electricsheepeurope/europe-ilo-luu-xlu3-sex-geo-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: - 1K<n<10K 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 and rural / urban ar | Europe (ILOSTAT)" --- # Combined rate of unemployment and potential labour force (LU3) by sex and rural / urban ar | Europe (ILOSTAT) 🇪🇺 **6,721 observations** · **36 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **6,721 observations** of `Other measures of labour underutilization` data across **36 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_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_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 36 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 351 | 1987 | 2025 | | `FIN` | 258 | 1998 | 2024 | | `AUT` | 252 | 1998 | 2025 | | `BEL` | 243 | 1998 | 2024 | | `PRT` | 243 | 1998 | 2024 | | `FRA` | 243 | 1998 | 2024 | | `DNK` | 243 | 1998 | 2024 | | `ESP` | 243 | 1998 | 2024 | | `DEU` | 243 | 1998 | 2024 | | `LUX` | 243 | 1998 | 2024 | | `NLD` | 243 | 1998 | 2024 | | `MDA` | 233 | 2000 | 2025 | | `GBR` | 222 | 1998 | 2019 | | `EST` | 222 | 1998 | 2024 | | `HUN` | 216 | 2001 | 2024 | | ... | _21 more countries_ | | | ## Indicators (sample) - `LUU_XLU3_SEX_GEO_RT` — Combined rate of unemployment and potential labour force (LU3) by sex and rural / urban areas (%) ## 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_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` | | `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-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_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU3_SEX_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU3_SEX_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu3_sex_geo_rt_combined_rate_of_unemployment_and_potential_labour_2025, title = {Combined rate of unemployment and potential labour force (LU3) by sex and rural / urban ar | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU3_SEX_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu3-sex-geo-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_RT_This dataset contains combined rate of unemployment and potential labour force (LU3) by sex and rural/urban areas for 36 European countries from 1987 to 2025, with 6,721 observations. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to include only European countries. It is organized in tabular format with columns such as country code, country name, data source, indicator code, indicator name, sex classification, rural/urban classification, observation year, observed value, and observation status. The dataset is suitable for labor market analysis, economic forecasting, policy evaluation, and supports machine learning tasks like classification, regression, and time-series forecasting. Repackaged by Electric Sheep Europe with a standardized schema for easy loading using Hugging Faces `load_dataset()` function.

搜集汇总

数据集介绍

构建方式

该数据集由国际劳工组织（ILO）的ILOSTAT数据库整理而成，经由Electric Sheep Europe重新封装并发布至HuggingFace平台。构建过程中，数据直接从ILOSTAT的REST API接口（https://rplumber.ilo.org/data/indicator?id=LUU_XLU3_SEX_GEO_RT）拉取，随后按照欧洲ISO3国家代码进行地理范围过滤，仅保留36个欧洲国家的观测记录。ILOSTAT依据国际劳工统计学家会议（ICLS）的定义对原始调查微观数据进行统一协调，并通过`source.label`字段标注数据来源，确保数据具有可追溯性。最终数据集包含6,721条观测，时间跨度覆盖1987年至2025年。

特点

该数据集聚焦于劳动力利用不足的补充度量指标——失业与潜在劳动力综合比率（LU3），并按性别（男性、女性、总计）和城乡区域进行细致划分。数据涵盖36个欧洲国家，时间维度长达近四十年，为长期劳动市场趋势分析提供了宝贵资源。每个观测包含丰富的元数据字段，如来源、指标定义、观测状态及注释，有助于用户理解数据质量和潜在断点。此外，数据集采用CC-BY-4.0许可协议，便于学术与商业应用。

使用方法

用户可通过HuggingFace的`datasets`库便捷加载：`load_dataset("electricsheepeurope/europe-ilo-luu-xlu3-sex-geo-rt-combined-rate-of-unemployment-and-potential-labour")`，返回的数据可直接转换为Pandas DataFrame进行后续分析。典型用法包括按国家筛选（如`df[df["ref_area"] == "DEU"]`）、按指标绘制时间序列（如使用`obs_value`与`time`字段），或通过透视表构建国家×年份的矩阵。该数据集适用于分类、回归及时间序列预测等任务，为欧洲劳动力市场研究提供了标准化的机器学习就绪数据格式。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年通过其ILOSTAT数据库整理发布，并由Electric Sheep Europe重新打包以适配机器学习工作流。其核心研究问题聚焦于欧洲36个国家在1987至2025年间，基于性别与城乡地域划分的失业与潜在劳动力综合率（LU3）的测量与对比。作为全球劳动统计领域的权威来源，ILOSTAT通过整合各国劳动力调查与行政记录数据，为评估劳动力未充分利用程度提供了标准化参考。该数据集在劳动经济学、社会政策研究及可持续发展目标（SDG）监测中具有重要影响力，尤其为分析欧洲区域劳动力市场结构性变化与性别、地域差异提供了精细化的时序数据基础。

当前挑战

该数据集所解决的领域问题在于，传统的失业率指标常低估劳动力市场的真实闲置程度，而LU3指标综合了失业与潜在劳动力人群，能更全面反映劳动力未充分利用的复杂性。构建过程中面临的挑战包括：跨国数据源的质量与口径差异，需依赖ILO对调查微观数据的标准化处理与国际劳工统计学家会议（ICLS）定义的统一；时序数据存在断点与修订标记，需通过obs_status与note_indicator等字段对方法变更加以追溯；此外，城乡分类、性别细分等维度存在大量缺失值，且不同国家的统计周期与数据覆盖年份不一，增加了跨区域可比分析与模型构建的难度。

常用场景

经典使用场景

该数据集汇集了欧洲36个国家自1987年至2025年间劳动力利用不足的综合指标，具体聚焦于失业与潜在劳动力联合比率（LU3），并按照性别及城乡地域进行细致划分。研究者常将其作为时间序列数据，用于分析欧洲各国劳动力市场的结构性变迁，或构建面板数据模型探究劳动力供给与社会经济因素间的动态关联。数据集丰富的维度设置也使其成为分类与回归任务的理想训练素材，可服务于劳动力状态的预测模型校准。

实际应用

在实际应用中，该数据集为欧盟及各国劳动部门监测劳动力市场失衡、制定精准就业促进策略提供了量化依据。政策制定者可依据LU3指标的时空差异，识别出亟需干预的地区和人群，优化职业培训与就业服务资源的配置。同时，该数据亦被国际组织用于跨国劳动力水平对比，支撑可持续就业目标的进展评估，并为企业人力资源规划与区域经济投资决策提供宏观参考。

衍生相关工作

该数据集的发布催生了一系列衍生研究，包括基于长短期记忆网络的欧洲失业率预测模型、融合城乡差异的劳动力供给弹性分析，以及利用面板回归探讨移民政策对本土劳动力利用率影响的实证工作。此外，数据标准化与便捷的加载格式激励了机器学习社区开发可复用的基准测试方法，进一步丰富了劳动力经济学的计算实验范式，并推动了开放科学与跨机构数据协作的实践。