electricsheepeurope/europe-ilo-luu-xlu3-sex-age-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: - 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 rural / urb | Europe (ILOSTAT)" --- # Combined rate of unemployment and potential labour force (LU3) by sex, age and rural / urb | Europe (ILOSTAT) 🇪🇺 **53,287 observations** · **36 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **53,287 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_AGE_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_AGE_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` | 2,756 | 1987 | 2025 | | `AUT` | 2,014 | 1998 | 2025 | | `FIN` | 2,007 | 1998 | 2024 | | `DEU` | 1,935 | 1998 | 2024 | | `DNK` | 1,890 | 1998 | 2024 | | `NLD` | 1,881 | 1998 | 2024 | | `PRT` | 1,879 | 1998 | 2024 | | `ESP` | 1,878 | 1998 | 2024 | | `FRA` | 1,874 | 1998 | 2024 | | `EST` | 1,776 | 1998 | 2024 | | `LUX` | 1,751 | 1998 | 2024 | | `BEL` | 1,747 | 1998 | 2024 | | `HUN` | 1,728 | 2001 | 2024 | | `GBR` | 1,723 | 1998 | 2019 | | `LVA` | 1,699 | 2001 | 2024 | | ... | _21 more countries_ | | | ## Indicators (sample) - `LUU_XLU3_SEX_AGE_GEO_RT` — Combined rate of unemployment and potential labour force (LU3) by sex, age 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_AGE_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.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) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-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_AGE_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU3_SEX_AGE_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU3_SEX_AGE_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu3_sex_age_geo_rt_combined_rate_of_unemployment_and_potential_labour_2025, title = {Combined rate of unemployment and potential labour force (LU3) by sex, age and rural / urb | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU3_SEX_AGE_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu3-sex-age-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_AGE_GEO_RT_This dataset is a tabular dataset on labour underutilization in Europe, focusing on the Combined rate of unemployment and potential labour force (LU3) indicator. It contains 53,287 observations across 36 European countries from 1987 to 2025. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via its REST API and filtered to European countries. The dataset provides detailed disaggregation by sex, age, and rural/urban areas, with columns including country code, country name, data source, indicator code, sex classification, age classification, area type, observation year, observed value, observation status, etc. Data is annual in frequency; when multiple sources exist for the same country×year, the ILO-selected best source is used, and disaggregation columns are non-null only when the indicator publishes that breakdown. The dataset is suitable for tasks such as tabular classification, regression, and time-series forecasting, useful for analyzing trends and disparities in the European labour market.

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，通过调用其REST API直接获取指标LUU_XLU3_SEX_AGE_GEO_RT的原始数据，并依据欧洲ISO3国家代码进行过滤，最终整合为涵盖36个欧洲国家、时间跨度从1987年至2025年的53287条观测记录。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国调查微观数据进行统一协调，并在source.label字段中标注数据来源以确保可追溯性。数据集由Electric Sheep Europe重新打包，以标准化格式发布在HuggingFace平台上。

特点

数据集聚焦于失业与潜在劳动力综合比率（LU3）这一关键劳动力利用不足衡量指标，按性别、年龄及城乡地区进行精细分解，提供多维度的分类变量（如sex、classif1、classif2）以满足差异化分析需求。数据覆盖了36个欧洲国家的长期序列，其中希腊、奥地利、芬兰等国的观测数量最为丰富，且所有记录均附有观测状态标签和数据质量注释，如数据可靠性标志与序列中断说明。数据集以年度频率呈现，并优先采用ILO选定的最佳来源，确保了跨国家、跨时间的一致性。

使用方法

用户可通过HuggingFace的datasets库轻松加载数据，只需调用load_dataset函数即可将数据转换为Pandas DataFrame，随后支持按国家代码筛选（如df[df['ref_area']=='DEU']）、按指标排序并绘制时间序列图，或使用pivot_table构建国家×年份的透视矩阵进行跨区域对比分析。数据集的列定义清晰，包括指示器编码、观测值、分类维度及数据来源标签，便于研究人员直接用于时间序列预测、面板数据分析或机器学习建模，而无需繁琐的数据清洗预处理。

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团队重新打包至HuggingFace平台，旨在提供欧洲36国1987至2025年间劳动力利用不足的综合测量指标。核心研究问题聚焦于失业与潜在劳动力（LU3）的联合率，按性别、年龄及城乡地域进行细分，以揭示传统失业率之外更广泛的劳动力闲置状况。作为全球劳动统计的权威来源，ILOSTAT的数据通过对各国劳动力调查、家庭收支调查等原始微观数据的统一标准化处理，构建了跨时空可比的时间序列。该数据集凭借其覆盖范围广、时间跨度长及细粒度分类维度，为劳动经济学、区域发展研究及国际政策制定提供了关键实证基础，对理解欧洲劳动力市场的结构性变化与不平等格局具有显著影响力。

当前挑战

在领域问题层面，该数据集应对的核心挑战在于：传统失业率指标难以捕捉因就业信心不足而退出劳动力市场的“潜在劳动力”群体，导致对劳动力利用不足全貌的误判；LU3联合率指标的引入虽弥补了此缺陷，但其跨国可比性受制于各国调查方法、定义标准（如ICLS会议准则演变）及数据质量差异，需通过ILO的筛选与标准化流程实现协同。在构建过程中，数据整合面临多重障碍：首先，原始数据源来自36国各异的调查系统，存在年代缺失、分类编码不统一及元数据追踪复杂等问题；其次，年度频率与部分国家季度/月度数据的混合要求谨慎处理时间颗粒度差异；最后，指标在多源冲突时需依赖ILO的“最佳来源”选择规则，而断点序列（如方法论修订）需通过注释字段显式标记，以确保时间序列分析的严谨性与可追溯性。

常用场景

经典使用场景

该数据集汇聚了国际劳工组织ILOSTAT数据库中关于欧洲36国失业与潜在劳动力综合率（LU3）的年度观测记录，时间跨度从1987年至2025年，共计53,287条样本。经典的使用方式在于将其作为时间序列回归与面板数据分析的基石，研究者可借助该数据集追踪不同性别、年龄组及城乡地域的劳动力利用不足程度，进而构建动态预测模型。其结构化的分类变量（如性别与地域）使得分层比较与多维度混合效应建模成为可能，为欧洲劳动力市场的结构性特征提供了可靠的量化标尺。

实际应用

实际应用中，该数据集被广泛用于欧洲各国劳动部门的经济监测与政策模拟。决策者可以依托其中的性别与年龄细分数据，识别出特定人群（如青年、女性或农村居民）在就业市场上的脆弱性，从而设计精准的调控措施，比如定向职业培训或区域就业补贴。此外，国际组织（如欧盟统计局）在发布《就业与社会发展报告》时，常引用该数据构建综合性劳动闲置指标，用以衡量不同经济周期下劳动力市场的吸纳弹性。金融机构与投资分析公司亦将其融入宏观风险模型，通过对LU3变动趋势的追踪，前瞻性地捕捉劳动力供给瓶颈可能引发的工资通胀压力。

衍生相关工作

围绕该数据集，学术社区已孕育出一系列经典衍生工作。一方面，研究者基于该数据构建了欧洲劳动力利用不足的长期面板数据库，并以此为依据开展收敛性分析，探究欧元区国家之间劳动市场失衡的弥合趋势。另一方面，该数据被用于训练机器学习模型以预测潜在劳动力比率，代表性的工作包括利用XGBoost与LSTM网络结合性别—地域分层特征进行短期预测，其结果显著优于传统的ARIMA基准模型。此外，该数据集的规范化结构与时间序列属性，启发了一系列关于数据插补与统计匹配的方法论研究，特别是在处理标注为“不可靠”或“断点”的观测值时，学者发展了基于状态空间模型的重估计技术，提升了历史序列的可用性与一致性。