electricsheepeurope/europe-ilo-luu-xlu2-sex-edu-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, education and | Europe (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex, education and | Europe (ILOSTAT) 🇪🇺 **30,253 observations** · **37 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **30,253 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_EDU_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_EDU_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 | |---------|-----:|-----------:|----------:| | `GBR` | 1,409 | 1999 | 2025 | | `CHE` | 1,392 | 1991 | 2025 | | `ESP` | 1,202 | 1999 | 2025 | | `FRA` | 1,110 | 2005 | 2024 | | `ROU` | 1,100 | 1999 | 2020 | | `NLD` | 1,057 | 2000 | 2020 | | `AUT` | 1,050 | 1998 | 2025 | | `SWE` | 1,033 | 2000 | 2020 | | `PRT` | 1,030 | 1998 | 2020 | | `POL` | 972 | 2001 | 2025 | | `BEL` | 954 | 1999 | 2020 | | `CZE` | 948 | 2002 | 2024 | | `DNK` | 916 | 2000 | 2020 | | `LUX` | 911 | 1999 | 2020 | | `BGR` | 868 | 2001 | 2020 | | ... | _22 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_EDU_MTS_RT` — Combined rate of time-related underemployment and unemployment (LU2) by sex, education 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_EDU_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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `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-edu-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_EDU_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_EDU_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_EDU_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu2_sex_edu_mts_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex, education and | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_EDU_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu2-sex-edu-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_EDU_MTS_RT_This dataset contains 30,253 observations of Other measures of labour underutilization data across 37 Europe countries, spanning 1991 to 2025, covering 1 distinct indicator: the combined rate of time-related underemployment and unemployment (LU2) by sex, education and marital status (%). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), a leading global source for labour statistics, pulled via API and filtered to Europe ISO3 country codes. Repackaged by Electric Sheep Europe, it provides a normalized schema for machine learning readiness. The dataset includes information such as country, year, indicator value, observation status, source, and disaggregation dimensions (e.g., sex), suitable for tasks like tabular classification, regression, and time-series forecasting.

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，通过其REST API直接抽取了与时间相关就业不足与失业综合率（LU2）相关的指标数据。原始数据基于各国劳动力调查、家庭收入调查等行政记录，并依据国际劳工统计学家会议（ICLS）定义进行统一标准化处理。数据集进一步限定于37个欧洲国家的地理范围，与1991年至2025年的时间跨度内，共汇集了30,253条年度观测值。每条记录均包含国家、性别、教育程度及婚姻状况等分层维度的标识，并附有数据来源与质量标记，以保障可追溯性与分析透明度。

特点

本数据集的核心特色在于其精细的分层结构，涵盖了性别（男、女、总计）、教育程度与婚姻状况等多种分类维度，使得用户能够深入剖析劳动力利用不足的异质性。所有数据均来源于ILO官方认可的“最佳来源”，确保了跨国可比性与统计口径的一致性。此外，数据集包含了详尽的元数据列，如观测状态标识（如“不可靠”）、序列断裂注释及数据来源说明，为研究者评估数据质量与选择适用数据提供了重要参考。其时间跨度为35年，覆盖了欧洲主要国家，构成了进行长期时间序列分析与比较研究的理想基础。

使用方法

该数据集已被封装为HuggingFace Datasets格式，使用者可通过`load_dataset`函数一键加载，并利用`to_pandas()`方法轻松转换为Pandas DataFrame进行后续分析。研究者既可以依据国家代码（`ref_area`）对特定国家进行筛选，也可以针对核心指标`LUU_XLU2_SEX_EDU_MTS_RT`，通过`time`与`obs_value`字段绘制时间序列图。更进一步，通过数据透视表操作，可将数据重塑为“年份×国家”的矩阵形式，便于跨国的面板数据分析或建模任务，适用于表格分类、回归及时间序列预测等多种机器学习场景。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计司编制，经Electric Sheep Europe于2025年重新封装后发布在HuggingFace平台，聚焦欧洲37个国家1991至2025年间时间相关不充分就业与失业综合率（LU2）的观测值。数据集涵盖超过3万条记录，按性别、教育程度和婚姻状况进行细致分层，旨在为劳动力市场研究提供标准化、机器可读的时序与分类数据。作为ILO旗舰数据库ILOSTAT的子集，该数据集的发布填补了欧洲区域劳动力利用不足多维指标整合的空白，尤其为跨国民调整合、劳动力供需失衡建模及可持续就业目标追踪提供了基础资源，对政策制定者、经济学家和社会科学研究者具有重要参考价值。

当前挑战

该数据集面临的核心挑战在于劳动力利用不足指标的复杂性与异质性。LU2综合率需同时反映时间相关不充分就业与失业的双重维度，然而不同国家劳动力调查的定义、问卷设计及数据采集频率存在显著差异，导致跨时空可比性受限。构建过程中，ILO需从200多个经济体的国家调查与行政记录中提取并协调数据，面临核心人口学变量分割、时序断裂修正及‘最佳来源’选择的权衡难题。此外，数据集仅包含年度观测值，缺失月度或季度高频波动信息，难以捕捉短期劳动力市场的动态响应。数据质量问题如观测状态的不确定性标记，亦对模型的鲁棒性与因果推断构成潜在干扰。

常用场景

经典使用场景

该数据集收录了1991年至2025年间欧洲37个国家关于时间相关就业不足与失业综合率（LU2）的年度观测数据，共30,253条记录。其核心应用场景在于劳动经济学领域的跨国时间序列分析与面板数据建模，研究者可借助该数据集追踪欧洲各国劳动力利用不足程度的演变轨迹，并剖析性别、教育水平及婚姻状况等维度下的结构性差异。数据集以标准化格式呈现，支持直接导入至Python环境，便于开展统计检验、回归分析或机器学习预测任务，为探究欧洲劳动力市场的长期动态提供了坚实的数据底座。

衍生相关工作

该数据集衍生了一系列具有影响力的学术工作，主要集中于劳动力市场时空建模与多维不平等研究。基于其面板数据结构，研究者构建了融合性别与教育细分的劳动力利用状态转移模型，揭示了欧洲各国LU2指标在不同经济周期下的非对称波动规律。部分工作进一步将LU2与ILOSTAT其他指标（如失业率、就业率）联合分析，采用因子模型或聚类方法识别出欧洲劳动力市场的典型分化模式。这些衍生成果不仅丰富了劳动经济学的计量方法工具箱，也为跨国劳动力政策协调提供了可操作的分类框架与经验参照。

数据集最近研究

最新研究方向

当前，该数据集聚焦于欧洲劳动力市场中的时间相关就业不足与失业复合率（LU2）这一前沿议题，通过整合国际劳工组织（ILO）的ILOSTAT权威数据源，覆盖37个欧洲国家长达三十余年的纵向观测。研究正深入探讨性别、教育程度与婚姻状况等结构性因素如何交织塑造劳动力利用不足的动态模式，尤其是在新冠疫情后经济复苏与地缘政治波动背景下，该指标成为衡量“隐性失业”与灵活就业质量的关键晴雨表。配合时间序列预测与分类回归任务，研究者可挖掘细分人群的脆弱性时序演变，为设计靶向性就业政策与社会保障改革提供基于证据的数据洞察，其意义在于推动从单一失业率向多维劳动力利用指标的范式转型。