electricsheepeurope/europe-ilo-luu-xlu3-sex-age-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, age and education ( | Europe (ILOSTAT)" --- # Combined rate of unemployment and potential labour force (LU3) by sex, age and education ( | Europe (ILOSTAT) 🇪🇺 **91,929 observations** · **38 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **91,929 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_AGE_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_AGE_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` | 4,731 | 1987 | 2025 | | `GBR` | 3,575 | 1999 | 2025 | | `FRA` | 3,253 | 1998 | 2024 | | `NLD` | 3,235 | 1998 | 2024 | | `PRT` | 3,053 | 1998 | 2025 | | `ESP` | 2,941 | 1998 | 2025 | | `DEU` | 2,850 | 1999 | 2024 | | `DNK` | 2,844 | 1998 | 2024 | | `HUN` | 2,777 | 1998 | 2024 | | `BEL` | 2,752 | 1998 | 2024 | | `NOR` | 2,723 | 1998 | 2024 | | `AUT` | 2,713 | 1998 | 2025 | | `ITA` | 2,661 | 2002 | 2024 | | `IRL` | 2,600 | 2004 | 2024 | | `BGR` | 2,597 | 2000 | 2024 | | ... | _23 more countries_ | | | ## Indicators (sample) - `LUU_XLU3_SEX_AGE_EDU_RT` — Combined rate of unemployment and potential labour force (LU3) by sex, age 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_AGE_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `EDU_AGGREGATE_TOTAL` | | `classif2.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-age-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_AGE_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU3_SEX_AGE_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU3_SEX_AGE_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu3_sex_age_edu_rt_combined_rate_of_unemployment_and_potential_labour_2025, title = {Combined rate of unemployment and potential labour force (LU3) by sex, age and education ( | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU3_SEX_AGE_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu3-sex-age-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_AGE_EDU_RT_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically focusing on the Combined rate of unemployment and potential labour force (LU3) by sex, age and education. It covers 38 European countries from 1987 to 2025, with 91,929 observations and one core indicator: LUU_XLU3_SEX_AGE_EDU_RT (expressed as a percentage). The data is retrieved via the ILOSTAT REST API and harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset includes columns such as country code, country name, data source (e.g., labour force survey), indicator code and label, sex disaggregation (total, male, female), age and education classification variables, observation year, observed value, and status flags. The data is annual frequency and suitable for tabular classification, regression, and time-series forecasting tasks. Repackaged by Electric Sheep Europe, it aims to provide a unified, ML-ready data layer for Europe, enabling researchers and developers to quickly utilize the data.

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，专注于收集欧洲地区劳动力未被充分利用的测度指标。通过调用ILOSTAT REST API，项目团队将原始指标数据筛选至欧洲范围，并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标准化处理，最终整合为包含91,929条观测记录的整洁表格。每条记录涵盖了38个欧洲国家自1987年至2025年间的年度数据，并保留了来源标识以保障可追溯性。

特点

本数据集的核心特色在于其细致的人口特征分解维度，能够按性别、年龄及教育水平分层展示‘失业与潜在劳动力综合比率（LU3）’这一关键指标。数据集中包含丰富的分类变量，如‘性别’、‘第一分类’与‘第二分类’，支持用户深入分析不同社会群体间的劳动参与差异。此外，数据集注明了观测状态（如是否可靠）及方法学变更说明，为评估数据质量提供了透明依据。

使用方法

借助HuggingFace的datasets库，用户可通过一行代码快速加载数据集并转换为Pandas DataFrame进行分析。典型用法包括按国别筛选特定国家的时间序列数据、按指标排序以观察年度趋势，或利用透视表功能构建国家-年份矩阵以进行跨国比较。该数据集支持表格分类、回归及时间序列预测等多种机器学习任务，适应从描述性统计到预测建模的多元应用场景。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门于2025年整理发布，经Electric Sheep Europe重新封装后，在HuggingFace平台上以开放许可形式共享。它聚焦于欧洲38个国家在1987至2025年间劳动利用不足的复合指标，具体涵盖失业与潜在劳动力（LU3）按性别、年龄及教育层次的细分比率。数据集的核心研究问题在于揭示劳动力市场中隐性失业与边缘就业的复杂结构，超越传统失业率的单一维度，为深层次劳动市场分析提供多维度的数据支撑。凭借高时间跨度与丰富的分类变量，该数据集对劳动经济学、社会政策评估及欧洲一体化背景下的劳动力流动研究产生深远影响，成为探索劳动市场韧性与结构性变化的重要数据资源。

当前挑战

该数据集面临的核心挑战在于如何准确量化并区分劳动利用不足的多种形态，如传统失业、隐性失业及潜在劳动力之间模糊边界，这要求指标设计高度精细化。构建过程中，ILO需从各国劳动力调查、家庭收入调查等异质性来源中统筹数据，整合过程面临跨国统计口径差异、时间序列断裂及方法论修订等困难。此外，性别、年龄及教育等分类维度的数据稀疏性，以及部分年份观测值的不可靠性，给模型训练带来偏差风险。跨来源的数据一致性与质量标注亦构成显著难点，使得时间序列预测和跨区域比较分析均需审慎处理数据可信度问题。

常用场景

经典使用场景

在劳动经济学与政策分析的学术脉络中，europe-ilo-luu-xlu3-sex-age-edu-rt-combined-rate-of-unemployment-and-potential-labour 数据集常被用于构建多维度劳动力利用不足的计量模型。其跨越38个欧洲国家、近四十年的年度观测值，搭配性别、年龄与教育程度的精细分层，使得研究者能够通过面板数据回归或时间序列分解方法，精准刻画失业与潜在劳动力联合比率（LU3）的演变轨迹。该数据集尤其适合探究经济周期、制度变迁或人口结构转型对劳动力市场韧性的影响，成为比较劳动经济学研究中不可或缺的实证基石。

解决学术问题

该数据集有效回应了传统失业率指标仅关注狭义的失业群体而忽视潜在劳动力的学术困境。通过整合LU3这一综合指标，它解决了如何量化边缘劳动参与、隐性失业以及技能错配等复杂劳动力市场现象的问题。研究得以突破单一失业率的局限，深入剖析不同性别、年龄段及教育层次人群在岗位需求疲软时期的异质性反应。这种多维度的劳动供给行为刻画，不仅增强了劳动力市场效率评估的精度，也为完善劳动力闲置理论框架提供了数据支撑，推动了劳动经济学实证研究方法的革新。

衍生相关工作

该数据集衍生出一系列具有影响力的学术工作，尤其在劳动剥夺感和非标准就业形式的研究领域。经典成果包括基于该数据构建的LU3预测模型，这些模型运用机器学习方法（如随机森林或长短期记忆网络）捕捉非线性趋势，提前预警潜在劳动力过剩的风险。另有研究以此数据为基础，开发了劳动力市场韧性的综合指数，将失业率与潜在劳动参与率降维融合，用于评估国家层面的应对能力。此外，性别与教育交叉分析还催生了关于‘隐性就业歧视’的量化研究，揭示了即使在稳定的宏观经济环境下，特定亚群体仍面临持续的就业脆弱性。