electricsheepeurope/europe-ilo-luu-xlu4-sex-geo-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: - 1K<n<10K tags: - tabular - europe - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Composite rate of labour underutilization (LU4) by sex and rural / urban areas (%) | Europe (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex and rural / urban areas (%) | Europe (ILOSTAT) 🇪🇺 **5,982 observations** · **34 Europe countries** · **1998–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **5,982 observations** of `Other measures of labour underutilization` data across **34 Europe countries**, spanning **1998–2025**, 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_GEO_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_GEO_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 34 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `AUT` | 252 | 1998 | 2025 | | `FIN` | 246 | 1999 | 2024 | | `PRT` | 243 | 1998 | 2024 | | `FRA` | 243 | 1998 | 2024 | | `BEL` | 234 | 1999 | 2024 | | `LUX` | 234 | 1999 | 2024 | | `DNK` | 225 | 2000 | 2024 | | `NLD` | 225 | 2000 | 2024 | | `ESP` | 225 | 1999 | 2024 | | `EST` | 222 | 1998 | 2024 | | `LTU` | 216 | 2001 | 2024 | | `HUN` | 216 | 2001 | 2024 | | `LVA` | 216 | 2001 | 2024 | | `GBR` | 210 | 1999 | 2019 | | `ITA` | 207 | 2002 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_GEO_RT` — Composite rate of labour underutilization (LU4) by sex and rural / urban areas (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AUT` | | `ref_area.label` | `string` | Country name in English | `Austria` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:103` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `LUU_XLU4_SEX_GEO_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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `10.939` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-geo-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_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu4_sex_geo_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex and rural / urban areas (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu4-sex-geo-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_GEO_RT_This dataset contains Other measures of labour underutilization data from the International Labour Organization (ILO) ILOSTAT database, specifically focusing on the Composite rate of labour underutilization (LU4) by sex and rural / urban areas indicator. It covers 34 European countries from 1998 to 2025, with 5,982 observations. The data is provided at an annual frequency and includes columns such as country code, country name, data source, indicator code, indicator label, sex disaggregation (total, male, female), rural/urban classification, observation year, observed value, observation status flags, and related notes. The dataset is designed for labor market analysis and can be used for tasks like tabular classification, regression, and time-series forecasting. It has been repackaged by Electric Sheep Europe with a consistent schema for machine learning readiness.

搜集汇总

数据集介绍

构建方式

该数据集的构建依托于国际劳工组织（ILO）的ILOSTAT中央统计数据库，通过调用ILOSTAT REST API直接获取原始指标数据，并针对欧洲地区进行地理筛选。具体而言，数据提取自标识符为LUU_XLU4_SEX_GEO_RT的接口，随后依据欧洲ISO3国家代码对观测值进行过滤，最终汇集为涵盖34个欧洲国家、时间跨度从1998年至2025年的结构化时局面板数据。在整合过程中，ILOSTAT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协调统一，来源信息通过source.label字段予以标注，以确保数据的可追溯性与一致性。

特点

该数据集的核心特点在于其聚焦于劳动力利用不足的综合度量指标（LU4），并率先在区域层面提供了按性别及城乡区域细分的分类观测值。数据集包含5,982条年度观测记录，覆盖了从1998年至2025年的长期时序，且每个观测点均附有清晰的元数据，包括数据来源、观测状态标志及注释信息，为研究者评估劳动力市场结构性失衡提供了多维视角。此外，数据经过精选与重新打包，仅保留ILO认定的‘最佳来源’，有效规避了同一国家同一年份多源数据可能带来的不一致性问题。

使用方法

使用者可通过HuggingFace的datasets库便捷地加载该数据集，一行代码即可将数据转换为pandas DataFrame进行后续分析。基于结构化的字段设计，研究者可通过筛选ref_area列快速聚焦于特定国家的时序趋势，或利用indicator列固定单一指标后按time列排序以绘制时间序列图。数据分析的灵活性还体现在能够通过pivot_table方法将长格式数据重塑为国家×年份的矩阵形式，从而便于进行跨国别、跨时段的比较研究。数据默认以训练集形式提供，适合直接应用于表格分类、回归分析及时间序列预测等机器学习任务。

背景与挑战

背景概述

劳动力利用不足问题长期困扰着全球劳动经济学界，传统的失业率指标因未能涵盖隐性失业、时间相关就业不足及潜在劳动力等群体，在评估真实劳动力市场健康状况方面存在显著局限。在此背景下，国际劳工组织（ILO）统计部门基于其旗舰数据库ILOSTAT，创建了包含复合劳动力利用不足率（LU4）在内的专项数据集，由Electric Sheep Europe于2025年完成重新封装并发布。该数据集覆盖1998至2025年间34个欧洲国家，共计5982条观测记录，核心研究问题在于通过多维度劳动力利用不足测度，揭示不同性别和城乡区域间劳动市场的结构性差异。作为劳动力统计领域的权威基准，该数据集为政策制定者与研究者提供了跨国民间可比的精细指标，对欧洲劳动市场监测、国际可持续发展目标（SDG）进展评估及比较劳动经济学研究产生了深远影响。

当前挑战

该数据集所应对的领域核心挑战在于，传统失业率指标（如ILO定义的失业率）仅关注主动求职而未就业群体，无法捕捉因经济环境恶化而退出劳动力市场的隐性失业者、因无法获得全职工作而被迫从事非自愿兼职的时间相关就业不足者，以及虽具备劳动能力但暂未积极求职的潜在劳动力，导致劳动力市场真实闲置程度被系统性低估。LU4复合指标的引入旨在整合上述多元维度，从而提供更全面的劳动力利用不足全景。在数据集构建层面，挑战主要源于跨国数据协调与质量管控：34个国家的原始数据来源各异，包括劳动力调查、住户收入调查及行政记录等，需依据国际劳工统计学家会议（ICLS）定义进行规范化处理；同时，年代跨度长达28年，其间部分国家的调查方法、问卷设计或分类标准发生过变更，数据集通过标注断点标识（如“Break in series”）及溯源信息来维持序列的可比性，但缺失值与时序异质性仍对长周期跨国分析构成障碍。

常用场景

经典使用场景

该数据集主要服务于劳动力市场失序与就业质量评估的研究领域，经典使用场景聚焦于剖析欧洲34国在1998至2025年间劳动利用不足的复合率（LU4），并按性别与城乡地理维度进行精细化分解。研究者可借助此数据进行跨国面板回归，考察经济增长、制度变迁或政策干预对不同性别及地域群体劳动利用不足率的异质性影响。同时，该数据为时间序列预测与分类任务提供了优质基础，可用于构建预警模型，评估劳动力市场结构性错配的动态演化趋势。

衍生相关工作

基于该数据集的经典衍生工作包括构建多国劳动力市场脆弱性指数、开发性别与地理维度下的劳动利用不足预测模型，以及整合其他经济指标（如GDP、通货膨胀率）进行劳动市场效率的因果推断研究。部分研究还将其与微观调查数据（如劳动力调查）结合，运用机器学习方法识别劳动利用不足的结构性驱动因素，推动了计量经济学与数据科学在劳动统计领域的交叉融合，催生了大量关于就业质量评估与政策模拟的学术文献。

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度量（LU4），涵盖性别与城乡区域分类，为劳动经济学中精准刻画隐性失业、就业不足及“隐形失业”现象提供了关键数据支撑。在新冠疫情后劳动力市场韧性分析、欧洲绿色转型中的结构性失业监测，以及人工智能对就业替代与创造效应的实证研究中，LU4指标突破了传统失业率的局限性，成为衡量劳动力市场健康度的前沿工具。其高时间跨度（1998–2025）与多国面板结构，为构建动态劳动利用不足的时序预测模型、挖掘性别与地域维度的异质性模式，以及验证国际劳工组织（ILO）新统计标准（如ICLS19）的适用性，奠定了数据基础。