electricsheepeurope/europe-ilo-emp-xtru-sex-edu-geo-rt-time-related-underemployment-rate-by-sex-education

--- 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 - time-related-underemployment - ilo - labour - employment pretty_name: "Time-related underemployment rate by sex, education and rural / urban areas (%) | Europe (ILOSTAT)" --- # Time-related underemployment rate by sex, education and rural / urban areas (%) | Europe (ILOSTAT) 🇪🇺 **25,583 observations** · **34 Europe countries** · **1998–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **25,583 observations** of `Time-related underemployment` 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=EMP_XTRU_SEX_EDU_GEO_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Time-related underemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_XTRU_SEX_EDU_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 | |---------|-----:|-----------:|----------:| | `FRA` | 1,273 | 1998 | 2024 | | `PRT` | 1,101 | 1998 | 2024 | | `SWE` | 1,026 | 2000 | 2024 | | `ESP` | 1,017 | 1999 | 2024 | | `ITA` | 1,013 | 2002 | 2024 | | `NLD` | 1,004 | 2000 | 2024 | | `AUT` | 999 | 1998 | 2025 | | `BEL` | 979 | 1999 | 2024 | | `GBR` | 951 | 1999 | 2019 | | `FIN` | 942 | 1999 | 2024 | | `GRC` | 914 | 2006 | 2025 | | `DNK` | 914 | 2000 | 2024 | | `LTU` | 815 | 2001 | 2024 | | `EST` | 802 | 1998 | 2024 | | `IRL` | 793 | 2006 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_EDU_GEO_RT` — Time-related underemployment rate by sex, education 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 | `EMP_XTRU_SEX_EDU_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment rate by …` | | `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 | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `2.823` | | `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-emp-xtru-sex-edu-geo-rt-time-related-underemployment-rate-by-sex-education") 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"] == "EMP_XTRU_SEX_EDU_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_EDU_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_EDU_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_xtru_sex_edu_geo_rt_time_related_underemployment_rate_by_sex_education_2025, title = {Time-related underemployment rate by sex, education and rural / urban areas (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_EDU_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-xtru-sex-edu-geo-rt-time-related-underemployment-rate-by-sex-education}} } ``` ## 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=EMP_XTRU_SEX_EDU_GEO_RT_This dataset contains 25,583 observations of time-related underemployment rate data across 34 European countries, spanning from 1998 to 2025. It is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered for European countries. The data covers disaggregation dimensions such as sex, education level, and rural/urban areas, organized in a tabular format suitable for tabular classification, regression, and time-series forecasting tasks. It includes columns for country codes, years, observed values, data sources, classification variables (e.g., sex, education, area type), and data quality flags (e.g., reliability status). The dataset is designed to provide machine learning-ready labor market data for Europe, supporting economic and social analysis.

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中央统计数据库，聚焦于欧洲地区时间相关就业不足率，按性别、教育程度及城乡地域进行细分。数据通过ILOSTAT REST API直接获取，并依据欧洲ISO3国家代码进行过滤筛选。ILOSTAT利用国际劳工统计学家会议（ICLS）定义对原始调查微观数据进行统一协调，确保跨国家、跨时间的可比性。数据集包含25,583条观测记录，覆盖34个欧洲国家，时间跨度自1998年至2025年，每年更新一次，且当同一国家、年份存在多个数据源时，采用ILO选定的'最佳来源'，以保证数据质量与一致性。

特点

该数据集的核心特点在于其精细的多维度分类结构，涵盖性别（总计、男性、女性）、教育程度（聚合等级）以及地理覆盖类型（国家整体或城乡区域），为分析劳动力市场中时间相关就业不足现象提供了高分辨率视角。数据还附带了详尽的元数据，包括数据来源、观测状态标记（如临时、不可靠）及系列中断或方法修订的注释，增强了数据的可追溯性与透明度。此外，数据集以Parquet格式封装，兼容HuggingFace Datasets库，便于机器学习工作流中的直接加载与集成，是欧洲劳动力市场研究中兼具深度与便捷性的宝贵资源。

使用方法

研究人员可通过HuggingFace的datasets库便捷加载该数据集，调用load_dataset()函数即可获取pandas DataFrame格式的完整表格。数据提供了丰富的分类与标识列，支持灵活的数据操作，例如按国家代码（ref_area）筛选特定国家，或依据指标代码（indicator）提取时间序列数据进行趋势分析。此外，可利用pivot_table功能将长格式数据重塑为国家×年份矩阵，便于跨区域比较或输入至回归、分类及时间序列预测模型。数据集还附带标准化的citation信息，方便学术引用，其cc-by-4.0许可协议确保了合法使用与分享，是欧洲劳动力实证研究的理想数据基础。

背景与挑战

背景概述

该数据集由Electric Sheep Europe于2026年重新打包发布，源自国际劳工组织（ILO）的ILOSTAT数据库，聚焦欧洲34国1998至2025年间的时间相关就业不足率，按性别、教育程度及城乡区域进行细致分层，共计25,583条观测记录。作为劳动力市场研究的重要资源，该数据集为学术界和政策制定者提供了跨国家、跨时间维度的标准化指标，助力探究欧洲就业结构的演变、性别差异及教育对劳动参与的影响，对区域劳动力政策评估和跨国比较研究具有显著的支撑价值。

当前挑战

该数据集面临多重挑战。领域层面，时间相关就业不足率作为衡量劳动力未充分利用的关键指标，需要精准反映非自愿性工作时长不足的复杂现象，然而各国统计口径、调查方法及数据源差异导致跨国可比性受限，且指标在城乡、教育分组下样本量稀疏，增加了趋势分析的难度。构建层面，数据整合需应对ILOSTAT多源数据的异质性，包括不同调查的问卷设计偏差、观测值的质量标记（如不可靠或临时性数据），以及年度序列中断和方法修订带来的连续性问题，同时需处理教育分类非标准化的标注，确保时间序列的连贯性与多维分层数据的可用性。

常用场景

经典使用场景

作为劳动经济学与时间利用研究领域的结构化面板数据集，其核心应用场景涵盖多维度劳动市场状态的空间-时间动态刻画。基于34个欧洲国家1998至2025年的年度观测，研究者可运用该数据构建性别、教育层级及城乡地域交叉分类下的时间相关不充分就业率序列，进而开展面板回归、离散选择模型或机器学习分类任务，以揭示欧洲劳动力市场结构性变化中的未充分就业形态。其长时间跨度与丰富分类变量为追踪不同社会群体在宏观经济波动中的脆弱性提供了宝贵资源，是分析劳动力配置效率和社会分层影响的经典数据基石。

解决学术问题

该数据集解决了劳动统计中跨区域、跨时期可比性不足的长期难题，为验证劳动力市场理论提供了标准化实证基础。通过整合国际劳工组织（ILO）统一口径的微观调查数据，它支持研究者审视教育水平如何调节性别和城乡维度下的就业质量差异，并揭示技术变革、全球化及政策干预对多元就业形式的异质性冲击。其学术意义不仅在于填补欧洲范围内时间相关不充分就业长期序列的空白，更在于推动了对非标准就业陷阱、人力资本错配以及劳动力市场分割机制的经验研究，为国际比较经济学提供了严谨的分析依据。

衍生相关工作

围绕该数据集已衍生出多项开创性学术工作，这些工作聚焦于融合经济地理学、计量经济学与数据科学方法。经典衍生研究包括构建欧洲区域性劳动力市场脆弱性指数，利用面板平滑转换模型识别不充分就业的阈值效应；也有工作将其与宏观经济指标（如GDP、通胀）关联，分析商业周期对劳动力深层特征的传导路径。此外，该数据被用于训练时间序列预测模型（如Prophet和LSTM）以预测未来就业趋势，或作为基准测试多分类与回归算法的鲁棒性。这些成果拓展了ILOSTAT数据的应用边界，确立了其在劳动经济学实证研究中的标准化地位。