序列，支持对欧洲劳动力市场闲置状况进行纵向与横向对照分析。数据模式中包含ref_area、sex、classif1、classif2等分类变量，允许研究者按国家、性别、地理覆盖类型及婚姻状态进行灵活的子集筛选与聚合运算。此外，obs_status与note_indicator等列详细标注了观测值的质量状态与方法论修订信息，为严谨的统计推断提供可信赖的数据基础。

使用方法

研究者可通过HuggingFace Datasets库一行代码完成数据加载，即刻获得可直接操作的Pandas DataFrame。典型用法包括：利用ref_area字段按国家代码筛选特定经济体的时间序列；针对单一指标按年份排序并绘制趋势图，观察劳动力闲置程度的演变轨迹；也可通过pivot_table将数据重塑为以年份为行、国家为列的矩阵形式，便于进行面板数据分析或构建统计模型。数据集中所有分类标签均以英文显式呈现，大幅降低了预处理门槛，使研究者能够将精力集中于洞察劳动力市场的深层结构。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）的ILOSTAT数据库整理而来，经Electric Sheep Europe于2025年重新打包并发布在HuggingFace平台，聚焦于欧洲地区劳动力利用不足的综合测量指标。核心研究问题在于量化失业与潜在劳动力（LU3）的综合比率，并按性别、城乡区域进行精细化解构，从而揭示劳动力市场的结构性差异。数据集涵盖1987至2025年间35个欧洲国家的17,731条观测值，为劳动经济学、区域发展以及国际劳动力比较研究提供了宝贵的时间序列资源。ILOSTAT作为全球劳动统计的权威来源，其数据来源于各国劳动力调查及行政记录，并经ILO统计部门统一标准化，保证了跨国的可比性和数据质量，对制定就业政策、监测可持续发展目标中的体面劳动指标具有重要影响力。

当前挑战

该数据集所解决的领域问题在于，传统的失业率指标无法全面反映劳动力市场的闲置状况，尤其忽略了那些虽未积极求职但有工作意愿的潜在劳动力。LU3综合率正是为此而生，但构建过程中面临多重挑战。其一，数据整合的复杂性：不同国家常采用多种调查数据源（如劳动力调查、生活水平调查），ILO虽筛选最佳来源，但序列断裂和方法论修订（如标注为'Break in series'）仍需谨慎处理。其二，高维度的分解维度（性别、地区类型、婚姻状况等）导致大量缺失值，例如特定指标可能不发布所有分类数据。其三，时间序列的长跨度（近40年）使得早期年份的数据覆盖不一致，部分国家起始年份较晚，影响面板数据分析的均衡性。

常用场景

经典使用场景

该数据集常用于欧洲劳动力市场的跨时空比较研究，特别是对失业与非充分就业综合指标的动态追踪。通过整合35个欧洲国家1987至2025年的LU3指标（失业与潜在劳动力综合比率），研究者可借助时间序列分析与面板回归模型，揭示性别、城乡区域及婚姻状况维度下劳动力利用不足的结构性特征。其年度观测值的连续性与标准化分类体系，为探讨经济周期、政策干预与劳动力市场弹性之间的关系提供了坚实的数据基础。

衍生相关工作

该数据集衍生了一系列关于劳动力非充分测量与预测的经典工作。例如，研究者基于其构建了欧洲劳动力市场弹性指数，用于比较不同国家应对经济冲击的恢复能力；另有工作将其与教育水平、行业结构等外部数据联合，训练了用于早期预警劳动力市场危机的机器学习模型。此外，结合ILOSTAT中其他劳动利用不足指标（如LU1、LU2）的跨数据集融合分析，催生了多维劳动力健康状况评估框架，成为国际比较研究中的基准资源。

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的隐性失业与潜在劳动力结合率（LU3）的时序分析与区域差异化研究。前沿方向包括利用时间序列预测模型（如Prophet、LSTM）对多国长期失业趋势进行建模，并结合性别、城乡等分类维度，揭示金融危机、新冠疫情及后疫情时代劳动力结构性变化。此外，研究者通过高分辨率面板数据支持欧洲各国比较劳动政策效果，尤其关注边缘劳动群体的受冲击程度，为欧洲就业与社会包容政策的精细化评估提供了数据基石，推动了劳动经济学与计算社会科学的交叉融合。