electricsheepeurope/europe-ilo-une-tune-sex-dur-mts-nb-unemployment-by-sex-duration-and-marital-status-th

--- 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, duration and marital status (thousands) | Europe (ILOSTAT)" --- # Unemployment by sex, duration and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **36,401 observations** · **39 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **36,401 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_DUR_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_DUR_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` | 1,615 | 1991 | 2025 | | `ITA` | 1,473 | 1992 | 2024 | | `GBR` | 1,430 | 1992 | 2025 | | `IRL` | 1,408 | 1992 | 2023 | | `CZE` | 1,386 | 1993 | 2024 | | `DEU` | 1,279 | 1992 | 2020 | | `ESP` | 1,267 | 1992 | 2025 | | `GRC` | 1,224 | 1987 | 2020 | | `AUT` | 1,174 | 1995 | 2025 | | `NLD` | 1,169 | 1992 | 2020 | | `PRT` | 1,145 | 1992 | 2020 | | `MDA` | 1,133 | 2000 | 2025 | | `FIN` | 1,112 | 1995 | 2020 | | `SWE` | 1,079 | 1995 | 2020 | | `BEL` | 1,073 | 1992 | 2020 | | ... | _24 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_DUR_MTS_NB` — Unemployment by sex, duration 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_DUR_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex, duration and mar…` | | `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.) | `DUR_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Duration (Aggregate): Total` | | `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` | — | `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-dur-mts-nb-unemployment-by-sex-duration-and-marital-status-th") 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_DUR_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_DUR_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_DUR_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_tune_sex_dur_mts_nb_unemployment_by_sex_duration_and_marital_status_th_2025, title = {Unemployment by sex, duration and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_DUR_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-tune-sex-dur-mts-nb-unemployment-by-sex-duration-and-marital-status-th}} } ``` ## 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_DUR_MTS_NB_This dataset, named Unemployment by sex, duration and marital status (thousands) | Europe (ILOSTAT), is a tabular dataset containing 36,401 observations across 39 European countries, spanning from 1987 to 2025. It includes a single indicator: UNE_TUNE_SEX_DUR_MTS_NB, which records unemployment figures (in thousands) disaggregated by sex, duration of unemployment, and marital status. The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via API and processed for machine learning tasks such as tabular classification, regression, and time-series forecasting. The dataset provides a detailed schema covering country codes, years, observed values, data sources, and disaggregation dimensions (e.g., sex), along with data quality notes and usage examples.

搜集汇总

数据集介绍

构建方式

该数据集基于国际劳工组织（ILO）的ILOSTAT统计数据库构建，通过直接调用其REST API接口（https://rplumber.ilo.org/data/indicator?id=UNE_TUNE_SEX_DUR_MTS_NB）获取原始数据，并依据ICLS（国际劳工统计学家会议）定义对各国调查微观数据进行协调标准化。数据集进一步筛选出欧洲39个国家的ISO3代码对应记录，最终整合为包含36,401条观测值的结构化表格。所有数据均附有来源标签（source.label），以确保其可追溯性。

使用方法

使用该数据集极为便捷，用户可通过Hugging Face的`datasets`库一行代码加载：`load_dataset("electricsheepeurope/europe-ilo-une-tune-sex-dur-mts-nb-unemployment-by-sex-duration-and-marital-status-th")`，随后可转换为Pandas DataFrame进行深入分析。典型操作包括按国家编码（如`ref_area == "DEU"`）筛选单一国家数据，按时间序列排序并可视化特定指标的观测值（obs_value），或利用透视表功能构建国家×年份的矩阵，便于横向比较和面板数据分析。

背景与挑战

背景概述

失业率作为衡量劳动力市场健康状况的核心指标，其在不同性别、失业持续时间和婚姻状况下的分布特征，为社会学、经济学及公共政策研究提供了深刻的洞察。该数据集源自国际劳工组织（ILO）的ILOSTAT数据库，由Electric Sheep Europe于2025年整理并发布，收录了1987年至2025年间39个欧洲国家的36,401条观测记录。其核心研究问题在于揭示欧洲各国失业人群在性别、失业时长与婚姻状态等维度上的结构性差异，从而支持劳动力市场分层分析、政策评估及跨国比较研究。作为ILO在劳动力统计领域的权威数据集，它为经济学家、社会学家及数据科学家提供了标准化、可复用的基础数据，对理解欧洲就业市场变迁、识别弱势群体及检验劳动力政策效果具有重要学术与公共价值。

当前挑战

该数据集的核心挑战在于应对劳动力市场数据的多维异质性与统计口径差异。首先，不同国家的失业定义与数据采集方法受国际劳工统计学家会议（ICLS）框架约束，但各国在调查设计、抽样方法与数据处理上存在差异，导致跨国比较时需谨慎处理数据一致性难题。其次，数据集包含性别（三类别）、失业时长（分类变量）与婚姻状态（分类变量）的交叉分解，细粒度分组导致部分国家-年份组合的观测值稀疏，可能引发统计稳定性问题。此外，数据来源于劳动力调查、行政记录等多种渠道，不同来源的时效性与偏差程度各异，且部分观测值被标记为“不可靠”（unreliable）或存在“系列中断”（break in series），提示用户需在分析中严格鉴别数据质量，并采用稳健方法处理缺失值与异常标记，以维护研究结论的可靠性。

常用场景

经典使用场景

该数据集收录了1987年至2025年间39个欧洲国家的失业统计数据，包含性别、失业时长及婚姻状况等多维度分层信息，共计36,401条观测记录。其经典使用场景聚焦于时间序列预测与面板数据分析，研究者可借助该数据构建失业率动态变化的预测模型，剖析性别差异与婚姻状态对就业市场韧性的交互影响。此外，该数据亦广泛应用于跨国家、跨时段的比较研究，用以揭示欧洲各国在劳动力结构调整过程中的异质性路径，为宏观经济学与劳动社会学领域的实证分析提供了坚实的数据基石。

解决学术问题

在学术研究层面，该数据集有效解决了欧洲失业问题中多维交叉分层数据的稀缺性难题。传统失业研究常受限于粗粒度汇总数据，难以精准刻画性别、失业持续时间与婚姻状况之间的复杂关联。此数据集的引入使得研究者能够深入探讨长期失业者的婚姻模式变迁、女性在劳动力市场中的结构性劣势，以及不同社会保护体系下失业人群的分化特征。其深远意义在于推动了劳动经济学中关于弱势群体就业壁垒的理论深化，并为国际劳工组织所倡导的体面劳动议程提供了量化评估工具。

实际应用

在实际应用场景中，该数据集为欧洲各国劳动政策制定者与跨国经济组织提供了精准的决策支持。劳工部门可依据不同性别与婚姻状态的失业时长分布，设计更具靶向性的再就业培训项目，优化社会保障资源的分配效率。同时，金融与咨询机构可借助该数据的趋势分析，预测区域劳动力供给变化对经济增长的潜在冲击，进而在投资布局与人力资源规划中做出前瞻性调整。数据集的标准化架构与ILOSTAT官方认证亦使其成为国际比较研究的可靠参照基准。

数据集最近研究

最新研究方向

面向欧洲劳动力市场的失业率多维解构与时间序列预测研究。该数据集源自国际劳工组织ILOSTAT数据库，涵盖了1987至2025年间39个欧洲国家按性别、失业时长及婚姻状况划分的失业人数记录，共计36,401条观测值。在2025年全球就业形势分化、结构性失业与技能错配加剧的背景下，该数据集为剖析不同群体失业脆弱性、评估劳动力市场政策效应提供了珍贵的高颗粒度时序面板。当前前沿方向聚焦于利用性别-时长-婚姻状况的交叉分解维度，结合机器学习模型（如LightGBM、Transformer）捕捉非线性时序依赖，以提升失业短期预警与区域间异质性分析的能力。同时，该数据与ILO的ICLS统计标准对齐，便于与其它劳动力指标（如就业率、工时）进行联合建模，从而推动基于证据的欧洲就业战略制定与SDG体面工作目标的监测。