electricsheepeurope/europe-ilo-emp-xtru-sex-mts-nb-time-related-underemployment-by-sex-and-marital-st

--- 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 by sex and marital status (thousands) | Europe (ILOSTAT)" --- # Time-related underemployment by sex and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **7,142 observations** · **19 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **7,142 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_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_MTS_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 19 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 753 | 1991 | 2025 | | `ESP` | 620 | 2000 | 2025 | | `GBR` | 557 | 2005 | 2025 | | `CZE` | 527 | 2002 | 2024 | | `MDA` | 520 | 2006 | 2025 | | `FRA` | 513 | 2005 | 2024 | | `AUT` | 512 | 2004 | 2025 | | `PRT` | 471 | 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_NB` — Time-related underemployment by sex and marital status (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_MTS_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.) | `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) | `38.876` | | `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-nb-time-related-underemployment-by-sex-and-marital-st") 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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_xtru_sex_mts_nb_time_related_underemployment_by_sex_and_marital_st_2025, title = {Time-related underemployment by sex and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-xtru-sex-mts-nb-time-related-underemployment-by-sex-and-marital-st}} } ``` ## 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_NB_This dataset contains 7,142 observations of Time-related underemployment data across 19 Europe countries, spanning 1991 to 2025, covering 1 distinct indicator. The data is sourced from the International Labour Organizations ILOSTAT database, focusing on time-related underemployment by sex and marital status (in thousands). It includes columns such as country codes, sources, indicators, sex classifications, years, and observed values for analyzing and forecasting underemployment trends in European labor markets.

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，聚焦于欧洲19个国家1991至2025年间基于性别与婚姻状况的时间相关就业不足指标（以千人为单位）。构建流程首先通过ILOSTAT REST API（如rplumber.ilo.org/data/indicator?id=EMP_XTRU_SEX_MTS_NB）提取原始数据，并依据欧洲ISO3国家代码进行地理过滤。随后，数据按照统一的模式结构进行规范化处理，涵盖国家编码、来源标识、指标定义、性别分类、时间维度及观测值等关键字段，并整合了数据来源标签与观测状态注释，确保数据的可追溯性与质量透明度。最终以Parquet格式封装，为机器学习应用提供整洁、即用的数据层。

使用方法

数据集的使用便捷高效，研究者可通过HuggingFace的load_dataset()函数直接加载，并转换为Pandas DataFrame进行深度分析。具体实施路径包括：按国家筛选（如ref_area=='DEU'），以提取特定区域的时序数据；针对单一指标进行时间排序，利用obs_value绘制趋势图，揭示就业不足的动态变化；更可构建国家×年份的透视矩阵，实现跨国的横向比较。此外，数据集的年度频率和分类字段设计，使其天然适用于时间序列预测、分类或回归等监督学习任务，为劳动经济学研究、政策评估及社会科学建模提供了可靠的数据支撑。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）的ILOSTAT数据库整理并重新封装，由Electric Sheep Europe于2025年发布，聚焦于1991至2025年间欧洲19个国家的时间相关就业不足状况，按性别和婚姻状况细分（单位：千人）。作为劳动统计领域的权威来源，ILOSTAT基于各国劳动力调查等数据，依据国际劳工统计学家会议（ICLS）定义进行统一口径处理，为研究欧洲劳动力市场的结构性问题提供了宝贵的时间序列数据。该数据集的发布增强了劳动经济学的实证研究基础，尤其在分析就业质量、劳动力市场灵活性及性别差异方面具有重要价值，为政策制定者和学术研究者提供了跨国家、长周期的标准化数据资源。

当前挑战

该数据集所解决的领域问题在于，时间相关就业不足作为劳动力市场的重要指标，传统上缺乏系统性的跨国家可比数据，且其与性别、婚姻状况等人口特征的交叉分析常因数据分散而难以开展。在构建过程中，挑战包括：整合来源于不同国家、不同调查体系的原始数据，需克服统计口径差异与数据可比性问题；确保时间序列的完整性与连续性，应对部分国家数据缺失或方法修订（如断点标记）；处理多来源数据时需选择ILO认定的最佳来源，同时维护性别、婚姻状况等维度的细粒度划分，保证数据质量与可追溯性。

常用场景

经典使用场景

该数据集汇集了1991年至2025年间19个欧洲国家的时间相关就业不足率，按性别和婚姻状况分类，包含7142个观测值。其经典用途在于为劳动经济学和社会政策研究提供宏观层面的时间序列数据。研究者常利用该数据集进行跨国比较分析，考察不同性别及婚姻状况群体在就业不足现象中的差异与演变趋势，进而探讨劳动力市场结构的变化。此外，该数据集亦适用于构建预测模型，以揭示经济周期、制度变迁对就业不足的潜在影响。

解决学术问题

该数据集有效解决了劳动经济学中关于就业不足的度量与分析难题。传统研究多聚焦于失业率，而忽视了就业不足这一隐性劳动力闲置现象。该数据集基于国际劳工组织统一口径，提供了按性别和婚姻状况细分的标准化数据，使得跨时期、跨国别的实证研究成为可能。它不仅弥补了现有数据库在细分维度上的不足，还推动了对劳动力市场非充分就业形态的深入理解，为评估经济政策效果、劳动权益保障等学术议题提供了坚实的数据基础。

实际应用

在实际应用层面，该数据集对于政策制定者、国际组织及社会研究机构具有重要参考价值。通过分析不同国家和地区就业不足率的动态变化，可识别劳动力市场的结构性短板，辅助制定有针对性的就业促进政策与性别平等措施。例如，劳动部门可据此评估现有就业政策对特定群体（如已婚女性）的覆盖效果，并调整培训与就业服务方向。同时，该数据亦可用于企业人力资源规划，帮助识别潜在劳动力供给池，优化人才招聘策略。

数据集最近研究

最新研究方向

该数据集聚焦于欧洲19国1991至2025年间按性别与婚姻状况细分的时间相关非充分就业统计，其前沿研究方向在于利用ILOSTAT标准化口径的纵向面板数据，结合机器学习时序预测与分类模型，深入解析劳动力市场中非充分就业的结构性演变，尤其关注性别差异、婚姻状态与就业质量之间的交互效应。在欧盟推动体面工作与性别平等议程、以及后疫情时代灵活就业形态激增的背景下，该数据为评估部分就业不足对劳动生产率和个体福祉的影响提供了关键实证基础，并助力于跨国家比较下的政策模拟与早期预警系统构建，对完善劳动力市场监测体系具有重要方法论与决策支持价值。