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

--- 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 by sex and age (thousands) | Europe (ILOSTAT)" --- # Unemployment by sex and age (thousands) | Europe (ILOSTAT) 🇪🇺 **111,161 observations** · **43 Europe countries** · **1969–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **111,161 observations** of `Unemployment` data across **43 Europe countries**, spanning **1969–2025**, 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_AGE_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_AGE_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 43 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ESP` | 4,061 | 1969 | 2025 | | `ITA` | 3,864 | 1970 | 2025 | | `PRT` | 3,759 | 1974 | 2025 | | `SWE` | 3,739 | 1969 | 2025 | | `GBR` | 3,642 | 1971 | 2025 | | `GRC` | 3,638 | 1981 | 2025 | | `AUT` | 3,534 | 1969 | 2025 | | `IRL` | 3,518 | 1983 | 2025 | | `NLD` | 3,512 | 1975 | 2025 | | `DNK` | 3,502 | 1969 | 2025 | | `DEU` | 3,495 | 1983 | 2025 | | `FRA` | 3,481 | 1969 | 2025 | | `BEL` | 3,442 | 1969 | 2025 | | `FIN` | 3,383 | 1969 | 2025 | | `NOR` | 3,245 | 1972 | 2025 | | ... | _28 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_AGE_NB` — Unemployment by sex and age (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_AGE_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex and age (thousands)` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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` | — | `C6:1474` | | `note_classif.label` | `string` | — | `Nonstandard age group: Including ages…` | | `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-age-nb-unemployment-by-sex-and-age-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_AGE_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_AGE_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_AGE_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_tune_sex_age_nb_unemployment_by_sex_and_age_thousands_2025, title = {Unemployment by sex and age (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_AGE_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-tune-sex-age-nb-unemployment-by-sex-and-age-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_AGE_NB_This dataset contains tabular data on unemployment by sex and age (in thousands) for European countries. It includes 111,161 observations across 43 European countries, spanning from 1969 to 2025, with one distinct indicator: UNE_TUNE_SEX_AGE_NB (Unemployment by sex and age, thousands). The data is sourced from the International Labour Organization (ILO) ILOSTAT database, pulled via the REST API and filtered to Europe ISO3 country codes, harmonized using ICLS (International Conference of Labour Statisticians) definitions. The dataset features columns such as country code, country name, source code, source label, indicator code, indicator label, sex disaggregation (total, male, female), age classification, observation year, observed value, observation status flags, and notes. Data is annual in frequency and is suitable for tasks like tabular classification, regression, and time-series forecasting. Repackaged by Electric Sheep Europe in Parquet format 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，通过REST API直接获取失业率指标（UNE_TUNE_SEX_AGE_NB），并依据欧洲ISO3国家代码进行地理范围过滤。ILOSTAT依据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协调与标准化处理。数据集的构建过程中，原始调查数据来源在source.label字段中予以标注，以确保可追溯性。最终形成包含111,161条观测记录、覆盖43个欧洲国家、时间跨度从1969年至2025年的结构化表格数据。

特点

该数据集的核心特点在于其高度的标准化与细粒度。它仅包含一个核心指标——按性别与年龄划分的失业人数（单位：千），但提供了性别（总、男性、女性）和年龄分类等多个维度进行拆解。每条记录均包含ISO 3166-1 alpha-3国家代码、ILOSTAT指标代码、观测年份、观测值、以及数据状态标记（如unreliable）和多个注释字段，便于用户识别数据质量与系列中断等问题。此外，数据以年度频率呈现，并采用ILO选定的最佳来源，确保同一国家与年份的数据一致性。

使用方法

用户可通过HuggingFace的datasets库便捷加载数据集，使用`load_dataset("electricsheepeurope/europe-ilo-une-tune-sex-age-nb-unemployment-by-sex-and-age-thousands")`即可获取完整的训练集并转换为Pandas DataFrame进行后续分析。数据集的典型使用方式包括：按国家代码筛选获取特定国家的失业趋势时间序列；针对单一指标按年份排序以进行可视化探索；以及通过数据透视表将数据重塑为国家×年份的矩阵，便于进行跨国的面板数据分析或时间序列预测任务。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门依托其核心数据库ILOSTAT构建，由Electric Sheep Europe于2025年进行重新封装并发布于HuggingFace平台。作为全球劳动统计领域的权威来源，ILOSTAT通过整合各国劳动力调查、家庭收入调查及行政记录等多元数据，为劳动经济学研究提供了标准化、可比较的跨国产出。本数据集聚焦欧洲43个国家1969至2025年间的失业率细分数据，依据性别与年龄维度（如青年与成年群体）进行精细化分解，共计超过11万条观测记录。其核心研究问题在于揭示欧洲劳动力市场中结构性失业的长期演变规律，为政策制定者与学术研究者提供高颗粒度的时序证据。该数据集的发布显著降低了获取标准化国际劳动统计的门槛，推动了基于机器学习的劳动力市场动态预测、区域经济比较分析及社会保障政策评估等领域的实证研究。

当前挑战

该数据集所应对的领域核心挑战在于国际劳动统计的碎片化与不一致性——各国在失业定义、调查方法、年龄分组标准及数据采集频率上存在显著差异，导致跨时期、跨国别的可靠比较极为困难。ILOSTAT通过采用国际劳工统计学家会议（ICLS）的统一定义与数据协调流程予以解决，但由此也带来了构建过程中的多重挑战：首先，原始调查微观数据需经过复杂的清洗与标准化处理，以消除不同来源地在统计口径、抽样误差及时间序列断裂（如方法论修订标识于note_indicator列）上的偏差；其次，多源数据融合时需要甄别并选取ILO认定的‘最佳来源’，并对缺失值、异常标志（如obs_status中的‘不可靠’标签）进行审慎标注，确保数据使用者能明确追溯每条记录的信源与质量状态。

常用场景

经典使用场景

作为欧洲劳动力市场中失业率时序分析的经典数据源，该数据集广泛应用于追踪43个国家从1969年至2025年间按性别与年龄划分的失业人数变化趋势。研究者通常借助此数据集开展跨国比较研究，剖析不同经济体在劳动力市场周期性波动中的异质性表现。其高维度的分列结构——涵盖总失业人数、男性失业人数与女性失业人数——使得性别差异视角下的劳动经济学分析成为可能，尤其在探讨经济危机、技术变革或政策干预对不同人群就业冲击的长期效应时，该数据集提供了无可替代的时序观测基础。

实际应用

在实际应用层面，该数据集为欧洲各国政府、国际组织及政策研究机构提供了制定就业促进方针与评估社保体系效能的定量依据。通过监测不同性别与年龄群体失业人数的年度波动，政策制定者能够精准识别劳动力市场中的薄弱环节，例如青年失业高企或女性就业参与率偏低等问题，并据此设计靶向干预措施。此外，跨国企业的人力资源规划与行业协会的薪资谈判策略亦可基于该数据的长期趋势分析进行前瞻性调整，从而在宏观与微观两个维度发挥其现实指导价值。

衍生相关工作

基于该数据集，学术界衍生出了一系列富有影响力的经典工作，包括利用面板数据模型探究移民流入与本地就业替代效应之间的关联，以及构建马尔可夫切换模型预测欧洲失业周期的拐点。也有研究以该数据为基准，训练时序预测模型用于前瞻性政策模拟，或结合教育、产业升级等其他社会经济变量探讨失业结构的深层次成因。这些衍生产出不仅深化了对欧洲劳动力市场运行规律的理解，也验证了该数据集在跨学科交叉研究中的稳健性与实用性。