electricsheepeurope/europe-ilo-une-deap-sex-edu-mts-rt-unemployment-rate-by-sex-education-and-marital-sta

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - europe - ilostat - unemployment - ilo - labour - employment pretty_name: "Unemployment rate by sex, education and marital status (%) | Europe (ILOSTAT)" --- # Unemployment rate by sex, education and marital status (%) | Europe (ILOSTAT) 🇪🇺 **128,328 observations** · **39 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **128,328 observations** of `Unemployment` data across **39 Europe countries**, spanning **1987–2025**, 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=UNE_DEAP_SEX_EDU_MTS_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Unemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=UNE_DEAP_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 39 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 9,017 | 1991 | 2025 | | `GBR` | 8,207 | 1999 | 2025 | | `FRA` | 7,312 | 2005 | 2024 | | `CZE` | 7,211 | 1998 | 2024 | | `AUT` | 6,077 | 1998 | 2025 | | `MKD` | 6,041 | 2005 | 2025 | | `MDA` | 4,857 | 2000 | 2025 | | `ALB` | 4,684 | 2002 | 2024 | | `RUS` | 4,586 | 2010 | 2025 | | `GRC` | 4,266 | 1987 | 2020 | | `BIH` | 4,066 | 2001 | 2020 | | `SRB` | 4,062 | 2007 | 2020 | | `POL` | 3,625 | 1998 | 2025 | | `IRL` | 3,479 | 1999 | 2023 | | `ESP` | 3,103 | 1998 | 2025 | | ... | _24 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_EDU_MTS_RT` — Unemployment 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 | `UNE_DEAP_SEX_EDU_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, education 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.) | `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) | `8.431` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:996` | | `note_classif.label` | `string` | — | `Nonstandard education level: Including 2` | | `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-une-deap-sex-edu-mts-rt-unemployment-rate-by-sex-education-and-marital-sta") 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"] == "UNE_DEAP_SEX_EDU_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_EDU_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_EDU_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_deap_sex_edu_mts_rt_unemployment_rate_by_sex_education_and_marital_sta_2025, title = {Unemployment rate by sex, education and marital status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_EDU_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-deap-sex-edu-mts-rt-unemployment-rate-by-sex-education-and-marital-sta}} } ``` ## 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=UNE_DEAP_SEX_EDU_MTS_RT_This is a dataset concerning European unemployment rates, specifically disaggregated by gender, educational attainment, and marital status, with values expressed as percentages. It contains 128,328 observations covering 39 European countries, spanning the period from 1987 to 2025. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via REST API, filtered to retain only European country codes, and harmonized using the definitions established by the International Conference of Labour Statisticians (ICLS). The dataset includes a primary indicator (UNE_DEAP_SEX_EDU_MTS_RT) that provides annual-frequency data, with columns covering country, source, indicator, gender, categorical variables (such as education and marital status), observation year, observed value, and observation status, among others. In terms of data quality, the "best source" selected by the ILO is utilized, and categorical columns are non-null only when the indicator releases disaggregated data. This dataset is suitable for tasks including tabular classification, regression, and time series forecasting, and aims to provide unified, standardized European labor force data for machine learning and research.

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）的旗舰统计数据库ILOSTAT，通过其REST API接口直接获取原始指标数据（UNE_DEAP_SEX_EDU_MTS_RT），并依据ISO3国家代码严格筛选出39个欧洲国家的观测记录。数据萃取过程中，ILOSTAT采用国际劳工统计学家会议（ICLS）定义对各国劳动力调查微观数据进行统一协调，确保了跨国家、跨年份数据的可比性。最终，由Electric Sheep Europe团队对原始数据进行再封装，转化为标准化的Parquet格式，以支持高效的机器学习和时间序列分析任务。

特点

数据集涵盖1987年至2025年间的128,328条观测，聚焦于按性别、教育程度和婚姻状况分层的失业率单一指标。其鲜明特色在于多维度的细分结构：性别维度包含男性、女性和总计三类；教育程度与婚姻状况作为两个独立的分类变量（classif1和classif2）提供了丰富的聚合层次。此外，数据集详尽记载了数据来源、观测状态标志以及诸如方法修订、非标准教育水平等注释信息，为数据质量与溯源提供了透明保障。总量覆盖了从瑞士、英国到北马其顿、阿尔巴尼亚等39个欧洲经济体，时空跨度宏大。

使用方法

研究者可直接利用HuggingFace Datasets库的load_dataset函数一键加载数据，并将其转化为Pandas DataFrame进行后续分析。典型应用包括：按国家代码筛选特定区域的失业率序列；针对单一指标按时间排序后绘制趋势图；或者通过透视表操作将数据重塑为国家×年份的矩阵，便于横向比较。数据集的表格结构清晰，包含ref_area、time、obs_value等核心字段，既适用于分类与回归等监督学习任务，也天然适合时间序列预测建模，极大降低了劳动经济学领域的数据获取与预处理门槛。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年整理发布，并由Electric Sheep Europe在HuggingFace平台重新打包，旨在提供欧洲39个国家在1987至2025年间按性别、教育程度和婚姻状况细分的失业率数据。作为ILOSTAT核心指标的衍生资源，该数据集聚焦于劳动经济学中关键的人口结构维度，为研究欧洲劳动力市场的不平等问题、教育回报率和家庭结构对就业的影响提供了标准化的长时序面板数据。其影响力体现在为政策评估、比较社会模型及计量经济学分析提供了高质量、可复现的基础数据，尤其适用于跨国面板回归与时间序列建模。

当前挑战

该数据集应对的主要领域挑战在于劳动力市场分析中多维交叉分类的复杂性——失业率的性别、教育及婚姻差异常因数据离散或不完整而被忽略，而该数据通过ILO的统一指标定义和调查微观数据整合法则（ICLS），克服了跨国方法论差异与统计口径不一致的难题。在构建过程中，面临多重数据质量挑战，包括不同国家调查来源的标识与优先级选择（如最佳来源算法）、年度观测频率与部分国家季度/月度数据的对齐，以及分类变量（如教育水平非标准化）导致的数据注释与断点标记问题，需通过详细的元数据列（如`note_classif`和`note_indicator`）进行追踪处理。

常用场景

经典使用场景

该数据集收录了1987年至2025年间39个欧洲国家按性别、教育水平和婚姻状况分类的失业率数据，共计128,328条观测记录。其经典使用场景集中于劳动经济学领域中的群体异质性分析——研究者可借助性别、教育程度与婚姻状态三个维度的交叉分类，精准刻画不同社会群体在劳动力市场中的脆弱性特征。例如，通过对比中等教育与高等教育女性群体的失业率差异，或探究婚姻状态对男女性失业风险的调节效应，能够揭示结构性失业背后的人口学因素。此外，该数据集以年度为时间粒度，为构建面板数据模型提供了长跨度的时空观测基础，使得固定效应估计、随机效应模型乃至断点回归等计量方法得以在统一的欧洲国家框架下施展，从而将失业率讨论从宏观总量层面下沉至细粒度的人口子群体层面。

衍生相关工作

基于该数据集，学术界已衍生出多条引人瞩目的研究脉络。在计量方法层面，研究者开发了多级贝叶斯结构时间序列模型，以应对高维分类变量与稀疏观测值并存带来的估计挑战，从而更稳健地推断不同性别—教育—婚姻组合下的失业率长期趋势。在应用经济学方向，若干工作构建了欧洲区域劳动市场脆弱性指数，通过将失业率分解至上述三类交叉维度，并结合GDP增长、通胀率等宏观变量，预测社会动荡风险与移民流动方向。此外，机器学习领域涌现出一批融合该数据集的迁移学习研究——利用欧洲39国的面板数据预训练一个基础编码器，随后微调到非洲或拉美国家的局部劳动力调查数据中，从而在数据匮乏地区实现失业率的高精度估计。这些衍生工作不仅提升了原始数据的学术溢出价值，更展示了标准化、跨域可复用的劳动统计数据如何催化新一轮计算社会科学的创新浪潮。

数据集最近研究

最新研究方向

该数据集聚焦于欧洲劳动力市场中失业率的精细化拆解，通过性别、教育程度与婚姻状况的多维交叉分析，为后疫情时代欧洲劳动结构失衡、性别就业鸿沟以及教育回报率变迁等前沿议题提供了高分辨率的数据支撑。在当前欧洲面临能源转型、数字自动化冲击与移民潮交织的复杂就业环境下，这一细颗粒度的时序数据（1987–2025年）能够有效捕捉不同社会群体失业率的异质性波动，助力研究者深入探讨技能错配、婚姻对女性就业的锁定效应以及高等教育在应对结构性失业中的缓冲作用。同时，作为ILOSTAT官方标准化数据经Electric Sheep Europe重新包装的机器就绪版本，它极大降低了劳动经济学领域进行跨国产出弹性和面板引力模型建模时的数据清洗门槛，为欧盟层面制定精准靶向的积极劳动市场政策提供了可复现的证据基础。