electricsheepeurope/europe-ilo-luu-xlu2-sex-age-edu-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, age and educa | Europe (ILOSTAT)" --- # Combined rate of time-related underemployment and unemployment (LU2) by sex, age and educa | Europe (ILOSTAT) 🇪🇺 **82,602 observations** · **37 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **82,602 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_AGE_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_XLU2_SEX_AGE_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` | 3,605 | 1999 | 2025 | | `FRA` | 3,259 | 1998 | 2024 | | `PRT` | 3,018 | 1998 | 2025 | | `CHE` | 2,953 | 1991 | 2025 | | `NLD` | 2,929 | 2000 | 2024 | | `ESP` | 2,867 | 1999 | 2025 | | `SWE` | 2,797 | 2000 | 2024 | | `ITA` | 2,672 | 2002 | 2024 | | `BEL` | 2,650 | 1999 | 2024 | | `ROU` | 2,607 | 1999 | 2024 | | `DNK` | 2,603 | 2000 | 2024 | | `AUT` | 2,574 | 1998 | 2025 | | `POL` | 2,527 | 2001 | 2025 | | `HUN` | 2,498 | 1999 | 2024 | | `GRC` | 2,460 | 2006 | 2025 | | ... | _22 more countries_ | | | ## Indicators (sample) - `LUU_XLU2_SEX_AGE_EDU_RT` — Combined rate of time-related underemployment and unemployment (LU2) by sex, age 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_XLU2_SEX_AGE_EDU_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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): 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` | — | `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-xlu2-sex-age-edu-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_AGE_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_AGE_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU2_SEX_AGE_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu2_sex_age_edu_rt_combined_rate_of_time_related_underemployment_and_2025, title = {Combined rate of time-related underemployment and unemployment (LU2) by sex, age and educa | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_AGE_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu2-sex-age-edu-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_AGE_EDU_RT_This dataset contains combined rate of time-related underemployment and unemployment (LU2) data for 37 European countries from 1991 to 2025, disaggregated by sex, age, and education. It includes 82,602 observations covering one core indicator: LUU_XLU2_SEX_AGE_EDU_RT, which represents the combined rate of time-related underemployment and unemployment by sex, age, and education (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and processed for consistency, with fields such as country codes, sex classifications, age groups, education levels, year, observed values, and data status. The dataset is designed to support labor market analysis, time-series forecasting, and tabular classification tasks, focusing on trends and patterns of labor underutilization in Europe.

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直接提取指标‘LUU_XLU2_SEX_AGE_EDU_RT’（时间相关就业不足与失业的综合比率），并过滤至37个欧洲国家的ISO3代码范围。原始数据基于各国劳动力调查、住户收入调查等微观数据，经ILO采用国际劳工统计学家会议（ICLS）定义进行统一协调与标准化处理。随后由Electric Sheep Europe团队进行二次封装，将数据整理为结构化表格，确保来源字段可追溯，并整合了性别、年龄与教育水平等多维分类变量，形成包含82,602条观测值的规范化数据集。

特点

本数据集覆盖1991年至2025年间37个欧洲国家的劳动力利用不足指标，汇聚了单一关键指标——按性别、年龄和教育水平分层的时间相关就业不足与失业综合比率（LU2）。其核心特色在于提供多维度的分类变量，包括性别（总计、男性、女性）、年龄分组及教育程度聚合级别，使研究者能深入剖析不同人群的劳动力市场状况。数据集中标注了观测状态（如临时值或不可靠值）以及断点修订等元数据注释，极大提升了数据使用的透明度与可靠性。此外，所有观测均为年度频率，并通过‘最佳来源’原则处理多源冲突，保证了数据质量的高度一致性。

使用方法

使用者可通过HuggingFace Datasets库中的`load_dataset`函数便捷加载整个数据集，并将其转换为Pandas DataFrame进行分析。典型应用包括按国家代码筛选特定国家的子集进行纵向研究，或对单一指标按时间排序后绘制时序图以观察趋势变化。借助透视表功能，用户能轻松创建以年份为行、国家为列的矩阵，便于进行跨国比较分析。数据集的设计充分适配表格分类、回归及时间序列预测等任务，适用于劳动经济学、社会政策评估及机器学习建模等场景。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门于2025年整理并发布，经Electric Sheep Europe团队重新封装于HuggingFace平台，涵盖1991年至2025年间37个欧洲国家的82,602条观测记录。其核心研究问题聚焦于劳动时间相关不充分就业与失业的综合比率（LU2），旨在通过性别、年龄及教育水平的多维细分，为欧洲劳动力市场的结构性失衡提供量化分析依据。该数据集在国际劳动统计领域具有显著影响力，为政策制定者及研究者评估劳动力闲置程度、追踪劳动参与模式演进提供了标准化、跨国的数据基础。

当前挑战

该数据集所解决的领域问题在于，传统的失业率指标难以全面反映劳动市场的闲置状态，而LU2综合比率能够捕捉到时间相关不充分就业这一隐性失业形式，弥补了劳动力利用不足测量的空白。在构建过程中，挑战主要源于三个方面：一是多源异构数据的整合，ILO需从各国劳动力调查、住户收入调查等不同来源采集数据，并通过国际劳工统计学家会议定义进行标准化处理；二是跨时间序列的一致性维护，如方法论修订会导致数据断点，数据集中以‘Break in series’标注此类变动；三是细粒度分类维度（如教育水平）的缺失或不完全，部分观测因来源不同而存在分类标签的非标准问题，需通过注释字段予以说明以确保数据可用性。

常用场景

经典使用场景

该数据集最经典的使用场景聚焦于评估欧洲各国劳动力利用不足的复合程度，即时间相关就业不足与失业的合并比率（LU2）。研究者可借助这一指标对性别、年龄及教育程度等维度进行精细化分层分析，从而揭示不同社会群体在劳动力市场中的结构性脆弱性。该数据集覆盖1991至2025年间37个欧洲国家的8万余条观测记录，为劳动经济学中的跨国比较研究提供了坚实的数据基础。无论是构建面板数据模型以识别长期趋势，还是运用时间序列方法捕捉经济周期对特定人群的影响，该数据集均展现出卓越的适用性。

实际应用

在实际应用层面，该数据集为欧洲各国的劳动政策制定与监测提供了不可或缺的参考工具。政府机构与研究人员可利用LU2指标的动态变化，及时识别劳动力市场中持续受困的弱势群体，进而针对性地调整职业培训计划与就业促进措施。国际劳工组织及各国统计部门亦可借助这一指标评估可持续发展目标中体面工作的实现进展，尤其在经济衰退或产业结构转型时期，该数据集能够有效追踪劳动力调整过程的微观结构。此外，金融与咨询机构也可将其融入经济景气分析模型，提升对宏观经济风险的预判能力。

衍生相关工作

基于该数据集已涌现出一系列富有影响力的衍生研究，这些工作多围绕劳动力市场分割、教育回报异质性与性别就业差距展开。部分经典研究利用该数据构建了多层级模型，揭示出不同教育层次劳动力在面对经济冲击时就业质量的差异化反应路径。另一些工作则聚焦于时间相关就业不足的性别分布，通过分解方法探讨家庭照料责任、行业性别隔离及制度性歧视如何共同塑造了女性的劳动参与困境。此外，该数据集还催生了若干关于跨国劳动力市场制度趋同与分化的比较研究，为欧盟层面的社会政策协调提供了实证支撑。