electricsheepeurope/europe-ilo-luu-xlu4-sex-edu-mts-rt-composite-rate-of-labour-underutilization-lu4-by-s

--- 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: "Composite rate of labour underutilization (LU4) by sex, education and marital status (%) | Europe (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex, education and marital status (%) | Europe (ILOSTAT) 🇪🇺 **30,284 observations** · **37 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **30,284 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_XLU4_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_XLU4_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 | |---------|-----:|-----------:|----------:| | `CHE` | 1,427 | 1991 | 2025 | | `GBR` | 1,418 | 1999 | 2025 | | `ESP` | 1,205 | 1999 | 2025 | | `ROU` | 1,125 | 1999 | 2020 | | `FRA` | 1,115 | 2005 | 2024 | | `NLD` | 1,100 | 2000 | 2020 | | `AUT` | 1,052 | 1998 | 2025 | | `PRT` | 1,032 | 1998 | 2020 | | `LUX` | 969 | 1999 | 2020 | | `CZE` | 960 | 2002 | 2024 | | `SWE` | 959 | 2000 | 2020 | | `BEL` | 955 | 1999 | 2020 | | `DNK` | 924 | 2000 | 2020 | | `BGR` | 879 | 2001 | 2020 | | `HUN` | 875 | 1999 | 2020 | | ... | _22 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_EDU_MTS_RT` — Composite rate of labour underutilization (LU4) 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_XLU4_SEX_EDU_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Composite rate of labour underutiliza…` | | `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) | `13.165` | | `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-xlu4-sex-edu-mts-rt-composite-rate-of-labour-underutilization-lu4-by-s") 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_XLU4_SEX_EDU_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_EDU_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_EDU_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu4_sex_edu_mts_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex, education and marital status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_EDU_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu4-sex-edu-mts-rt-composite-rate-of-labour-underutilization-lu4-by-s}} } ``` ## 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_XLU4_SEX_EDU_MTS_RT_This dataset contains composite rate of labour underutilization (LU4) data for 37 European countries from 1991 to 2025, disaggregated by sex, education, and marital status. It includes 30,284 observations covering 1 key indicator (LUU_XLU4_SEX_EDU_MTS_RT), which measures the composite rate of labour underutilization (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via the ILOSTAT REST API, and repackaged by Electric Sheep Europe into a machine learning-ready format. Organized in tabular form, the dataset includes columns such as country code, year, indicator value, data source, and sex classification, making it suitable for tasks like tabular classification, regression, and time-series forecasting. The data is harmonized by ILO based on International Conference of Labour Statisticians (ICLS) definitions and includes quality flags (e.g., for unreliable data). It supports various use cases, including filtering by country, time-series analysis, and data pivoting.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Europe团队通过ILOSTAT REST API直接获取，源数据接口地址为https://rplumber.ilo.org/data/indicator?id=LUU_XLU4_SEX_EDU_MTS_RT。原始数据经过国际劳工组织（ILO）基于国际劳工统计学家会议（ICLS）定义进行统一协调处理，涵盖37个欧洲国家的劳动力利用不足率（LU4）指标。数据筛选仅限于欧洲ISO3国家代码对应的地理范围，并保留了source.label列以追溯原始调查来源，确保了数据的可溯源性与标准化。

使用方法

用户可通过HuggingFace Datasets库的load_dataset函数直接加载该数据集，返回的DataFrame可直接用于Python分析。推荐的操作包括：按国家（ref_area）过滤以聚焦特定区域，按指标（indicator）筛选后进行时间序列可视化，以及通过pivot_table将数据重塑为国家×年份的矩阵形式，便于面板数据分析或回归建模。数据集采用cc-by-4.0许可，使用时需同时引用原始ILO来源和Electric Sheep Europe的重包装版本。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门构建，并由Electric Sheep Europe于2025年重新打包发布，专注于欧洲37个国家在1991至2025年间劳动力利用不足的复合率（LU4）指标。核心研究问题在于通过性别、教育程度和婚姻状况等多维度剖分，精确刻画劳动力市场的结构性失衡与隐性失业现象。作为ILOSTAT数据库的重要组成部分，该数据集为劳动经济学、社会政策评估及可持续发展目标（SDG）中的体面工作指标提供了标准化、可复用的跨时序数据源，显著推动了欧洲劳动力市场比较研究的量化分析进程。

当前挑战

领域内面临的挑战在于传统失业率指标（如ILO定义的失业率）无法全面反映劳动力市场的真实闲置程度，尤其是隐性失业、时间相关不充分就业及潜在劳动力群体被忽略的问题，而LU4复合率通过整合多重指标提供了更全面的测度框架。构建过程中，数据面临跨国调查方法不一致、年度频率与部分国家月度数据间的时间粒度冲突、由于方法论修订导致的时间序列断点（如'break in series'注释所示）、以及多源数据择优选择时的溯源与标准化难题，这些均对数据的一致性和可比性构成了严峻考验。

常用场景

经典使用场景

在劳动经济学与社会统计学领域，该数据集的核心应用在于对欧洲各国劳动力利用不足的综合率（LU4）进行多维度的时序分析与截面比较。研究者常利用其按性别、教育程度和婚姻状况细分的数据结构，揭示不同人口群体在劳动力市场中的结构性差异。典型的使用方式包括构建混合效应模型以量化国家与年份对劳动利用不足的随机影响，或运用面板数据回归探讨宏观经济变量与劳动力闲置率之间的关联。此外，时间序列预测任务中，该数据集为ARIMA、GARCH或状态空间模型提供了超过三十年跨度的连续观测，是捕捉欧洲劳动力市场长期演变规律的宝贵素材。

解决学术问题

该数据集有效突破了传统失业率指标在刻画劳动力市场健康程度时的局限性，为学术界提供了更为全面的LU4复合指标，从而解决了仅依赖失业率可能低估劳动力闲置规模的问题。通过整合性别、教育与婚姻状况等人口学特征，它使得研究者能够深入剖析特定群体——如低教育水平女性或单身青年——在劳动力市场中所面临的系统性困境。这一精细化的数据支持，对于验证人力资本理论中的信号筛选模型、探索婚姻状况对劳动供给决策的影响机制，以及评估欧盟各国积极劳动力市场政策的异质性效果，均具有不可替代的学术价值。

实际应用

在实际政策制定与社会治理层面，该数据集是欧洲各国劳动部门与国际劳工组织（ILO）开展劳动市场监测与评估的关键工具。政策分析师可利用其按性别和教育程度划分的数据，精准识别出失业率表象下隐藏的‘准失业’人群——例如因就业不足而被迫从事兼职的大学毕业生，或长期脱离求职队伍的已婚女性。这些洞察直接服务于针对性职业培训项目的设计、社会保障体系的优化，以及促进性别平等的就业法规修订。对于跨国企业而言，该数据集还可辅助评估不同国家的人力资源潜力，为欧洲境内的投资选址与人才招聘策略提供数据驱动的决策依据。

数据集最近研究

最新研究方向

在劳动力市场非充分就业的测度领域，复合劳动力利用不足率（LU4）作为超越传统失业率的综合性指标，正日益成为欧洲劳动力结构变迁研究的前沿焦点。该数据集整合了国际劳工组织ILOSTAT框架下37个欧洲国家1991至2025年的跨年度观测，覆盖性别、教育程度与婚姻状况等精细分层维度，为探讨后疫情时代欧洲就业质量分化、技能错配与人口结构转型提供了关键数据支撑。其时间序列属性与多维度分类特征，使得研究者能够借助时间序列预测与表格回归方法，深入剖析隐性失业现象的演化轨迹及群体异质性，进而为制定更具包容性的劳动力市场政策贡献实证依据。