electricsheepeurope/europe-ilo-emp-xtru-sex-edu-nb-time-related-underemployment-by-sex-and-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 by sex and education (thousands) | Europe (ILOSTAT)" --- # Time-related underemployment by sex and education (thousands) | Europe (ILOSTAT) 🇪🇺 **24,660 observations** · **37 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **24,660 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_NB) - **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_NB` 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_NB` — Time-related underemployment by sex and education (thousands) ## 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_NB` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment by sex a…` | | `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) | `38.876` | | `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-nb-time-related-underemployment-by-sex-and-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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_xtru_sex_edu_nb_time_related_underemployment_by_sex_and_education_2025, title = {Time-related underemployment by sex and education (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-xtru-sex-edu-nb-time-related-underemployment-by-sex-and-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 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_EDU_NB_This dataset contains 24,660 observations of Time-related underemployment data across 37 Europe countries, spanning 1991–2025, covering 1 distinct indicator (EMP_XTRU_SEX_EDU_NB). It measures employment insufficiency due to inadequate working hours in the labor market, disaggregated by sex (total, male, female) and education level. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, presented in tabular format with fields such as country code, year, observed value, data source, and quality flags. It is suitable for tasks like tabular classification, regression, and time-series forecasting, and has been harmonized according to International Conference of Labour Statisticians (ICLS) definitions, providing comprehensive coverage for European labor market analysis and economic research.

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，通过REST API直接提取指标EMP_XTRU_SEX_EDU_NB的原始记录，并筛选欧洲ISO3国家代码，最终由Electric Sheep Europe重新打包发布。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查等微观数据进行标准化处理，来源信息以标签形式保留在数据列中，确保可追溯性。数据集覆盖37个欧洲国家，时间跨度为1991年至2025年，共包含24,660条观测值，以Parquet格式存储，便于机器学习工作流直接加载。

特点

数据集聚焦于与时间相关的就业不足现象，按性别和教育程度进行细分，提供了丰富的分类维度。其中性别变量涵盖总计、男性和女性三类，教育分类则通过classif1字段呈现。每条记录包含国家、来源、指标、观测值及质量标志等字段，并附有详细的注释说明数据断点或非标准分类。数据以年度频率为主，囊括了从早期转型经济体到成熟福利国家的广泛地理样本，为比较劳动市场性别与教育差异提供了长时序面板数据。

使用方法

研究者可通过HuggingFace的datasets库以一行代码加载数据集，并转换为Pandas数据框进行灵活操作。典型用法包括按国家代码筛选特定经济体、绘制单一指标的时间序列趋势、或将数据透视成国家×年份矩阵以分析跨国差异。数据集支持表格分类、回归及时间序列预测等任务，用户亦可结合obs_status等质量标志进行稳健性检验。使用时应遵循CC-BY-4.0许可，并同时引用国际劳工组织与Electric Sheep Europe的贡献。

背景与挑战

背景概述

在国际劳工组织（ILO）致力于推动全球体面劳动与可持续发展目标的宏观背景下，ILOSTAT作为其核心统计数据库，长期为劳动经济学与政策研究提供权威数据支撑。该数据集由Electric Sheep Europe于2025年基于ILOSTAT REST API重新封装发布，覆盖1991至2025年间37个欧洲国家，共计24,660条观测记录，聚焦于按性别与教育程度分类的与时间相关的就业不足指标。其核心研究问题在于揭示欧洲劳动力市场中就业不足的性别与教育维度差异及其时序演变，为评估劳动力利用效率、制定针对性就业政策提供了不可或缺的量化基础，对劳动经济学、社会政策分析及机器学习应用具有显著影响力。

当前挑战

该数据集所应对的领域问题在于，与时间相关的就业不足作为衡量劳动力未充分使用的关键指标，其跨国家、跨性别与跨教育水平的精确测度长期受制于定义差异与数据稀疏性，传统上难以进行一致的跨国比较与趋势预测。构建过程中面临的挑战尤为突出：各国劳动力调查的抽样方法、教育分类标准及就业不足界定存在异质性，ILO虽依据国际劳工统计学家会议（ICLS）决议进行协调，但原始数据的非标准化仍导致部分观测值缺失或标注为不可靠；此外，1991至2025年的长时间跨度涵盖多次方法论修订，序列断裂与来源切换进一步增加了数据融合与质量控制的复杂度，对时间序列建模与因果推断构成实质性障碍。

常用场景

经典使用场景

在劳动经济学与就业研究的经典范式中，该数据集常被用于刻画欧洲各国时间相关不充分就业的性别与教育维度分布特征。研究者依托其1991至2025年的长时序面板结构，可构建按性别与教育水平分层的就业质量评估模型，进而揭示不同社会群体在劳动力市场中的边缘化程度。借助时间序列分析与跨国比较框架，该数据集亦服务于识别不充分就业的季节性波动与结构性变迁，为理解欧洲一体化进程中劳动力市场分化提供了微观层面的量化基础。

衍生相关工作

围绕该数据集，学术界与政策研究机构衍生出一系列经典工作。例如，基于其构建的欧洲不充分就业指数被用于检验奥肯定律在就业质量维度的适用性；多篇文献利用其面板结构进行性别就业差距的分解分析，拓展了布林德-奥克斯瓦卡分解方法的应用边界。部分研究将其与欧洲社会调查数据匹配，探究教育错配与不充分就业的因果链条。这些工作不仅丰富了劳动统计数据的二次利用范式，也推动了ILOSTAT系列指标在机器学习与预测建模中的标准化应用。

数据集最近研究

最新研究方向

在全球劳动力市场结构性变革的背景下，该数据集所承载的性别与教育维度的时间相关不充分就业信息，正成为劳动经济学与计量社会学交叉领域的前沿议题。近期研究聚焦于运用时间序列预测与面板回归方法，探究欧洲各国不同教育层级劳动力在非自愿兼职与工时不足状态下的动态演化机制，尤其关注2008年金融危机与新冠疫情冲击下性别差异的异质性响应。与此同时，机器学习分类模型被引入以识别教育水平对不充分就业风险的边际效应，为欧盟就业政策精准化提供实证基础。该数据集凭借其长时段、跨国别、细粒度的观测结构，亦为评估劳动力市场性别平等进展与教育回报衰减提供了关键量化依据，推动了区域就业治理从总量调控向结构性干预的范式转型。