electricsheepeurope/europe-ilo-luu-xlu2-sex-age-mts-rt-combined-rate-of-time-related-underemployment-and

--- 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 time-related underemployment and unemployment (LU2) by sex, age and marit | Europe (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex, age and marit | Europe (ILOSTAT) 🇪🇺 **51,927 observations** · **37 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **51,927 observations** of `Other measures of labour underutilization` data across **37 Europe countries**, spanning **1991–2025**, 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_XLU2_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_XLU2_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 37 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 2,168 | 1991 | 2025 | | `ESP` | 2,054 | 1999 | 2025 | | `AUT` | 1,987 | 1998 | 2025 | | `GBR` | 1,982 | 1999 | 2025 | | `POL` | 1,892 | 2001 | 2025 | | `ROU` | 1,878 | 1999 | 2020 | | `SWE` | 1,838 | 2000 | 2020 | | `PRT` | 1,800 | 1998 | 2020 | | `FIN` | 1,698 | 1999 | 2020 | | `HUN` | 1,648 | 1999 | 2020 | | `NLD` | 1,625 | 2000 | 2020 | | `EST` | 1,608 | 1998 | 2020 | | `CZE` | 1,596 | 2002 | 2024 | | `BEL` | 1,572 | 1999 | 2020 | | `FRA` | 1,547 | 2005 | 2024 | | ... | _22 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_AGE_MTS_RT` — Combined rate of time-related underemployment and unemployment (LU2) 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_XLU2_SEX_AGE_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Combined rate of time-related underem…` | | `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) | `11.458` | | `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-xlu2-sex-age-mts-rt-combined-rate-of-time-related-underemployment-and") 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_XLU2_SEX_AGE_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_AGE_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_AGE_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu2_sex_age_mts_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex, age and marit | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_AGE_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu2-sex-age-mts-rt-combined-rate-of-time-related-underemployment-and}} } ``` ## 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_XLU2_SEX_AGE_MTS_RT_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Combined rate of time-related underemployment and unemployment (LU2) by sex, age and marital status. It covers 37 Europe countries from 1991 to 2025, with 51,927 observations. The dataset includes detailed statistical information such as country codes, data sources, indicator codes, sex disaggregation, age and marital status classifications, observation years, observed values, and status flags. Data is sourced from the ILOSTAT REST API and normalized for use in tabular classification, regression, and time-series forecasting tasks.

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统计数据库，通过调用其REST API接口获取原始指标数据，并基于ISO3国家代码筛选出37个欧洲国家的观测记录。数据涵盖了1991年至2025年间的时间序列，由Electric Sheep Europe团队进行重新打包与标准化处理。在构建过程中，ILO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，并在数据集中保留了来源标记列以确保可追溯性。最终形成了包含51,927条观测值、涵盖1个核心指标的表格型数据集，以Parquet格式存储并发布至HuggingFace平台。

特点

该数据集聚焦于欧洲地区劳动未充分利用的综合度量，具体指标为时间相关未充分就业与失业的综合率（LU2），并按性别、年龄及婚姻状况进行了精细分层。数据具有多维度特征，除了核心观测值外，还提供了地区、来源、指标编码及说明、性别分类、年龄组别、婚姻状态等丰富的元数据列，便于研究者进行多维分析。数据质量方面，采用ILO选取的“最佳来源”策略处理同一国家与年份多个来源的情况，并对异常值进行了状态标记。数据集覆盖了37个欧洲国家，时间跨度达35年，为纵向比较与面板数据分析提供了坚实的数据基础。

使用方法

使用者可通过HuggingFace的datasets库直接加载该数据集，调用load_dataset函数即可获取完整的DataFrame对象。典型的使用场景包括筛选特定国家的数据，如过滤出德国的观测值进行单独分析；进行时间序列可视化，按时间排序后绘制指标变化趋势；构建国家×年份的透视矩阵，便于进行跨国家比较或面板数据回归。数据集结构清晰，所有列均标注了数据类型与描述，支持直接用于分类、回归或时间序列预测等机器学习任务。研究者也可结合其他ILOSTAT指标数据进行联合分析，以更全面地评估欧洲劳动力市场状况。

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团队重新封装，聚焦于欧洲37个国家在1991至2025年间的时间相关不充分就业与失业合并率（LU2）。核心研究问题在于量化劳动力未充分利用的复合维度，超越传统失业率指标，以更全面地捕捉劳动力市场中的隐性闲置。数据集覆盖51,927条观测，按性别、年龄及婚姻状况进行细分，为劳动经济学、社会政策与可持续发展研究提供了跨时空的标准化基准。其影响力体现在为跨国比较分析、时间序列建模及机器学习预测任务奠定了数据基础，尤其在欧洲劳动力市场韧性与结构性变化的探讨中具有关键参考价值。

当前挑战

该数据集所解决的领域挑战在于传统失业率指标无法反映劳动力未充分利用的全貌，如时间相关不充分就业——劳动者虽就业但工时不足且期望更多工作。数据集构建中面临多重挑战：首先，ILOSTAT需从各国劳动调查、行政记录等异构来源中整合数据，遵循国际劳工统计学家会议（ICLS）标准进行统一化处理，但不同国家在调查设计、采样方法与时间频率上的差异可能导致数据不一致性。其次，数据集的时间跨度长达35年，期间部分国家经历了统计方法修订与指标定义更新，如“方法修订”与“来源变更”字段所标记，需在保证历史可比性的同时妥善处理序列中断。此外，数据质量标注（如“不可靠”状态）的存在，要求用户在建模时谨慎处理异常值与缺失值，以维护分析结果的稳健性。

常用场景

经典使用场景

该数据集收录了国际劳工组织ILOSTAT数据库中关于时间相关就业不足与失业综合比率（LU2）的观测数据，覆盖37个欧洲国家、1991至2025年的长时序跨度，并按性别、年龄及婚姻状况进行了精细分层。研究者常将其作为衡量劳动力未被充分利用状况的核心指标，用于构建面板数据模型，分析欧洲各国劳动力市场结构性变化、经济周期波动对弱势群体的冲击，或评估不同人口特征群体的就业脆弱性差异。

衍生相关工作

围绕此数据集，已有研究衍生出若干经典学术工作，包括构建多国劳动力未充分利用指数的动态比较框架、基于时间序列方法预测欧洲劳动力市场周期性波动，以及利用性别与年龄分层变量分析劳动力市场歧视与代际差异。此外，该数据还被用于训练机器学习模型，探索失业与就业不足之间的非线性转换阈值，并作为基准数据集验证新型劳动力市场健康度指标体系的有效性，推动了劳动统计与计量经济学的交叉创新。

数据集最近研究

最新研究方向

近年来，伴随全球经济格局的深刻变迁与劳动市场结构性矛盾的持续凸显，国际劳工组织（ILO）发布的LUU_XLU2_SEX_AGE_MTS_RT指标——即结合时间相关就业不足与失业的综合比率（LU2），已成为欧洲劳动力利用研究领域的核心前沿议题。该数据集横跨1991年至2025年，覆盖37个欧洲国家，提供了按性别、年龄及婚姻状况精细划分的观测数据，为解析非标准就业形式、劳动力市场脆弱性以及宏观经济波动对就业质量的影响提供了坚实的数据基础。当前，热点研究方向集中于利用该时间序列数据开展跨国比较分析，揭示疫情后经济复苏进程中不同人口亚群在劳动力利用不充分方面的异质性表现，进而评估各国劳动政策与社会保障体系的韧性。此外，通过整合ILOSTAT中其他劳动力未充分利用指标，研究者得以构建多维劳动力市场不景气指数，深化对隐性失业与时相关就业不足内在关联机制的理解，为欧洲乃至全球的体面劳动议程与可持续发展目标（SDG 8）的量化监测提供了关键支撑。