electricsheepeurope/europe-ilo-une-tune-sex-age-mts-nb-unemployment-by-sex-age-and-marital-status-thousan

--- 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, age and marital status (thousands) | Europe (ILOSTAT)" --- # Unemployment by sex, age and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **195,059 observations** · **39 Europe countries** · **1983–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **195,059 observations** of `Unemployment` data across **39 Europe countries**, spanning **1983–2025**, 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_MTS_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_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 39 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 11,315 | 1991 | 2025 | | `GBR` | 10,931 | 1983 | 2025 | | `CZE` | 10,821 | 1993 | 2024 | | `POL` | 9,393 | 1997 | 2025 | | `AUT` | 8,213 | 1995 | 2025 | | `MDA` | 7,585 | 2000 | 2025 | | `FRA` | 7,565 | 2005 | 2024 | | `MKD` | 7,346 | 2005 | 2025 | | `RUS` | 6,732 | 2010 | 2025 | | `ESP` | 6,473 | 1986 | 2025 | | `IRL` | 6,241 | 1983 | 2023 | | `ALB` | 6,199 | 2002 | 2024 | | `ITA` | 5,974 | 1983 | 2024 | | `BIH` | 5,533 | 2001 | 2020 | | `DEU` | 5,139 | 1983 | 2020 | | ... | _24 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_AGE_MTS_NB` — Unemployment by sex, age 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 | `UNE_TUNE_SEX_AGE_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex, age and marital …` | | `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+` | | `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) | `108.247` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C6:1688` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding 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-mts-nb-unemployment-by-sex-age-and-marital-status-thousan") 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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_AGE_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_AGE_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_tune_sex_age_mts_nb_unemployment_by_sex_age_and_marital_status_thousan_2025, title = {Unemployment by sex, age and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_AGE_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-tune-sex-age-mts-nb-unemployment-by-sex-age-and-marital-status-thousan}} } ``` ## 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_MTS_NB_This is a tabular dataset on unemployment in Europe, specifically containing statistics on unemployment by sex, age and marital status (in thousands). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), covering 39 European countries from 1983 to 2025, with 195,059 observations and 1 core indicator (UNE_TUNE_SEX_AGE_MTS_NB). The dataset is provided at annual frequency and includes fields such as country code, country name, data source, indicator code, indicator name, sex classification, age classification, marital status classification, observation year, observed value, observation status, and related notes, suitable for tasks like tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

本数据集依托国际劳工组织（ILO）的ILOSTAT数据库构建，通过REST API直接拉取指标`UNE_TUNE_SEX_AGE_MTS_NB`的原始数据，并依据欧洲ISO3国家代码进行地理筛选。ILOSTAT对各国劳动力调查、住户收支调查及行政记录等微观数据，采用国际劳动统计学家会议（ICLS）定义进行统一协调与标准化，确保了跨国可比性。数据集最终由Electric Sheep Europe团队整理、规范为Parquet格式，以便于机器学习的直接调用。

特点

数据集涵盖1983至2025年间39个欧洲国家的19.5万余条失业观测记录，按性别、年龄及婚姻状况三个维度进行精细分层。其特色在于不仅提供失业人数（千计）的核心指标，还保留了数据来源、观测状态标识及系列断裂等元数据注释，便于用户评估数据质量。数据集支持分类、回归与时间序列预测三项任务，且所有观测均以年度频率呈现，为长周期劳动经济分析提供了坚实依据。

使用方法

用户可通过HuggingFace的`datasets`库以`load_dataset`函数一键加载数据，并轻松转换为Pandas DataFrame进行后续操作。典型应用包括：按国家代码过滤获取单一国家的失业序列，提取单一指标并按时间排序绘制趋势图，或使用透视表构建国家×年份的矩阵面板数据。推荐在论文或报告中同时引用ILO原始来源及Electric Sheep Europe的重新打包版本，并遵循CC-BY-4.0许可协议。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）的ILOSTAT数据库整理并经由Electric Sheep Europe于2025年重新打包发布，旨在提供欧洲39个国家自1983年至2025年间按性别、年龄和婚姻状况划分的失业人数（单位：千）的详细统计信息。作为劳动经济学与社会科学研究的重要资源，该数据集涵盖了近20万条观测记录，整合了来自劳动力调查、家庭收入调查等多源行政数据，并经ILO依据国际劳工统计学家会议（ICLS）标准进行统一规范化处理。其核心研究问题聚焦于揭示欧洲劳动力市场中不同社会人口群体在失业维度上的时空分布特征与演变趋势，为相关政策制定、跨国比较分析及时间序列预测提供了坚实的数据基础。该数据集对劳动经济学、人口社会学以及区域发展研究领域具有显著的推动作用，尤其促进了欧洲一体化进程中劳动力市场异质性的量化研究。

当前挑战

该数据集的构建与应用面临多重挑战。在领域问题层面，其核心在于解决劳动力统计中多维度细分（性别、年龄、婚姻状况）下的失业率精确度量与跨国可比性难题，即如何在不同国家多样的调查方法与定义差异中，通过ILO的统一标准（如ICLS定义）实现数据的协调与整合，从而避免因方法论断裂导致的错误推断。在构建过程中，挑战首先体现在数据源的异构性上：ILOSTAT需从各国不同的劳动力调查、行政记录中提取原始数据，并处理“最佳来源”选择（当同一国家同年份存在多个来源时）及方法修订（如序列中断标记）所带来的不确定性。其次，时间跨度的不均衡性（如瑞士覆盖1991-2025年，而俄罗斯仅覆盖2010-2025年）与国家间缺失数据（如BIH数据止于2020年）的异质性，增加了时空分析的复杂度。此外，观察状态标记（如“unreliable”）与分类维度（如非标准年龄组）的存在，要求使用者具备专业的数据清洗与质量评估能力，以确保统计推断的稳健性，这构成了数据应用的隐性门槛。

常用场景

经典使用场景

该数据集汇聚了自1983年至2025年间欧洲39个国家的失业率统计数据，按性别、年龄及婚姻状况精细划分，提供了多达195,059条观测记录。在经典使用场景中，研究者常将其作为时间序列预测的基础数据源，利用历史失业波动特征训练模型以推测未来就业市场走势。此外，基于其多维分类属性，数据集亦广泛应用于人口统计学的分层建模，例如评估不同性别或年龄段人群的失业风险差异，从而揭示社会结构中的脆弱群体。

解决学术问题

在学术研究中，该数据集有效回应了劳动经济学领域关于结构性失业与周期性失业区分的难题。通过提供长达四十余年的连续观察，它使研究者得以量化经济危机、政策调整或技术变革对特定人群就业状况的冲击程度。数据集还支持对婚姻状态与失业关联性的因果推断，为家庭经济学中关于婚姻对劳动力供给影响的讨论提供了实证依据，助力构建更精确的社会保障干预模型。

衍生相关工作

基于该数据集衍生了多项经典工作，包括开发用于预测区域性失业峰值的机器学习模型，以及构建跨性别与年龄的不平等指数。部分研究将其与工资卡、教育评估等其他指标联动，生成了综合性的劳动市场健康度仪表盘。此外，该数据集还被作为基准用于验证新型时间序列分解算法或联邦学习框架在处理异构多源统计信息时的有效性，推动了数据科学在官方统计领域的方法论创新。