electricsheepeurope/europe-ilo-emp-xtru-sex-edu-mts-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 marital status (%) | Europe (ILOSTAT)" --- # Time-related underemployment rate by sex, education and marital status (%) | Europe (ILOSTAT) 🇪🇺 **13,199 observations** · **18 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **13,199 observations** of `Time-related underemployment` data across **18 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_MTS_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_MTS_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 18 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 1,194 | 1991 | 2025 | | `ESP` | 1,170 | 2000 | 2025 | | `GBR` | 1,109 | 2005 | 2025 | | `FRA` | 1,100 | 2005 | 2024 | | `PRT` | 868 | 2002 | 2020 | | `CZE` | 845 | 2002 | 2024 | | `AUT` | 828 | 2004 | 2025 | | `MDA` | 716 | 2006 | 2025 | | `GRC` | 713 | 2006 | 2020 | | `ALB` | 680 | 2007 | 2024 | | `IRL` | 672 | 2009 | 2023 | | `SVK` | 646 | 2001 | 2020 | | `BIH` | 621 | 2006 | 2020 | | `SRB` | 566 | 2008 | 2020 | | `MKD` | 429 | 2014 | 2025 | | ... | _3 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_EDU_MTS_RT` — Time-related underemployment rate by sex, education and marital status (%) ## 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_MTS_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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-mts-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_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_EDU_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_EDU_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_xtru_sex_edu_mts_rt_time_related_underemployment_rate_by_sex_education_2025, title = {Time-related underemployment rate by sex, education and marital status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_EDU_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-xtru-sex-edu-mts-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_MTS_RT_This dataset contains 13,199 observations across 18 European countries from 1991 to 2025, focusing on the indicator Time-related underemployment rate by sex, education and marital status (%). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and filtered to European country codes. It is presented in a tabular format with columns such as country code (ref_area), indicator code (indicator), year (time), observed value (obs_value), and disaggregation dimensions like sex, education, and marital status. The data is annual in frequency, harmonized by ILO standards, and includes quality flags (e.g., observation status). Repackaged by Electric Sheep Europe, it serves as a machine learning-ready data layer for Europe, suitable for tasks like tabular classification, regression, and time-series forecasting.

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，经由Electric Sheep Europe重新打包，聚焦于欧洲18个国家的时间相关就业不足率，按性别、教育程度及婚姻状况进行细分。数据通过ILOSTAT REST API直接抽取，并依据ISO3国家代码筛选欧洲地区，涵盖了1991年至2025年的年度观测值，共计13,199条记录。ILOSTAT依据国际劳工统计学家会议（ICLS）的定义对各国家庭调查、劳动力调查等原始微观数据进行统一协调与处理，确保跨国家数据的可比性。数据集中每一条记录均包含国家、性别、教育分类、婚姻状态、观测年份、指标值及来源等详细注释，便于进行精确的层级化分析。

特点

该数据集的核心优势在于其精细的维度划分与时间跨度，不仅提供了总体指标，还按性别（男、女、总）以及教育水平和婚姻状况进行了多维度交叉呈现，使用户能够深入洞察不同亚群体间的就业不足差异。同时，数据集保留了ILOSTAT的原始来源标记与观测状态标志（如临时值或不可靠值），增强了数据的可追溯性与透明度。此外，其年度频率覆盖长达35年，为长期趋势分析与时序建模提供了坚实基础，尤其适合研究劳动力市场结构性变化与政策影响评估。

使用方法

数据集无缝集成于HuggingFace生态系统，用户可通过`load_dataset`接口快速加载为Pandas DataFrame，进而灵活开展探索性数据分析。针对单一国家的时序分析，可直接依据`ref_area`列筛选；对于特定指标的趋势可视化，则可通过`indicator`列过滤并绘制时间序列图。该数据集同时支持构建国家×年份的透视表，便于面板数据回归或机器学习中的特征工程。此外，由于包含分类与回归任务的自然属性，其亦可作为表格分类、回归或时间序列预测任务的基准数据，适应从学术研究到政策分析的多场景需求。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门发布，依托其核心统计数据库ILOSTAT，由Electric Sheep Europe于2025年重新整理并发布于HuggingFace平台，涵盖1991年至2025年间18个欧洲国家的13199条观测记录，专注于按性别、教育程度和婚姻状况细分的时间相关就业不足率。该数据集的创建旨在响应劳动力市场研究中对于精细分层指标的迫切需求，为分析欧洲各国就业质量、劳动力市场灵活性及社会人口结构对就业不足的影响提供标准化、可比的数据基础。作为ILOSTAT框架下的一个子集，该数据集继承了ILO在劳动力统计领域数十年的方法学积累，通过国际劳工统计学家会议（ICLS）定义统一数据口径，成为跨国比较研究和时间序列分析的重要支撑，对劳动经济学、社会政策评估及可持续发展目标（SDG）进展监测具有显著影响力。

当前挑战

该数据集所面临的挑战多维且复杂。领域层面，劳动力市场的动态性使得时间相关就业不足率受经济周期、政策干预及社会变迁的多重影响，准确捕捉其长期趋势与结构性变化需要精细化的时间序列分析方法，而数据的时间跨度与地域覆盖差异（如部分国家起始年份较晚）增加了横向比较的难度。构建过程层面，ILOSTAT整合多国调查微观数据时，需应对不同国家在调查设计、样本规模、定义口径及数据质量上的异质性，尤其是教育程度与婚姻状况的分类标准在各国间存在差异，导致跨国的可比性受限。此外，部分观测值被标记为不可靠（如'U'状态）或存在方法论修订（如断点），要求用户在使用时需谨慎处理数据缺失与统计噪声，以确保分析结论的稳健性。

常用场景

经典使用场景

该数据集作为ILOSTAT官方劳动统计指标在欧洲区域的精细化解构，其经典应用场景聚焦于时间相关不充分就业率的时空演变分析。研究者可依据性别、教育程度及婚姻状况三维交叉分类，构建面板数据模型，运用时间序列预测、分类回归等机器学习范式，探究欧洲各国劳动力市场结构性特征。数据集覆盖18国、横跨1991至2025年的年度观测值，为比较劳动经济学、社会分层研究及欧盟就业政策评估提供了标准化的量尺，使得跨国、跨时期的横向与纵向对比研究得以严谨展开。

衍生相关工作

该数据集的发布促进了多项衍生学术工作的勃兴，包括欧洲劳动力市场分化与趋同的实证研究、时间相关不充分就业的宏观经济决定因素分析，以及基于机器学习的就业脆弱性预测模型。其整合的ILOSTAT溯源信息与元数据，为构建劳动力统计知识图谱和跨库数据融合平台奠定了基础。后续研究可借助此数据开发公平性审计工具，评估不同性别与教育群体在劳动力市场中的均衡性。此外，该数据集作为开放科学实践范本，激励了同类欧洲社会统计数据的统一封装与共享，推动了可复现性研究文化的形成。

数据集最近研究

最新研究方向

该数据集基于国际劳工组织ILOSTAT权威统计，聚焦欧洲18国1991至2025年间按性别、教育及婚姻状况细分的时间相关不充分就业率，为劳动经济学与政策研究提供了精细化的时序面板数据。当前前沿研究侧重于利用此类高粒度分层数据剖析欧洲劳动力市场的结构性分化，尤其关注性别差异、教育回报率与家庭结构对就业质量的影响。在新冠疫情后复苏与欧洲地缘政治动荡背景下，该数据有助于监测非自愿非充分就业的演变趋势，评估积极劳动力市场政策效果，并推动可持续包容性增长目标的实现。其ML-ready的标准化构造亦促进了机器学习在劳动统计预测与社会政策模拟中的应用，为跨国比较与因果推断奠定了坚实基础。