electricsheepeurope/europe-ilo-emp-xtru-sex-geo-mts-rt-time-related-underemployment-rate-by-sex-rural-urb

--- 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, rural / urban area and marital status (%) | Europe (ILOSTAT)" --- # Time-related underemployment rate by sex, rural / urban area and marital status (%) | Europe (ILOSTAT) 🇪🇺 **4,131 observations** · **11 Europe countries** · **2004–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **4,131 observations** of `Time-related underemployment` data across **11 Europe countries**, spanning **2004–2025**, 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_GEO_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_GEO_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 11 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `AUT` | 594 | 2004 | 2025 | | `FRA` | 576 | 2005 | 2024 | | `MDA` | 540 | 2006 | 2025 | | `CHE` | 488 | 2005 | 2025 | | `BIH` | 423 | 2006 | 2020 | | `GRC` | 405 | 2006 | 2020 | | `SRB` | 351 | 2008 | 2020 | | `POL` | 297 | 2015 | 2025 | | `BLR` | 274 | 2017 | 2024 | | `CZE` | 156 | 2019 | 2024 | | `MNE` | 27 | 2020 | 2020 | ## Indicators (sample) - `EMP_XTRU_SEX_GEO_MTS_RT` — Time-related underemployment rate by sex, rural / urban area and marital status (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AUT` | | `ref_area.label` | `string` | Country name in English | `Austria` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:103` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EMP_XTRU_SEX_GEO_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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `2.823` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-geo-mts-rt-time-related-underemployment-rate-by-sex-rural-urb") 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_GEO_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_XTRU_SEX_GEO_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_XTRU_SEX_GEO_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_emp_xtru_sex_geo_mts_rt_time_related_underemployment_rate_by_sex_rural_urb_2025, title = {Time-related underemployment rate by sex, rural / urban area and marital status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_XTRU_SEX_GEO_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-xtru-sex-geo-mts-rt-time-related-underemployment-rate-by-sex-rural-urb}} } ``` ## 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_GEO_MTS_RT_This dataset contains 4,131 observations of Time-related underemployment data across 11 European countries (e.g., Austria, France, Moldova) from 2004 to 2025, covering 1 distinct indicator (EMP_XTRU_SEX_GEO_MTS_RT). The data is disaggregated by sex, rural/urban area, and marital status, sourced from the International Labour Organizations ILOSTAT database via a REST API. Organized in tabular format, it includes columns such as country code, year, observed value, data source, and classification variables, suitable for tabular classification, regression, and time-series forecasting tasks. The dataset has been repackaged by Electric Sheep Europe for machine learning readiness.

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重新封装并发布于HuggingFace平台。数据通过ILOSTAT REST API直接提取，筛选出欧洲11个国家的观测记录，涵盖2004至2025年的年度数据，共计4131条观测。构建过程遵循ILO的统计标准，利用ICLS定义对原始调查微观数据进行协调处理，并在源标签列中标注数据来源以实现可追溯性。数据集以Parquet格式存储，提供了统一的模式和高鲁棒性的数据结构，便于直接加载和分析。

特点

数据集聚焦于时间相关就业不足率这一关键劳动统计指标，按性别、城乡区域和婚姻状况进行细致划分，包含丰富的分类维度。其显著特点在于数据来源的权威性与广泛性，覆盖多个欧洲国家，时间跨度长达二十余年，且提供了详细的元数据注释，如观测状态、断点标识和来源备注，极大增强了数据的可解释性和可信度。此外，数据质量经过ILO精心把关，采用最佳来源选择策略，确保多源数据的一致性与可靠性。

使用方法

数据集通过HuggingFace的datasets库可便捷加载，支持直接转换为Pandas DataFrame进行操控。使用者可按国家代码、时间范围或指标代码进行灵活筛选，轻松实现单一国家的时间序列分析、跨国的矩阵透视等操作。数据集的扁平化表结构利于进行探索性数据分析、可视化展示以及作为机器学习模型的输入特征。其标准化格式和详尽的列描述，使得研究者能够快速构建预测模型或统计推断，满足劳动经济学研究与政策评估的多元化需求。

背景与挑战

背景概述

在劳动经济学与劳动力市场监测领域，时间相关不充分就业率是衡量劳动力资源利用效率的关键指标，其国际可比性与细粒度分解对政策制定与学术研究具有重要价值。此数据集由Electric Sheep Europe于2025年整理发布，依托国际劳工组织（ILO）的ILOSTAT统计数据库，覆盖11个欧洲国家、横跨2004至2025年的4,131条观测，聚焦于按性别、城乡及婚姻状况分层的时间相关不充分就业率。该数据集通过标准化流程对原始微数据进行协调，利用国际劳工统计学家会议（ICLS）定义确保跨国可比性，为研究者提供了精确的时间序列与多维分类维度，推动了欧洲劳动力市场不平等的实证分析及社会政策评估。

当前挑战

该领域的核心挑战在于劳动力统计数据的高度政策敏感性及跨国比较的复杂性，尤其是定义口径的差异性导致不同国家间数据难以直接对齐，而ILOSTAT虽经协调仍面临观测值缺失、指标口径修订及样本代表性不足等问题。构建数据集时，需处理多来源调查数据的异构性，统一分类变量（如性别、地理覆盖、婚姻状态），并应对时间序列中的断点与标记（如'Break in series'），同时保证数据质量与可溯源性。此外，克制的国家覆盖（仅11国）与不均衡的行数分配（如黑山仅27条）削弱了区域推断的稳健性，进一步凸显数据稀疏性与覆盖不足引发的归纳挑战。

常用场景

经典使用场景

该数据集汇集了欧洲11个国家2004至2025年间关于时间相关就业不足率的精细统计，并按性别、城乡区域及婚姻状况进行分层剖析。其经典应用场景集中于劳动力市场的时间维度分析，研究者可借此构建面板数据模型，探究不同群体在劳动力资源利用上的结构性差异，尤其是非全日制就业者及边缘劳动力的就业质量变迁。此类分析为劳动经济学中的就业不足理论提供了实证基础。

实际应用

在实际应用中，该数据集为政府机构、国际组织及政策制定者提供了动态监测劳动力市场健康状况的工具。基于这些数据，决策者可识别出就业不足的高发群体与地区，从而设计针对性的职业培训、就业援助及区域发展策略。同时，数据也服务于企业的人力资源规划及学术界的劳动经济预测模型，助力于提升劳动力配置效率和促进包容性增长。

衍生相关工作

基于此数据集，衍生了一系列关于劳动力市场灵活性与就业质量的研究。相关工作包括构建欧洲国家就业不足率的预测模型，探索经济周期、制度因素对时间相关就业不足的影响，以及利用机器学习方法进行劳动力市场细分和风险预警。这些工作不仅丰富了劳动经济学的实证文献，也为后续研究提供了方法论参考，促进了数据驱动的社会政策分析范式的形成。