electricsheepeurope/europe-ilo-une-tune-sex-age-dur-nb-unemployment-by-sex-age-and-duration-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, age and duration (thousands) | Europe (ILOSTAT)" --- # Unemployment by sex, age and duration (thousands) | Europe (ILOSTAT) 🇪🇺 **489,142 observations** · **40 Europe countries** · **1983–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **489,142 observations** of `Unemployment` data across **40 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_DUR_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_DUR_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 40 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ITA` | 21,476 | 1983 | 2024 | | `GBR` | 21,198 | 1983 | 2025 | | `FRA` | 21,012 | 1983 | 2024 | | `DEU` | 20,487 | 1983 | 2024 | | `NLD` | 19,275 | 1983 | 2024 | | `ESP` | 18,825 | 1986 | 2025 | | `GRC` | 17,664 | 1983 | 2024 | | `IRL` | 17,452 | 1983 | 2024 | | `BEL` | 17,342 | 1983 | 2024 | | `PRT` | 17,143 | 1986 | 2025 | | `CHE` | 17,087 | 1991 | 2025 | | `CZE` | 16,996 | 1993 | 2024 | | `DNK` | 16,606 | 1983 | 2024 | | `AUT` | 13,863 | 1995 | 2025 | | `FIN` | 13,714 | 1995 | 2024 | | ... | _25 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_AGE_DUR_NB` — Unemployment by sex, age and duration (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_DUR_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex, age and duration…` | | `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 | `DUR_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Duration (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` | — | `C7:2844` | | `note_classif.label` | `string` | — | `Nonstandard duration of unemployment:…` | | `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-dur-nb-unemployment-by-sex-age-and-duration-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_DUR_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_AGE_DUR_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_AGE_DUR_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_tune_sex_age_dur_nb_unemployment_by_sex_age_and_duration_thousands_2025, title = {Unemployment by sex, age and duration (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_AGE_DUR_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-tune-sex-age-dur-nb-unemployment-by-sex-age-and-duration-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_DUR_NB_This dataset contains unemployment data for 40 European countries from 1983 to 2025, with 489,142 observations. It is sourced from the ILOSTAT database of the International Labour Organization (ILO), extracted via REST API and filtered to European countries. The indicator is Unemployment by sex, age and duration (thousands), and includes dimensions such as country codes, sex disaggregation, age groups, and duration, suitable for tabular classification, regression, and time-series forecasting tasks.

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直接抽取与欧洲地区失业相关的指标数据，并依据ISO 3166-1 alpha-3国家代码进行地理范围筛选。ILOSTAT基于国际劳工统计学家会议（ICLS）的定义，对各国劳动力调查、家计调查及行政记录等原始微观数据进行了系统性的统筹与标准化处理。最终由Electric Sheep Europe团队将前述数据重新封装为便于机器学习应用的数据集，并发布至HuggingFace平台，确保了数据的可追溯性与可信度。

使用方法

研究者可通过HuggingFace的`datasets`库直接加载该数据集，调用`load_dataset()`函数即可快速获得一个DataFrame结构。基于该结构，用户能够轻松利用`ref_area`列进行单国别筛选，例如过滤出德国（DEU）的数据进行分析。同时，数据集的`time`与`obs_value`列天然支持时间序列的可视化与建模，通过简单的分组与排序操作即可绘制失业率演变曲线。此外，借助`pivot_table`功能，可便捷地将数据重塑为国家×年份的面板矩阵，为横截面或面板数据计量模型提供了直接的数据入口。

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团队重新封装于HuggingFace平台，旨在提供欧洲地区按性别、年龄和失业时长分组的失业数据（单位：千人）。ILOSTAT作为全球劳动统计的核心数据库，长期致力于整合各国劳动力调查与行政记录，为政策制定与学术研究提供可靠依据。该数据集覆盖40个欧洲国家、时间跨度从1983年至2025年，总计近49万条观测记录，聚焦失业这一关键劳动市场指标，通过精细的人口统计分组揭示了不同群体的失业结构性特征。其发布大大降低了获取高质量、长时序欧洲失业数据的门槛，对劳动经济学、社会政策评估及跨国比较研究具有重要价值。

当前挑战

该数据集聚焦的领域挑战在于，失业率不仅受宏观经济周期影响，还存在显著的性别、年龄及失业时长异质性，传统总量指标难以捕捉结构性失业的演变与政策干预的靶点。构建过程中核心挑战包括：各国劳动力调查的统计口径与调查方法不一致（如ICLS定义的差异），需通过ILO的统一协调与最佳来源选择来保证跨国家、跨时序的可比性；部分指标存在数据缺失、修订与序列断裂，需借助观测状态标记（如provisional、unreliable）进行质量控制；此外，数据按年发布，不包含更高频率的月度或季度观测，限制了季节性失业动态的精细分析。

常用场景

经典使用场景

欧洲失业数据集的经典使用场景根植于劳动经济学与公共政策研究的交叉领域。该数据集提供了1983至2025年间覆盖40个欧洲国家、横跨近五十万条观测的失业数据，并按照性别、年龄与失业持续时间进行精细分层。研究人员常借助这一丰富的结构化面板数据，构建时间序列模型或面板数据回归，以揭示欧洲各国失业率的长期演变趋势、周期性波动特征，以及不同亚群体（如青年与成年、男性与女性）在劳动力市场中的异质性表现。此外，该数据集也是进行跨国产出缺口估算、贝弗里奇曲线拟合以及失业持续时间分析的标准数据源之一。

解决学术问题

该数据集的问世有效回应了欧洲劳动力市场研究中长期存在的数据碎片化与口径不统一问题。利用来自国际劳工组织标准化且经过协调的统计指标，研究者能够系统性地探讨宏观经济 shocks 对不同性别与年龄段失业群体的异质性冲击，分析失业持续期的结构性变化及其对劳动力市场刚性的指示意义。同时，该数据集为检验效率工资理论、搜寻与匹配模型等经典劳动经济学假说提供了跨国的实证基础，推动了关于欧洲福利制度设计与积极劳动力市场政策效果的因果识别研究，深化了对失业回滞现象和技能错配等结构性问题的学术理解。

实际应用

在实际应用层面，该数据集是欧洲各国政府劳动部门、国际组织、中央银行以及经济智库进行劳动力市场监测与政策评估的核心工具。政策制定者能够依据按年龄、性别和失业时长细分的动态失业指标，精准识别脆弱群体，设计更具针对性的就业促进计划和职业培训项目。例如，通过跟踪青年失业率对经济周期的敏感度，可以及时调整青年保障计划与实习补贴策略。此外，该数据集还广泛应用于商业咨询公司进行欧洲市场进入风险评估、跨国企业的人力资源战略规划，以及金融投资机构构建基于劳动力市场状况的经济景气预测模型。

数据集最近研究

最新研究方向

该数据集整合了国际劳工组织ILOSTAT数据库中欧洲40国、跨越四十余年的失业率精细指标，聚焦于按性别、年龄与失业持续时间分层的宏观经济记录，为劳动经济学与政策评估提供了高质量的长时段面板数据。在当前全球就业市场脆弱复苏与结构性失衡加剧的背景下，研究者可借助该多维度时间序列对周期性失业与长期失业的动态演变进行计量建模，亦可结合性别平等与青年就业等热点议题展开异质性分析，从而揭示劳动力市场中隐蔽的结构性困境。其高频率的分层编码体系不仅为预测类模型提供了丰富的特征工程空间，也为监测欧洲各国应对失业危机的政策效果提供了坚实的数据基石，在区域经济社会发展研究中具有不可替代的前沿价值。