electricsheepeurope/europe-ilo-une-tune-sex-edu-nb-unemployment-by-sex-and-education-thousands

--- 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 - unemployment - ilo - labour - employment pretty_name: "Unemployment by sex and education (thousands) | Europe (ILOSTAT)" --- # Unemployment by sex and education (thousands) | Europe (ILOSTAT) 🇪🇺 **31,559 observations** · **39 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **31,559 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_TUNE_SEX_EDU_NB) - **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_TUNE_SEX_EDU_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 39 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 1,677 | 1987 | 2025 | | `CHE` | 1,311 | 1991 | 2025 | | `GBR` | 1,180 | 1992 | 2025 | | `FRA` | 1,083 | 1993 | 2024 | | `CZE` | 1,009 | 1998 | 2024 | | `IRL` | 1,003 | 1992 | 2024 | | `PRT` | 1,000 | 1992 | 2025 | | `ITA` | 994 | 1992 | 2024 | | `NLD` | 990 | 1996 | 2024 | | `ESP` | 966 | 1992 | 2025 | | `BEL` | 946 | 1992 | 2024 | | `SWE` | 944 | 1995 | 2024 | | `DEU` | 933 | 1992 | 2024 | | `DNK` | 899 | 1992 | 2024 | | `AUT` | 889 | 1995 | 2025 | | ... | _24 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_EDU_NB` — Unemployment by sex and education (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 | `UNE_TUNE_SEX_EDU_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex and education (th…` | | `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` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `108.247` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:5578` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-tune-sex-edu-nb-unemployment-by-sex-and-education-thousands") 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_TUNE_SEX_EDU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_EDU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_EDU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_tune_sex_edu_nb_unemployment_by_sex_and_education_thousands_2025, title = {Unemployment by sex and education (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_EDU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-tune-sex-edu-nb-unemployment-by-sex-and-education-thousands}} } ``` ## 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_TUNE_SEX_EDU_NB_This dataset comprises unemployment statistics for 39 European countries covering the period from 1987 to 2025, with a primary focus on the count of unemployed persons (in thousands) categorized by gender and educational attainment. The dataset contains 31,559 observations, derived from the ILOSTAT database of the International Labour Organization (ILO) — the leading global repository for labor force statistics. Data was acquired through the ILOSTAT REST API and filtered to exclusively include European nations. The dataset structure features columns including country code, year, gender classifications (total, male, female), educational attainment categories, observed values, and corresponding status flags, making it suitable for tasks such as tabular classification, regression, and time series forecasting. The data is disseminated annually and has undergone standardization by the ILO to guarantee consistency across all entries.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILO）旗下的ILOSTAT中央统计数据库构建，通过REST API直接提取指标为`UNE_TUNE_SEX_EDU_NB`的原始数据，并依据欧洲ISO3国家代码进行地理范围筛选。ILOSTAT遵循国际劳工统计学家会议（ICLS）的统一定义，对各国劳动调查微观数据进行规范化处理，并在`source.label`字段中标注数据来源以确保可追溯性。数据集覆盖39个欧洲国家，时间跨度从1987年至2025年，共计31,559条观测记录，最终由Electric Sheep Europe进行重新打包，以统一、易于使用的格式呈现于HuggingFace平台。

特点

本数据集聚焦于按性别与教育程度划分的欧洲失业人数（单位：千），具有多维度的精细拆解特性。其核心指标`UNE_TUNE_SEX_EDU_NB`通过`sex`变量（包含总计、男性、女性三类）和`classif1`教育分类变量实现分层统计，为研究者提供了深入分析劳动力市场结构性差异的基础。数据涵盖39个国家及近四十年时间序列，支持跨国家、跨时期的纵向比较。此外，数据集附带了详细的观测状态标记（如`obs_status`）和注释字段（如`note_indicator`），清晰记录了数据修订、方法变更等质量信息，增强了数据分析的可靠性与透明度。

使用方法

用户可通过HuggingFace的`datasets`库便捷加载数据，执行`load_dataset("electricsheepeurope/europe-ilo-une-tune-sex-edu-nb-unemployment-by-sex-and-education-thousands")`后即可将训练集转换为Pandas DataFrame进行分析。数据使用上支持多种操作范式：可按国家代码（如`ref_area`）过滤特定国家的时间序列；可依据`sex`和`classif1`维度对数据集进行切片，观察不同群体失业率的演变趋势；亦可利用`pivot_table`构建国家×年份的矩阵，便于进行面板数据分析或可视化呈现，满足从基础统计到计量经济学的多元需求。

背景与挑战

背景概述

该数据集由Electric Sheep Europe于2025年基于国际劳工组织（ILO）的ILOSTAT数据库重新整理发布，聚焦欧洲39个国家1987年至2025年间按性别与教育程度划分的失业人数（单位：千）。ILOSTAT作为全球劳动统计的权威来源，通过整合各国劳动力调查与行政记录，提供了标准化、可比较的指标体系。该数据集的构建旨在解决欧洲劳动市场中长期存在的结构性失业问题，为政策制定者与研究者提供细颗粒度的纵向数据，以分析教育水平与性别差异对就业机会的影响，进而推动联合国可持续发展目标中关于体面工作的议题。其跨时近四十年的高频观测值，使其成为时间序列分析与跨国比较研究的宝贵资源。

当前挑战

该数据集所面临的领域问题在于，欧洲各国失业率的统计口径与调查方法存在差异，尽管ILOSTAT采用国际劳工统计学家会议（ICLS）定义进行协调，但原始数据源的异构性仍可能导致跨国可比性偏差。构建过程中，数据清洗需处理多源标注的不一致性，如部分观测值标有“不可靠”状态，且教育水平分类（如非标准教育程度）需统一映射。此外，时间序列存在因方法论修订引发的结构性断点，需通过注释字段识别并调整，以确保回归与预测模型的稳健性。年度频率亦限制了对于短期经济波动的捕捉能力，而部分国家在早期年份的数据缺失则增加了插补与建模的复杂度。

常用场景

经典使用场景

在劳动经济学与公共政策研究领域，该数据集因其精细化的分层结构而备受青睐，尤其适用于探究欧洲各国失业率在不同性别与教育水平维度上的演化趋势。研究者常利用其丰富的年度观测数据，构建面板数据模型来分析教育投资回报率、性别就业差异的时空变迁，或评估经济周期对特定人群就业冲击的非对称效应。其覆盖1987至2025年、横跨39个欧洲国家的长时序与宽截面特性，为跨国比较研究和因果推断提供了坚实的数据基础。

实际应用

在实际应用层面，该数据集为政策制定者、国际组织及社会研究机构提供了关键决策支持。例如，欧盟委员会可利用其监测各成员国特定群体（如女性高等教育者）的失业动态，从而精准设计促进公平就业的干预措施。同时，劳动经济学家和人口学家可基于该数据构建预测模型，模拟人口老龄化或教育改革对失业率的潜在影响，为优化职业培训方案和调整教育资源配置提供数据驱动的洞察。

衍生相关工作

基于该数据集衍生出了一系列具有影响力的经典工作。研究者将其与ILOSTAT的其他劳动指标（如工资水平与劳动参与率）或宏观经济数据（如GDP、产业增加值）进行融合，开发出分析欧洲劳动市场韧性与结构性变革的综合模型。此外，该数据集也常被用作基准测试，在时间序列预测竞赛和统计学习研究中，用于评估新算法对含有多维离散特征与长程依赖的复杂社会数据的建模性能。