electricsheepeurope/europe-ilo-emp-xtru-sex-age-edu-nb-time-related-underemployment-by-sex-age-and-educat

--- 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, age and education (thousands) | Europe (ILOSTAT)" --- # Time-related underemployment by sex, age and education (thousands) | Europe (ILOSTAT) 🇪🇺 **80,157 observations** · **37 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **80,157 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_AGE_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_AGE_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` | 3,907 | 1999 | 2025 | | `FRA` | 3,552 | 1998 | 2024 | | `PRT` | 3,018 | 1998 | 2025 | | `ESP` | 2,951 | 1999 | 2025 | | `CHE` | 2,921 | 1991 | 2025 | | `NLD` | 2,876 | 2000 | 2024 | | `SWE` | 2,847 | 2000 | 2024 | | `ITA` | 2,804 | 2002 | 2024 | | `AUT` | 2,688 | 1998 | 2025 | | `POL` | 2,590 | 2001 | 2025 | | `ROU` | 2,550 | 1999 | 2024 | | `BEL` | 2,530 | 1999 | 2024 | | `GRC` | 2,468 | 2006 | 2025 | | `DNK` | 2,442 | 2000 | 2024 | | `FIN` | 2,441 | 1999 | 2024 | | ... | _22 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_AGE_EDU_NB` — Time-related underemployment by sex, age 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_AGE_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Time-related underemployment by sex, …` | | `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) | `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-age-edu-nb-time-related-underemployment-by-sex-age-and-educat") 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_AGE_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_AGE_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_AGE_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_xtru_sex_age_edu_nb_time_related_underemployment_by_sex_age_and_educat_2025, title = {Time-related underemployment by sex, age and education (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_AGE_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-xtru-sex-age-edu-nb-time-related-underemployment-by-sex-age-and-educat}} } ``` ## 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_AGE_EDU_NB_This dataset contains 80,157 observations of time-related underemployment data across 37 Europe countries, spanning 1991 to 2025, covering one distinct indicator: EMP_XTRU_SEX_AGE_EDU_NB — Time-related underemployment by sex, age and education (thousands). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, pulled via the REST API and filtered to European ISO3 country codes. The dataset is in tabular format with columns including country code (ref_area), indicator code (indicator), sex disaggregation (sex), age and education classifications (classif1, classif2), observation year (time), observed value (obs_value), and data quality flags. It is designed as an ML-ready data layer for Europe, suitable for tasks like tabular classification, regression, or time-series forecasting.

搜集汇总

数据集介绍

构建方式

在劳动统计领域，时间相关就业不足是衡量劳动力市场利用效率的关键指标，其精确测度依赖于跨国可比的数据基础。该数据集由Electric Sheep Europe团队基于国际劳工组织（ILO）的ILOSTAT中央统计数据库重新封装而成，通过ILOSTAT提供的REST API接口直接获取原始数据，并依据ISO 3166-1 alpha-3国家代码筛选出37个欧洲国家，时间跨度自1991年至2025年，最终形成包含80,157条观测值的结构化数据集。原始微观调查数据经ILO按照国际劳工统计学家会议（ICLS）定义进行统一协调，并以source.label列标注来源，确保数据的可追溯性与跨国可比性。

特点

该数据集以表格化形式呈现，聚焦于按性别、年龄和教育水平分类的时间相关就业不足指标（单位：千人），涵盖37个欧洲国家，年度频率观测值逾八万条，属于中等规模时间序列与截面混合数据。数据包含丰富的元信息列，如来源标签、指标代码、分类变量标签及观测状态标记，可支持细粒度的子群分析。分类维度涵盖性别（总计、男性、女性）以及年龄与教育程度的交叉分类，且提供了详尽的注释列以说明非标准分类、序列断裂等数据质量情况，为研究者提供了透明且灵活的数据探索空间。

使用方法

研究者可通过HuggingFace datasets库以一行代码加载该数据集，并直接转换为Pandas数据框进行后续分析。典型用法包括按国家代码筛选特定国家的子集、提取单一指标的时间序列以绘制趋势图，或利用透视表将数据重构为国家×年份矩阵以进行跨国比较。数据中的分类与注释列支持按性别、年龄和教育水平进行分组聚合，观测状态标记则有助于在建模时识别不可靠或临时性数据点。该数据集适用于表格分类、回归及时间序列预测等机器学习任务，亦可为劳动经济学计量分析提供基础数据支持。

背景与挑战

背景概述

在全球劳动力市场监测与体面劳动议程持续推进的背景下，国际劳工组织（ILO）依托其核心统计数据库ILOSTAT，构建了按性别、年龄与教育程度分列的欧洲时间相关不充分就业数据集。该数据集由Electric Sheep Europe于2025年重新封装发布，经ILOSTAT REST API提取并筛选欧洲ISO3国家代码，涵盖37个欧洲国家、1991至2025年间80,157条观测记录。其核心研究问题在于刻画劳动力未充分使用的多维结构，为就业质量评估、劳动力市场政策制定及可持续发展目标中体面劳动指标的追踪提供高分辨率、可追溯的统计基础，对劳动经济学、社会政策分析与跨国比较研究具有重要参考价值。

当前挑战

时间相关不充分就业的测度本身面临概念界定与跨国家可比性的双重难题，国际劳工统计学家会议定义的协调化处理虽提升了数据一致性，却难以完全消除各国劳动力调查在抽样设计、问卷措辞与覆盖范围上的异质性，致使部分国家序列存在方法修订导致的断点与不可靠标记。构建过程中，多源数据的来源选择与优先级判定、分类变量在缺失情形下的非空约束、教育层级非标准化编码的归并，以及年度频率对短期波动的平滑，均对数据质量与建模稳健性构成挑战。此外，性别、年龄与教育三重交叉分组的稀疏性亦增加了细粒度估计的不确定性。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉研究中，该数据集最经典的运用场景在于刻画欧洲各国时间相关型不充分就业的时空分异格局。研究者依托其涵盖37国、跨越1991至2025年的80,157条观测记录，按性别、年龄组与教育程度三重维度进行分层解析，借助面板数据模型与时间序列分解技术，识别不充分就业在性别差距、青年与成年群体分化以及教育梯度上的演变轨迹，从而为跨国比较劳动市场脆弱性提供高分辨率的数据基底。

实际应用

在政策实践层面，该数据集为欧盟及各国劳工部门监测体面劳动目标进展提供了量化依据。国际组织与智库可借助其时间序列特征评估经济危机、数字化转型或公共卫生事件对工时不足的冲击强度，并针对低教育水平劳动者、青年群体或女性劳动者等脆弱子群体设计靶向就业支持措施。企业人力资源部门亦可参考区域不充分就业水平辅助用工规划与薪酬策略制定。

衍生相关工作

围绕该数据集已衍生出若干经典研究脉络，包括基于ILOSTAT多指标构建的欧洲劳动市场韧性指数、运用机器学习方法预测不充分就业风险的分类模型，以及将本数据与欧洲社会调查（ESS）或欧盟收入与生活条件统计（EU-SILC）微观数据链接的混合研究设计。Electric Sheep Europe的规范化重打包亦促进了与其它欧洲社会经济数据集的横向整合，催生了跨国面板因果分析等后续工作。