electricsheepeurope/europe-ilo-luu-xlu4-sex-edu-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 and education (%) | Europe (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex and education (%) | Europe (ILOSTAT) 🇪🇺 **27,096 observations** · **37 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **27,096 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_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_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` | 1,098 | 1999 | 2025 | | `FRA` | 1,024 | 1998 | 2024 | | `NLD` | 984 | 2000 | 2024 | | `PRT` | 948 | 1998 | 2025 | | `GRC` | 915 | 2006 | 2025 | | `ESP` | 908 | 1999 | 2025 | | `BEL` | 894 | 1999 | 2024 | | `ITA` | 874 | 2002 | 2024 | | `CZE` | 867 | 2002 | 2024 | | `LUX` | 861 | 1999 | 2024 | | `AUT` | 861 | 1998 | 2025 | | `HUN` | 836 | 1999 | 2024 | | `DNK` | 830 | 2000 | 2024 | | `POL` | 802 | 2004 | 2024 | | `SWE` | 798 | 2005 | 2024 | | ... | _22 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_EDU_RT` — Composite rate of labour underutilization (LU4) by sex and education (%) ## 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_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` | | `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` | — | `C3:5578` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-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_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu4_sex_edu_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex and education (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu4-sex-edu-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_RT_This dataset contains 27,096 observations of the Composite rate of labour underutilization (LU4) by sex and education (%) across 37 Europe countries, spanning 1991 to 2025, covering 1 distinct indicator. It is sourced from ILOSTAT, the ILOs central statistics database, with data pulled via API and filtered to European countries, and is designed for tabular classification, regression, or time-series forecasting tasks.

搜集汇总

数据集介绍

构建方式

本数据集源自国际劳工组织（ILO）的ILOSTAT统计数据库，通过其REST API接口直接提取原始指标数据，并限定于欧洲37个国家的ISO3国家代码进行地理过滤。数据涵盖1991年至2025年的年度观测值，合计27,096条记录。原始微观数据经过ILO根据国际劳工统计学家会议（ICLS）定义进行协调统一，来源信息通过' source.label'列进行标注，确保数据可追溯性。数据集由Electric Sheep Europe重新打包，以Parquet格式发布，便于机器学习研究直接使用。

特点

该数据集聚焦于劳动力利用不足综合率（LU4）这一核心指标，并按性别和教育程度进行细致分类，为劳动经济学研究提供了丰富的分解维度。数据覆盖37个欧洲国家，时间跨度长达35年，兼具地域广度与时间纵深。每个观测值均包含详细的元数据，如数据来源、观测状态标志（如临时性或不可靠性）以及分类注释，帮助用户精准评估数据质量。此外，数据采用标准化模式，包含15个字段，支持时间序列分析、面板数据建模等多种研究范式。

使用方法

用户可通过HuggingFace Datasets库的一键加载功能快速获取数据，调用`load_dataset()`方法即可将数据集直接转换为Pandas DataFrame格式。加载后，可轻松按国家（如'DEU'）、指标（如'LUU_XLU4_SEX_EDU_RT'）或时间维度进行过滤与切片。数据集支持灵活的透视操作，例如构建国家×年份矩阵，便于进行跨国家比较和时间序列可视化。对于需要处理多维度分解的研究者，可通过性别和分类变量进行分组聚合，深入分析不同群体的劳动力利用不足模式。

背景与挑战

背景概述

在劳动经济学与社会政策研究领域，劳动力利用不足的综合衡量是评估就业市场健康程度与结构性失衡的关键指标。该数据集由国际劳工组织（ILO）统计部门创建，依托其旗舰数据库ILOSTAT，聚焦于欧洲地区劳动力利用不足复合率（LU4）的性别与教育层次细分。数据集涵盖1991年至2025年间37个欧洲国家的27,096条观测记录，由Electric Sheep Europe于2025年重新打包并标准化为机器学习友好格式。其核心研究问题在于通过可比较的跨国时间序列数据，揭示不同性别与教育背景群体在劳动力市场中的非充分就业形态，为欧盟就业战略与可持续发展目标（SDG体面工作）的监测提供量化基础。作为全球劳动力统计的权威来源，ILOSTAT数据已广泛应用于国际比较研究、政策评估及经济预测模型，该数据集通过统一架构与开放许可（CC-BY 4.0）进一步降低了研究门槛。

当前挑战

该数据集所解决的领域核心挑战在于劳动力利用不足的量化定义与跨国可比性。传统失业率指标（如ILO定义的失业率）无法捕捉隐性失业、时间相关就业不足及劳动力市场附着弱化等复杂形态，而LU4复合率通过整合闲暇时间、潜在劳动力及边缘附着群体，提供了更完整的非充分就业图景。然而，构建过程中面临多重挑战：首先，各成员国调查方法、问卷设计及统计标准存在差异，ILO虽依据国际劳工统计学家会议（ICLS）定义进行协调，但元数据中标记的“方法修订断层”与“不可靠观测”提示数据连续性风险；其次，数据来源涵盖劳动力调查、行政记录等多渠道，不同来源间的计量偏差需通过“最佳来源”选择策略处理；再者，教育分类的非标准化问题（如非标准教育水平注释）要求研究者谨慎处理跨周期对比，这些质性缺陷在高频政策应用中可能放大推断误差。

常用场景

经典使用场景

该数据集收录了1991年至2025年间37个欧洲国家的劳动力利用不足综合率（LU4），并按照性别与教育水平进行了精细分层。它最经典的使用场景在于追踪欧洲各国劳动力市场中隐性失业与就业不足的长期演变趋势，揭示不同性别群体以及不同受教育程度劳动者在劳动力资源闲置状态下的结构性差异。研究者可以通过时间序列分析，观察经济周期波动对特定人群劳动力利用效率的冲击，或比较各国在金融危机、疫情等外部冲击下劳动力市场的恢复弹性。

衍生相关工作

基于ILOSTAT原始数据集的清洗与结构化，一系列衍生研究工作得以展开。Electric Sheep Europe团队将该数据集重包装为机器学习友好的格式，推动了劳动力市场预测模型的构建，例如基于性别与教育分层的LU4时间序列预测算法。学术界已出现利用该数据训练时序回归模型以识别劳动力利用不足的先行指标，或结合社会经济特征进行因果推断的研究。同时，该标准化数据集促使跨国家面板数据比较分析更加高效，催生了关于欧洲劳动力市场制度差异（如失业保障、积极就业政策）对LU4影响的实证论文，并为进一步结合自然语言处理（如从政策文本中提取就业相关变量）提供了高质量的训练基底。

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利用不足的综合率（LU4）按性别与教育水平的细分动态，为劳动经济学与政策研究提供了高分辨率的时间序列数据。当前前沿方向涵盖后疫情时代劳动力市场韧性评估、性别与教育不平等对就业质量的结构性影响，以及欧洲绿色转型中技能错配与隐性失业的量化分析。结合ILOSTAT的标准化方法论，该数据支持跨国家、跨时期的比较研究，尤其在监测欧盟《欧洲社会权利支柱》与联合国可持续发展目标（SDG 8）的进展中具有关键作用。此外，其开放的CC-BY-4.0许可与Parquet格式推动了可复现的机器学习建模，助力预测劳动力利用模式与政策干预效果的实证评估。