electricsheepeurope/europe-ilo-emp-xtru-sex-mts-rt-time-related-underemployment-rate-by-sex-and-marit

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - europe - ilostat - time-related-underemployment - ilo - labour - employment pretty_name: "Time-related underemployment rate by sex and marital status (%) | Europe (ILOSTAT)" --- # Time-related underemployment rate by sex and marital status (%) | Europe (ILOSTAT) 🇪🇺 **7,134 observations** · **19 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **7,134 observations** of `Time-related underemployment` data across **19 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_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_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 19 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 752 | 1991 | 2025 | | `ESP` | 620 | 2000 | 2025 | | `GBR` | 557 | 2005 | 2025 | | `CZE` | 523 | 2002 | 2024 | | `MDA` | 520 | 2006 | 2025 | | `FRA` | 513 | 2005 | 2024 | | `AUT` | 510 | 2004 | 2025 | | `PRT` | 470 | 2002 | 2020 | | `ALB` | 393 | 2007 | 2024 | | `BIH` | 361 | 2006 | 2020 | | `IRL` | 360 | 2009 | 2023 | | `SRB` | 311 | 2008 | 2020 | | `POL` | 261 | 2015 | 2025 | | `MKD` | 255 | 2014 | 2025 | | `BLR` | 213 | 2017 | 2024 | | ... | _4 more countries_ | | | ## Indicators (sample) - `EMP_XTRU_SEX_MTS_RT` — Time-related underemployment rate by sex 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_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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.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_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-mts-rt-time-related-underemployment-rate-by-sex-and-marit") 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_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_xtru_sex_mts_rt_time_related_underemployment_rate_by_sex_and_marit_2025, title = {Time-related underemployment rate by sex and marital status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-xtru-sex-mts-rt-time-related-underemployment-rate-by-sex-and-marit}} } ``` ## 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_MTS_RT_This dataset contains 7,134 observations of time-related underemployment rates by sex and marital status (%) across 19 Europe countries, spanning from 1991 to 2025. The data is sourced from ILOSTAT, the International Labour Organizations central statistics database, which is a leading global source for labour statistics. It covers one distinct indicator, EMP_XTRU_SEX_MTS_RT, and is disaggregated by sex (total, male, female). The dataset includes fields such as country code, year, observed value, data source, and observation status, and is suitable for tabular classification, regression, and time-series forecasting tasks.

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Electric Sheep Europe团队重新封装，聚焦欧洲地区时间相关不充分就业率指标。数据通过ILOSTAT REST API直接抓取，并依据ISO 3166-1 alpha-3标准筛选出19个欧洲国家，时间跨度自1991年至2025年，共计7,134条观测记录。每条记录涵盖国家、来源、指标、性别及婚姻状况等分类维度，并附带详尽的数据来源标签与状态标记，确保数据的可追溯性与透明度。

使用方法

数据集可通过HuggingFace的datasets库便捷加载，以pandas DataFrame形式操作。用户可根据国家代码（ref_area）筛选特定国家，或针对单一指标（如EMP_XTRU_SEX_MTS_RT）进行时间序列分析与可视化。此外，支持透视表功能，将数据重塑为国家×年份矩阵，便于横向比较与面板回归建模。该数据集亦兼容分类与回归任务及时间序列预测，为劳动经济学研究提供标准化、可直接使用的数据基础。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）的ILOSTAT数据库于2025年发布，由Electric Sheep Europe重新整理并托管于HuggingFace平台，旨在系统记录1991至2025年间19个欧洲国家按性别和婚姻状况划分的与时间相关的不充分就业率。作为全球劳动统计的核心数据源，ILOSTAT基于各国劳动力调查等权威数据，遵循国际劳工统计学家会议的标准进行统一化处理。该数据集的问世为劳动经济学、社会政策及跨国比较研究提供了宝贵的时间序列数据，推动了关于劳动力利用不足及其结构性影响因素的研究，尤其在性别与家庭角色对就业质量影响的探讨中具有重要价值。

当前挑战

该数据集所解决的领域问题在于捕捉传统失业率指标之外的劳动力未充分利用现象，其挑战包括：准确界定并测量‘与时间相关的不充分就业’，这一概念涉及工作时长、工作意愿及可用性等多维判断；不同国家间统计口径和方法论的差异也带来了数据可比性难题。在构建过程中，面临的挑战有：整合多样化的国家来源数据并进行标准化，需要处理数据缺失、异常值及时间序列断裂等问题，例如数据中‘obs_status’标记的不可靠观测以及‘note_indicator’记录的方法论修订；此外，确保按性别和婚姻状况的细分数据在时间上的连续性和一致性，也是构建高质量数据集的关键难点。

常用场景

经典使用场景

该数据集汇集了1991至2025年间19个欧洲国家按性别与婚姻状况细分的时间相关就业不足率，共计7,134条观测记录。其经典使用场景在于劳动经济学领域的跨国比较研究与时间序列分析，研究者可借此审视不同国家劳动力市场的结构性差异，揭示性别与婚姻状况对就业不足现象的影响。通过面板数据模型的构建，该数据集为评估宏观经济政策、劳动力市场制度及社会变迁对就业质量的作用提供了坚实的数据基础，尤其适用于探讨非充分就业的动态演变规律及其与商业周期、社会保障体系的关联。

解决学术问题

该数据集有效回应了劳动经济学中关于就业不足衡量与人口特征分化的学术关切。它解决了以往研究中因数据零散或口径不一而难以进行跨国、长时段比较的困境，使学者能够系统分析性别与婚姻状况在就业不足风险中的交互效应，进而检验劳动力市场分割理论、性别角色理论及家庭经济理论等经典假说。其严谨的ILOSTAT来源与标准化的分类变量，保障了实证分析的可靠性与可复现性，为宏观劳动力市场研究提供了宝贵的微观证据支撑，有力推动了该领域从描述性分析向因果推断的深化。

实际应用

在实际应用层面，该数据集为政府机构、国际组织与社会政策研究者提供了决策支持工具。其数据可用于监测和评估各国实现体面劳动与可持续发展目标（特别是目标8）的进展，辅助制定针对特定人群（如已婚女性或未婚男性）的精准就业促进政策。劳动部门可借此识别就业不足的高风险群体，及时调整职业培训与就业服务资源配置。此外，该数据集亦能服务于企业的人力资源战略规划、学术机构的劳动力市场预警系统构建以及媒体与公众对就业质量议题的客观报道，促进社会对隐性劳动力闲置问题的关注与理解。

数据集最近研究

最新研究方向

在劳动经济学与机器学习交叉领域，该数据集聚焦于欧洲地区时间相关就业不足率的性别与婚姻状况异质性分析，其前沿研究方向在于利用时序预测与分类模型，揭示劳动力市场结构性变化对弱势群体（如已婚女性）的冲击。结合ILOSTAT的权威统计数据，该数据集支撑了欧洲多国1971至2025年间的比较研究，尤其在后疫情时代弹性用工制度兴起的背景下，为监测非标准就业质量、推动体面工作议程（SDG目标8）提供了关键证据。其细粒度分类（性别×婚姻状态）为分析家庭内部劳动供给决策、性别薪酬差距及政策干预效果（如育儿假改革）提供了数据基础，对欧洲社会政策制定与劳动力市场监测具有重要参考价值。