electricsheepeurope/europe-ilo-une-tune-sex-edu-dsb-nb-unemployment-by-sex-education-and-disability-statu

--- 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, education and disability status (thousands) | Europe (ILOSTAT)" --- # Unemployment by sex, education and disability status (thousands) | Europe (ILOSTAT) 🇪🇺 **21,424 observations** · **32 Europe countries** · **2002–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **21,424 observations** of `Unemployment` data across **32 Europe countries**, spanning **2002–2025**, 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_DSB_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_DSB_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 32 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GBR` | 1,086 | 2005 | 2025 | | `FRA` | 937 | 2004 | 2024 | | `ESP` | 890 | 2004 | 2024 | | `ITA` | 890 | 2004 | 2024 | | `BEL` | 864 | 2004 | 2024 | | `GRC` | 831 | 2004 | 2024 | | `PRT` | 829 | 2004 | 2024 | | `FIN` | 826 | 2004 | 2024 | | `LUX` | 741 | 2004 | 2024 | | `SWE` | 734 | 2004 | 2024 | | `AUT` | 731 | 2004 | 2024 | | `SVN` | 729 | 2005 | 2024 | | `DNK` | 708 | 2004 | 2024 | | `POL` | 696 | 2005 | 2024 | | `BGR` | 683 | 2007 | 2024 | | ... | _17 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_EDU_DSB_NB` — Unemployment by sex, education and disability 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) | `BB:7401` | | `source.label` | `string` | Source name in English | `HIES - Living Standards Survey` | | `indicator` | `string` | ILOSTAT indicator code | `UNE_TUNE_SEX_EDU_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex, education and di…` | | `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 | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2012` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `207.786` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C14:6260` | | `note_classif.label` | `string` | — | `Nonstandard definition of disability:…` | | `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-dsb-nb-unemployment-by-sex-education-and-disability-statu") 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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_EDU_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_EDU_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_tune_sex_edu_dsb_nb_unemployment_by_sex_education_and_disability_statu_2025, title = {Unemployment by sex, education and disability status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_EDU_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-tune-sex-edu-dsb-nb-unemployment-by-sex-education-and-disability-statu}} } ``` ## 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_DSB_NB_This dataset contains unemployment data for 32 European countries from 2002 to 2025, focusing on unemployment disaggregated by sex, education, and disability status (in thousands). It includes 21,424 observations covering one core indicator (UNE_TUNE_SEX_EDU_DSB_NB). The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, a leading global source for labour statistics, compiled from national labour force surveys, household income surveys, establishment surveys, and administrative records. The dataset is processed and filtered to include only European country ISO3 codes, harmonized using International Conference of Labour Statisticians (ICLS) definitions. The schema includes columns such as country code, country name, source code, indicator code, sex classification, education classification, disability status classification, year, observed value, observation status, and related notes. Data is provided at annual frequency and includes disaggregation dimensions like sex (total, male, female), though disaggregation columns are non-null only when the indicator publishes that breakdown. The dataset is suitable for tabular classification, tabular regression, and time-series forecasting tasks, useful for studying European labour market trends, unemployment rate analysis, and policy evaluation. It is packaged in Parquet format for machine learning readiness, with usage examples such as loading data, filtering by country, time-series analysis, and pivoting. The dataset is released under the CC-BY-4.0 license, requiring citation of both the original source and the repackaging by Electric Sheep Europe.

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核心统计数据库，通过其REST API接口直接拉取失业率指标数据，并筛选出覆盖32个欧洲国家的观测值。数据经过ILO统计部门依据国际劳工统计学家会议（ICLS）定义进行协调与标准化处理，确保各国劳动调查微观数据的一致性与可比性。为提升机器学习研究的便捷性，Electric Sheep Europe团队对原始数据进行了重新封装，将多维度分类变量（如性别、教育水平、残疾状况）整合为结构化表格，最终形成包含21,424条观测记录、时间跨度从2002年至2025年的年度面板数据。

使用方法

用户可通过HuggingFace Datasets库中的load_dataset()函数一键加载该数据集，并将其转换为Pandas DataFrame格式进行后续分析。针对特定国家的研究，支持通过ref_area字段进行筛选，例如提取德国（DEU）的子集。对于时间序列建模任务，可依据indicator字段筛选目标指标后按年份排序，利用obs_value列进行绘图或预测。数据还支持透视表操作，能够便捷地生成以时间为行、国家为列的矩阵，从而适用于跨国的面板数据分析或回归模型的构建。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年通过其核心统计数据库ILOSTAT发布，经Electric Sheep Europe重新封装后呈现在HuggingFace平台上。其核心研究问题聚焦于揭示欧洲32个国家在2002至2025年间，按性别、教育程度和残疾状况划分的失业人口规模（单位：千人）。作为ILO在劳动统计领域权威数据体系的一部分，该数据集为政策制定者与研究人员提供了评估劳动力市场结构性失衡、残疾人群就业壁垒以及教育对就业韧性影响的关键微观基础，对推动包容性就业政策设计与比较劳动经济学研究具有重要价值。

当前挑战

该数据集所应对的领域挑战在于，现有宏观失业数据往往高度聚合，缺乏按多个社会人口维度（如性别、教育、残疾状况）进行交叉细分的标准化时间序列，难以精准刻画弱势群体的就业困境。构建过程中面临的核心挑战包括：需从ILOSTAT API中原始调查微观数据出发，依据国际劳动统计学家会议（ICLS）定义进行跨国产出标准化与分类体系协调；需处理因各国调查方法（如劳动力调查与收入调查）不一致导致的数据可比性难题，并通过‘最佳来源’选择机制优化同一国家年份的多源冲突问题；还需应对数据质量注释（如方法论修订、定义变更）带来的潜在序列断点风险，确保长周期趋势分析的可靠性。

常用场景

经典使用场景

该数据集是国际劳工组织（ILO）发布的欧洲地区失业统计数据，涵盖了32个欧洲国家2002至2025年间按性别、教育水平和残疾状况划分的失业人数（单位：千人），总计21,424条观测记录。在劳动力经济学与社会分层研究中，该数据集被广泛用于分析结构性失业的群体差异，例如探究教育程度与失业风险之间的负相关关系是否因性别或残疾状况而异，或评估不同社会保护政策对弱势群体就业的影响。其丰富的分类维度支持多因素交互效应建模，为比较福利国家体制下的劳动力市场表现提供了可靠的数据基础。

解决学术问题

在学术研究层面，该数据集着力解决了将失业率、教育回报率与残疾就业障碍纳入统一分析框架的难题。传统研究常因缺乏细粒度分类数据而难以剥离性别、教育与残疾状况的独立影响，而该数据集的多元离散维度（如“sex”“classif1”“classif2”）允许研究者采用分层回归、倾向得分匹配或固定效应面板模型，识别不同群体失业率的时序趋势与国别异质性。这有助于澄清人力资本理论关于教育降低失业风险的普遍性主张是否同样适用于残疾群体，并揭示劳动力市场中“双重劣势”或“三重劣势”现象的存在证据，从而为包容性增长政策提供实证支撑。

实际应用

在实际应用场景中，该数据集为政策制定者和国际组织提供了实证证据支持。例如，欧盟委员会与各国劳工部门可依据按残疾状况与教育水平分解的失业数据，识别出最需要干预的脆弱群体，从而优化职业培训、就业补贴等劳力市场政策的定向投放。非政府组织亦能利用这些数据评估残疾融合政策的实际成效，或追踪不同性别群体在技能更新计划前后的失业率变化。此外，该数据集还常被用于跨国比较分析，帮助理解经济冲击（如新冠疫情）对不同群体的差异影响，进而设计更具韧性的社会保障体系。

数据集最近研究

最新研究方向

该数据集聚焦于欧洲32国2002至2025年间基于性别、教育程度与残疾状况的失业率统计，为劳动经济学中的交叉性不平等研究提供了珍贵的高粒度面板数据。在包容性增长与2030年可持续发展议程持续推进的当下，该数据集尤其契合三大前沿方向：一是用于构建多维度劳动力市场分割的量化模型，揭示残疾群体在不同教育层次下的结构性失业风险；二是结合欧洲统计局（Eurostat）与欧洲劳动调查数据，开展政策冲击评估——例如残疾人就业配额制改革或数字技能培训计划对失业动态的异质性效应；三是支撑时间序列预测与迁移学习研究，为后疫情时代劳动力韧性评估及欧盟《残障人士权利战略》实施效果监测提供可复用的基准。其ILOSTAT官方来源、ICLS统一定义的追踪字段及跨二十余年的跨度，使其成为衔接宏观劳动统计与微观福利分析的关键桥梁。