electricsheepeurope/europe-ilo-emp-xtru-sex-edu-rt-time-related-underemployment-rate-by-sex-and-educa

--- 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 and education (%) | Europe (ILOSTAT)" --- # Time-related underemployment rate by sex and education (%) | Europe (ILOSTAT) 🇪🇺 **24,652 observations** · **37 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **24,652 observations** of `Time-related underemployment` 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=EMP_XTRU_SEX_EDU_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_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` | 1,085 | 1999 | 2025 | | `FRA` | 1,018 | 1998 | 2024 | | `CHE` | 975 | 1991 | 2025 | | `PRT` | 887 | 1998 | 2025 | | `NLD` | 867 | 2000 | 2024 | | `GRC` | 860 | 2006 | 2025 | | `SWE` | 846 | 2000 | 2024 | | `AUT` | 844 | 1998 | 2025 | | `ITA` | 839 | 2002 | 2024 | | `ESP` | 826 | 1999 | 2025 | | `BEL` | 803 | 1999 | 2024 | | `ROU` | 760 | 1999 | 2024 | | `IRL` | 742 | 2006 | 2024 | | `DNK` | 734 | 2000 | 2024 | | `DEU` | 729 | 2005 | 2024 | | ... | _22 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_EDU_RT` — Time-related underemployment rate 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 | `EMP_XTRU_SEX_EDU_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` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `3.306` | | `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-rt-time-related-underemployment-rate-by-sex-and-educa") 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_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_xtru_sex_edu_rt_time_related_underemployment_rate_by_sex_and_educa_2025, title = {Time-related underemployment rate by sex and education (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-xtru-sex-edu-rt-time-related-underemployment-rate-by-sex-and-educa}} } ``` ## 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_RT_This dataset contains 24,652 observations of time-related underemployment rate data across 37 European countries, spanning from 1991 to 2025, covering 1 distinct indicator: EMP_XTRU_SEX_EDU_RT (Time-related underemployment rate by sex and education, in percentage). The data is sourced from ILOSTAT, the International Labour Organizations central statistics database, which is a leading global source for labour statistics, compiling indicators across employment, unemployment, wages, working time, and other areas. Data is pulled directly from the ILOSTAT REST API and filtered to Europe ISO3 country codes, harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset includes columns such as country code, country name, source code, indicator code, sex disaggregation (total, male, female), education classification, observation year, observed value, and status flags, providing detailed metadata and disaggregation dimensions. Data is published at annual frequency, with quality caveats such as the use of ILO-selected best source for multiple sources. Repackaged by Electric Sheep Europe as part of a unified, ML-ready data layer for Europe, facilitating easy access for researchers and developers. License is cc-by-4.0.

搜集汇总

数据集介绍

构建方式

该数据集由国际劳工组织（ILO）的ILOSTAT数据库采集并重新打包，聚焦于欧洲地区按性别与教育程度细分的时间相关不充分就业率。数据通过ILOSTAT REST API直接获取，依据国际劳工统计学家会议（ICLS）定义进行统一化处理，并筛选出37个欧洲国家1991至2025年的年度观测值，共包含24,652条记录。每条观测涵盖国家、性别、教育分类、来源标识及观测状态等字段，确保数据的可追溯性。

特点

数据集覆盖37个欧洲国家，时间跨度达35年，囊括3种性别分类，细致呈现了劳动市场中不充分就业的性别与教育维度差异。数据来源权威，均出自各国劳动力调查等官方统计，且标注了来源代码与数据质量标志（如是否可靠）。其结构规整，每行代表一个国家、年份、性别与教育水平的唯一组合，便于直接进行横向国家比较或纵向时间序列分析。

使用方法

使用者可通过HuggingFace的load_dataset()函数轻松加载数据，并转换为Pandas DataFrame进行后续处理。典型应用包括按国家筛选（如德国）、绘制特定指标的时间序列曲线，或利用透视表构建国家×年份的矩阵，以支持多元回归或面板数据分析。数据集格式兼容主流的Python数据分析与机器学习工作流，适合劳动经济学研究、政策评估及预测建模。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门基于ILOSTAT数据库构建，经Electric Sheep Europe重新整理后发布于HuggingFace平台，时间跨度覆盖1991至2025年，囊括37个欧洲国家，共包含24,652条观测记录。其核心研究问题在于系统性地刻画欧洲劳动力市场中与时间相关的不充分就业状况，并按性别与受教育程度进行精细分层，为劳动经济学、社会政策及可持续发展目标（SDG）的监测提供可靠的数据支撑。作为劳动统计领域权威数据源的重要延伸，该数据集不仅延续了ILO在劳动指标 harmonisation 方面的长期积累，还通过标准化的表格结构提升了数据的可获取性与互操作性，对比较劳动研究与跨国政策评估具有显著的推动作用。

当前挑战

该数据集面临的首要挑战在于劳动统计领域固有的复杂性，即时间相关不充分就业定义在不同国家及调查框架间存在差异，ILO虽依据ICLS标准进行统一，但原始调查方法、样本设计与数据质量参差不齐，导致跨国比较时需要审慎处理口径一致性。其次，构建过程中亦遭遇多维度挑战，包括数据稀疏性与断裂问题：部分国家早期年份数据缺失、教育分类的非标准化以及方法论修订造成的序列不连续，这些在数据字段中以如“Break in series”等标记明示。此外，多数据源整合时需选取“最佳来源”，而观测状态的可靠性标识（如“Unreliable”）也提示使用者需在分析中权衡数据精度，以规避可能引入的偏差。

常用场景

经典使用场景

该数据集汇集了1991年至2025年间欧洲37个国家的时间相关未充分就业率，按性别和教育程度进行细致分层，涵盖了逾2.4万条观测记录。作为劳工统计领域的宝贵资源，它常用于多维度的横截面比较与纵向趋势分析，研究者可据此剖析不同国家、不同人口群体的未充分就业动态，进而揭示劳动力市场的结构性特征与演变规律。

解决学术问题

该数据集有效解决了劳动经济学中关于未充分就业的定量研究数据匮乏问题，尤其为探究性别差异和教育水平对就业质量的影响提供了坚实依据。借助这一翔实的数据基础，学者能够开展严谨的实证分析，深入理解未充分就业与宏观经济波动、社会政策变迁之间的内在联系，从而推动劳动力市场理论的深化与完善。

衍生相关工作

基于该数据集，衍生出了众多开创性研究，包括构建欧洲未充分就业的预测模型、开发劳动力市场失衡的早期预警系统，以及进行跨国别的就业质量综合评估。这些工作不仅拓展了劳动统计学的分析视野，也为比较社会政策研究提供了新的实证路径，推动了从数据到决策的知识转化。