electricsheepeurope/europe-ilo-luu-xlu3-sex-age-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, age and marital sta | Europe (ILOSTAT)" --- # Combined rate of unemployment and potential labour force (LU3) by sex, age and marital sta | Europe (ILOSTAT) 🇪🇺 **62,472 observations** · **38 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **62,472 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_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_AGE_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 38 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 2,686 | 1991 | 2025 | | `GRC` | 2,404 | 1987 | 2020 | | `CZE` | 2,343 | 1993 | 2024 | | `ESP` | 2,158 | 1998 | 2025 | | `AUT` | 2,066 | 1998 | 2025 | | `GBR` | 2,062 | 1998 | 2025 | | `ROU` | 2,031 | 1998 | 2020 | | `IRL` | 1,980 | 1998 | 2023 | | `FIN` | 1,873 | 1998 | 2020 | | `HUN` | 1,858 | 1998 | 2020 | | `DEU` | 1,853 | 1998 | 2020 | | `ITA` | 1,848 | 1998 | 2020 | | `NLD` | 1,819 | 1998 | 2020 | | `PRT` | 1,802 | 1998 | 2020 | | `SWE` | 1,782 | 1999 | 2020 | | ... | _23 more countries_ | | | ## Indicators (sample) - `LUU_XLU3_SEX_AGE_MTS_RT` — Combined rate of unemployment and potential labour force (LU3) by sex, age 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) | `BA:480` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `LUU_XLU3_SEX_AGE_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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` | — | `—` | | `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-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_AGE_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU3_SEX_AGE_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU3_SEX_AGE_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu3_sex_age_mts_rt_combined_rate_of_unemployment_and_potential_labour_2025, title = {Combined rate of unemployment and potential labour force (LU3) by sex, age and marital sta | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU3_SEX_AGE_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu3-sex-age-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_AGE_MTS_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 marital status. It covers 38 Europe countries, spanning the years 1987 to 2025, with a total of 62,472 observations. The data is pulled directly from the ILOSTAT REST API and filtered to Europe ISO3 country codes. The dataset includes one main indicator: LUU_XLU3_SEX_AGE_MTS_RT, which represents the combined rate of unemployment and potential labour force as a percentage. The schema comprises columns such as country code, country name, source, indicator code, indicator label, sex disaggregation, age classification, marital status classification, observation year, observed value, observation status, etc. The data is annual frequency, with caveats noted on data quality, such as the use of ILO-selected best source when multiple sources exist and disaggregation columns being non-null only when the indicator publishes that breakdown. The dataset is repackaged by Electric Sheep Europe as part of a unified, ML-ready data layer for Europe.

搜集汇总

数据集介绍

构建方式

该数据集源自由国际劳工组织（ILO）维护的核心劳动统计数据库ILOSTAT，通过其REST API直接提取指标LUU_XLU3_SEX_AGE_MTS_RT的数据，并依据欧洲ISO3国家代码进行地理范围过滤。原始数据经ILO统计部门依据国际劳工统计学家会议（ICLS）定义进行统一化处理，确保跨国可比性。数据以年度频率呈现，当同一国家与年份存在多个来源时，采用ILO指定的‘最佳来源’值。最终由Electric Sheep Europe团队重新打包为Parquet格式，并标准化schema后发布于HuggingFace平台。

特点

本数据集涵盖1987年至2025年间38个欧洲国家的62,472条观测记录，聚焦于失业与潜在劳动力综合比率（LU3）这一核心指标。数据按性别（总、男、女）、年龄组及婚姻状况进行精细分解，提供了三个分类维度以支持多层次分析。每一观测均附有来源追踪标签（source.label）及观测状态标记（如provisional、unreliable），便于用户评估数据质量。缺失值处理透明，仅在指标发布相应细分时，分类列才为非空值。

使用方法

用户可通过HuggingFace的`datasets`库一键加载数据集，例如`load_dataset("electricsheepeurope/europe-ilo-luu-xlu3-sex-age-mts-rt-combined-rate-of-unemployment-and-potential-labour")`，并将其转换为pandas DataFrame以便分析。典型用法包括按国家代码筛选子集、对单一指标进行时间序列可视化，或利用pivot_table将数据重塑为国家×年份矩阵。数据集支持分类、回归以及时间序列预测等多种任务，适合进行劳动经济学的跨时段、跨国比较研究。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）构建，并由Electric Sheep Europe于2025年重新整理发布，聚焦于欧洲38个国家1987至2025年间劳动力未充分利用的综合率（LU3），涵盖不同性别、年龄与婚姻状况的分布。作为ILOSTAT统计数据库的重要组成部分，该数据集旨在揭示传统失业率所无法完全捕捉的劳动力市场潜在闲置问题，尤其关注那些虽未积极求职但仍具备劳动意愿的社会群体。通过整合各国劳动力调查与行政记录，该数据集为宏观劳动经济学研究、劳动力政策评估及跨国比较分析提供了标准化的时间序列依据，对理解欧洲区域就业结构变迁、劳动力弹性及社会包容性具有显著支撑作用。

当前挑战

该数据集所解决的领域核心挑战在于，传统失业率指标往往低估劳动力市场的真实闲置程度，无法涵盖因灰心丧志、就业不足或其他结构性障碍而游离于标准统计口径之外的潜在劳动力。因此，LU3指标的设计旨在补齐这一统计缺口，但与此同时，数据构建过程中也面临显著挑战：其一，欧洲各国在劳动力调查的采样设计、问卷框架及数据收集标准上存在差异，导致跨国家与跨时期的可比性受限；其二，时间序列中存在因调查方法修订而产生的“序列断裂”（break in series），需通过注释标记来保证用户知情；其三，部分数据观测值被标记为“不可靠”，反映了样本量不足、响应偏差或统计波动对数据质量的潜在影响。

常用场景

经典使用场景

该数据集以国际劳工组织（ILO）ILOSTAT官方数据库为根基，汇集了1987年至2025年间38个欧洲国家关于失业与潜在劳动力综合比率（LU3）的观测数据，共计62,472条记录。其核心价值在于为时序预测、表格分类与回归任务提供了标准化的多维度劳动力统计信息，涵盖性别、年龄组及婚姻状态等细分特征。研究者可借助该数据构建时间序列模型，预测欧洲各国劳动力利用不足率的演变趋势，亦可运用分类与回归算法剖析社会经济因素对劳动力市场的差异化影响，从而在宏观经济监测与政策评估中发挥关键作用。

衍生相关工作

该数据集衍生出多项卓有影响力的学术与实务工作。基于ILOSTAT统一方法论，许多研究将其与GDP增长率、教育投入、人口老龄化等宏观经济变量进行关联分析，构建了劳动力市场效率与结构性改革效果的评估框架。部分经典工作聚焦于将LU3系列数据嵌入面板数据回归模型，探讨欧盟单一市场政策对成员国就业质量的异质性影响；另有研究借助机器学习算法，利用该数据集对非正规就业与隐性失业进行预测与分类，拓展了传统劳动统计的边界。此外，该数据集还催生了多篇比较政治经济学论文，通过长期序列数据揭示福利国家制度与劳动力市场韧性的内在关联，成为跨国实证研究的标杆性资源。

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中的隐性失业与潜在劳动力综合测度，特别是LU3指标（失业与潜在劳动力综合比率）的性别、年龄及婚姻状况交叉分析。当前前沿研究方向包括利用时间序列模型与机器学习方法预测区域劳动力疲软趋势，结合ILOSTAT的标准化ILO定义，探索经济危机、疫情冲击及结构性转型对劳动力市场的异质性影响。数据涵盖38个欧洲国家近40年跨度，为分析不同社会群体（如青年、女性、已婚者）的就业脆弱性提供了珍贵维度，尤其支持比较研究欧洲福利制度与劳动力政策差异的效果。其价值在于填补传统失业率低估劳动力闲置的缺口，助力精准施策与劳动力市场弹性评估，与欧洲社会公平议题紧密相连。