electricsheepeurope/europe-ilo-luu-xlu4-sex-dsb-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: - n<1K tags: - tabular - europe - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Composite rate of labour underutilization (LU4) by sex and disability status (%) | Europe (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex and disability status (%) | Europe (ILOSTAT) 🇪🇺 **291 observations** · **3 Europe countries** · **2005–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **291 observations** of `Other measures of labour underutilization` data across **3 Europe countries**, spanning **2005–2025**, 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_DSB_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_DSB_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 3 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GBR` | 171 | 2005 | 2025 | | `MDA` | 66 | 2018 | 2025 | | `POL` | 54 | 2015 | 2020 | ## Indicators (sample) - `LUU_XLU4_SEX_DSB_RT` — Composite rate of labour underutilization (LU4) by sex and disability status (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `GBR` | | `ref_area.label` | `string` | Country name in English | `United Kingdom of Great Britain and N…` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:666` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `LUU_XLU4_SEX_DSB_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.) | `DSB_STATUS_TOTAL` | | `classif1.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `12.043` | | `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-dsb-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_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu4_sex_dsb_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex and disability status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu4-sex-dsb-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_DSB_RT_This dataset contains composite rates of labour underutilization (LU4) by sex and disability status (%) for 3 European countries (United Kingdom, Moldova, Poland) from 2005 to 2025. It includes 291 observations with one core indicator: LUU_XLU4_SEX_DSB_RT, sourced from the International Labour Organizations ILOSTAT database. The data is structured in tabular format with columns for country codes, year, observed values, sex disaggregation (total, male, female), disability status classification, and metadata. It is repackaged by Electric Sheep Europe to provide a unified, ML-ready data layer for Europe, suitable for tasks like tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集来源于国际劳工组织（ILO）的ILOSTAT统计数据库，通过其REST API直接获取原始指标数据，并经由Electric Sheep Europe进行二次处理与整合。原始数据以劳动力调查微观数据为基础，依据国际劳工统计学家会议（ICLS）标准进行统一化处理，确保了不同国家与时期数据的可比性。构建过程中，数据被筛选至欧洲三个国家的ISO3代码范围，并涵盖2005年至2025年的时间跨度，最终形成包含291条观测记录的结构化表格数据集。

特点

数据集聚焦于劳动利用不足的综合率（LU4），并根据性别与残疾状态进行细致分层，提供了‘总、男性、女性’三类性别视角以及‘残疾状态：总计’的分类维度。其核心指标LUU_XLU4_SEX_DSB_RT以百分比形式呈现，辅以详尽的数据溯源信息，如数据来源编码（source）与标注（source.label）、观测状态标志（obs_status）以及注释说明（note_indicator），显著增强了数据的可追溯性与质量透明度。

使用方法

该数据集以HuggingFace Datasets库的形式发布，用户可通过`load_dataset`函数便捷加载，并利用`to_pandas()`方法转换为DataFrame进行后续分析。典型的应用场景包括：按国家过滤进行区域劳动力市场对比，按时间序列绘制单一指标（如LUU_XLU4_SEX_DSB_RT）的变化趋势图，或通过数据透视表构建年份×国家的观测值矩阵，以支持多维度、跨时期的劳动利用效率比较研究。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年整理发布，并经Electric Sheep Europe重封装后提供，聚焦于欧洲地区劳动力利用不足的复合率（LU4），按性别和残疾状态进行细分。其核心研究问题在于通过ILOSTAT这一全球领先的劳动统计数据库，捕捉2005年至2025年间英国、摩尔多瓦和波兰三国的劳动力市场结构性失衡现象，尤其关注残疾人群体的就业边缘化程度。作为对传统失业率的补充指标，LU4综合反映了失业、时间相关就业不足和潜在劳动力规模，为政策制定者提供了更精准的劳动力闲置度量。该数据集的影响力体现在其能够支持跨国比较研究，推动包容性劳动政策的制定，并在劳动经济学、社会福利评估及可持续发展目标监测中发挥关键作用。

当前挑战

本数据集解决的领域问题在于传统失业率指标未能全面刻画劳动力市场的复杂性，尤其忽视了因技能错配、工时不足或被动退出劳动力市场所导致的资源浪费，而LU4指标通过整合多维度的利用不足情形，提供了更完整的劳动闲置图景。数据构建过程中的挑战包括：不同国家间劳动力调查方法论的差异化，如英国采用劳动力调查而波兰依赖行政记录，导致数据口径不一；ILOSTAT虽通过国际劳工统计学家会议定义进行协调，但如'断点'（break in series）状态标签所示，方法论修订会破坏时序连续性；此外，样本量较小（仅291条观测）且覆盖国家有限，使得推断结果易受个别国家异常扰动，削弱了泛化能力。

常用场景

经典使用场景

在欧洲劳动经济学与社会政策研究领域，劳动资源利用率的精准测度一直是评估劳动力市场健康程度的核心议题。该数据集基于国际劳工组织ILOSTAT数据库，整合了英国、摩尔多瓦与波兰三国自2005年至2025年间按性别与残疾状况分类的劳动利用不足综合率（LU4）观测值共计291条。研究者可借助该数据开展面板数据分析，通过时间序列建模追踪欧盟成员国中残障群体与性别维度的劳动参与率波动规律，亦可利用其多维度分类特征构建回归模型，量化社会保障政策、就业促进法案实施前后对弱势群体就业状况的干预效应。数据集提供的观测状态标识与来源注解字段，为元分析中处理数据断点修正和季节性调整提供了关键依据。

衍生相关工作

该数据集作为ILOSTAT官方数据的欧洲子集重包装成果，直接催生了一系列围绕劳动力利用不足多维测度的拓展研究。其中代表性工作包括基于LU4指标改进传统菲利普斯曲线模型的劳动市场松弛度测算框架，以及结合性别平等指数与残疾就业配额制度实施强度的跨国政策效果面板分析。研究者还以该数据集为基准，开发了针对观测状态标签的元学习异常检测算法，用于自动识别劳动统计中的方法断点与口径不一致问题。其在HuggingFace平台上采用的标准化Parquet存储与统一数据集卡片范式，亦成为后续欧洲公共统计数据集重包装工程的参考范本，推动了劳动力市场数据科学的可重复性建设。

数据集最近研究

最新研究方向

该数据集聚焦于欧洲国家按性别和残疾状态细分的工作利用不足综合率（LU4），为劳动力市场不平等研究提供了关键数据支撑。在包容性就业与可持续发展目标（SDG）备受关注的当下，该数据助力分析残疾人群体的就业脆弱性，揭示性别与残疾状态交叉维度下的劳动力资源错配现象。结合ILOSTAT的标准化统计方法，研究者可追踪2005至2025年间英国、摩尔多瓦和波兰的长期趋势，评估社会保护政策与残障包容性就业措施的实效性。其时间序列特性也支持预测建模，与欧盟《残疾人权利战略》及全球劳动力治理的数字化转型相呼应，为制定更具公平性的劳动力政策奠定实证基础。