electricsheepeurope/europe-ilo-luu-xlu3-sex-edu-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 and education (%) | Europe (ILOSTAT)" --- # Combined rate of unemployment and potential labour force (LU3) by sex and education (%) | Europe (ILOSTAT) 🇪🇺 **29,826 observations** · **38 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **29,826 observations** of `Other measures of labour underutilization` data across **38 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_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_XLU3_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 38 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 1,683 | 1987 | 2025 | | `GBR` | 1,094 | 1999 | 2025 | | `FRA` | 1,024 | 1998 | 2024 | | `NLD` | 992 | 1998 | 2024 | | `IRL` | 943 | 2004 | 2024 | | `PRT` | 937 | 1998 | 2025 | | `ESP` | 902 | 1998 | 2025 | | `BEL` | 883 | 1998 | 2024 | | `ITA` | 872 | 2002 | 2024 | | `DEU` | 869 | 1999 | 2024 | | `CZE` | 861 | 2002 | 2024 | | `AUT` | 860 | 1998 | 2025 | | `SRB` | 857 | 2007 | 2025 | | `DNK` | 844 | 1998 | 2024 | | `MKD` | 838 | 2005 | 2025 | | ... | _23 more countries_ | | | ## Indicators (sample) - `LUU_XLU3_SEX_EDU_RT` — Combined rate of unemployment and potential labour force (LU3) 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_XLU3_SEX_EDU_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.) | `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) | `10.196` | | `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-xlu3-sex-edu-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_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU3_SEX_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU3_SEX_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu3_sex_edu_rt_combined_rate_of_unemployment_and_potential_labour_2025, title = {Combined rate of unemployment and potential labour force (LU3) by sex and education (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU3_SEX_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu3-sex-edu-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_EDU_RT_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically the Combined rate of unemployment and potential labour force (LU3) by sex and education (%). It covers 38 European countries from 1987 to 2025, with 29,826 observations. Data is pulled from the ILOSTAT REST API and filtered to Europe ISO3 country codes. The dataset includes columns such as country code (ref_area), country name (ref_area.label), source code (source), source name (source.label), indicator code (indicator), indicator name (indicator.label), sex disaggregation (sex), sex label (sex.label), education classification (classif1), education label (classif1.label), year (time), observed value (obs_value), observation status (obs_status), and more. Data is provided at annual frequency and includes disaggregation by sex (total, male, female). The dataset is suitable for tabular classification, tabular regression, and time-series forecasting tasks, and can be used 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数据库，聚焦于欧洲地区失业与潜在劳动力综合比率（LU3）的观测值。通过ILOSTAT REST API直接拉取指标编码为LUU_XLU3_SEX_EDU_RT的原始数据，并依据欧洲ISO3国家代码进行地域过滤，最终整合了38个欧洲国家自1987年至2025年间的29,826条年度观测记录。ILOSTAT依据国际劳工统计学家会议（ICLS）的定义对各国劳动力调查微观数据进行统一协调，并在数据集中以source.label字段标注数据来源，确保其可追溯性。

使用方法

研究人员可通过HuggingFace的datasets库直接调用load_dataset()函数实现数据加载，并轻松转换为Pandas DataFrame进行后续分析。典型使用场景包括：按国家代码筛选特定国家的时序数据，对单一指标按年份排序绘制时间序列曲线，或利用pivot_table将数据重塑为国家×年份的矩阵格式，便于面板数据回归与跨国家横向对比。数据集支持表格分类、回归以及时间序列预测等任务，适合劳动经济学与政策评估领域的实证研究。

背景与挑战

背景概述

在劳动经济学与劳动力市场研究领域，准确衡量劳动力闲置程度对于制定有效的就业政策至关重要。传统的失业率指标往往无法全面反映劳动力市场的疲软状况，因此国际劳工组织（ILO）提出了包括LU3在内的多项劳动力利用不足指标。该数据集由Electric Sheep Europe于2025年重新整理发布，基于ILO旗下ILOSTAT数据库的官方数据，聚焦欧洲38个国家1987年至2025年间按性别和教育程度划分的失业与潜在劳动力综合率（LU3）。该数据集为宏观经济学、劳动社会学及政策评估领域提供了跨时长达38年的高粒度面板数据，成为研究欧洲劳动力市场结构性变化、教育回报差异以及性别就业不平等问题的重要基准资源，有力推动了基于证据的政策模拟与跨国比较研究。

当前挑战

该数据集所解决的领域问题在于，传统失业率指标仅覆盖主动寻找工作的失业者，而忽略了因各种原因退出劳动力市场或处于潜在劳动力状态的群体，LU3作为综合指标能更真实地反映劳动力闲置全貌，但其构建过程中面临多重挑战。首先，数据来源涵盖各国劳动力调查与行政记录，不同国家在调查设计、样本框架和定义口径上存在差异，ILO虽通过ICLS标准进行协调，但数据可比性仍受限于各国统计能力与自愿性报告偏差。其次，时间序列中存在因方法论修订导致的断点问题，如“Break in series: Methodology revised”标记的出现，需要研究者审慎处理结构性突变。此外，数据按年发布，缺乏对短期经济波动（如季节性失业或突发事件冲击）的敏感捕捉，限制了高频政策分析的应用场景。

常用场景

经典使用场景

该数据集收录了1987至2025年间38个欧洲国家按性别与教育程度划分的综合失业与潜在劳动力比率（LU3），共计29826条观测记录，为劳动力市场研究提供了跨时空、多维度的标准化数据基础。研究者可借助该数据集构建时间序列模型，追踪不同性别与教育群体在劳动力闲置程度上的长期演变趋势，亦可通过面板数据分析各国劳动力利用率的异质性及其与宏观经济周期的关联。

解决学术问题

数据集解决了劳动力统计中因各国调查口径差异导致的数据可比性缺失问题，ILOSTAT基于国际劳动统计学家会议（ICLS）标准对原始调查微观数据进行统一协调，使跨国家与跨时期的劳动力闲置量化成为可能。学术界得以深入探讨教育水平对劳动力供给行为的分异效应、性别不平等在劳动力市场中的动态表征，以及潜在劳动力群体的结构性特征，从而为劳动力利用不足的理论模型提供经验验证。

实际应用

在实际应用中，数据集能够支撑国际组织与各国劳动部门进行劳动力市场监测与政策评估，例如通过LU3指标识别未被传统失业统计捕捉的劳动闲置人口，为就业促进计划的靶向设计提供依据。研究人员还可结合其他社会经济变量，构建劳动力闲置预警系统，辅助制定针对特定性别或教育群体的精准干预措施，推动体面劳动目标的实现。

数据集最近研究

最新研究方向

当前，该数据集主要服务于欧洲劳动力市场弱势群体识别与政策评估的前沿研究，聚焦于结合性别与教育维度解构失业与潜在劳动力复合率（LU3）。研究者利用ILOSTAT统一方法论处理1987至2025年38国的横截面与时间序列数据，探讨教育水平对男女劳动力闲置状态的异质性影响。这一方向与欧盟近年倡导的‘包容性增长’及‘终身学习’战略紧密相连，尤其在后疫情时代劳动力复苏背景下，数据集为分析结构性失业、技能错配及边缘群体融入劳动市场提供了跨时空的量化基础。其引入的标准化分类与质量标记（如数据来源、异常值标注）显著提升了跨国比较研究的可靠性与可复现性，对推动以证据为基础的社会政策制定具有重要价值。