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

--- 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 and marital status (thousands) | Europe (ILOSTAT)" --- # Unemployment by sex and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **14,179 observations** · **39 Europe countries** · **1983–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **14,179 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_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_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` | 948 | 1991 | 2025 | | `CZE` | 765 | 1993 | 2024 | | `GBR` | 749 | 1983 | 2025 | | `MDA` | 652 | 2000 | 2025 | | `POL` | 651 | 1997 | 2025 | | `AUT` | 607 | 1995 | 2025 | | `MKD` | 565 | 2005 | 2025 | | `FRA` | 524 | 2005 | 2024 | | `ALB` | 472 | 2002 | 2024 | | `BIH` | 465 | 2001 | 2020 | | `ESP` | 435 | 1986 | 2025 | | `RUS` | 432 | 2010 | 2025 | | `IRL` | 417 | 1983 | 2023 | | `ITA` | 378 | 1983 | 2024 | | `LUX` | 343 | 1983 | 2020 | | ... | _24 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_MTS_NB` — Unemployment by sex 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_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex and marital statu…` | | `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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.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_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-mts-nb-unemployment-by-sex-and-marital-status-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_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_tune_sex_mts_nb_unemployment_by_sex_and_marital_status_thousands_2025, title = {Unemployment by sex and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-tune-sex-mts-nb-unemployment-by-sex-and-marital-status-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_MTS_NB_This dataset is a tabular dataset on unemployment in Europe, specifically focusing on unemployment numbers by sex and marital status (in thousands). It is sourced from the International Labour Organization (ILO) ILOSTAT database and repackaged by Electric Sheep Europe. It covers 39 European countries from 1983 to 2025, with 14,179 observations. The core indicator is UNE_TUNE_SEX_MTS_NB (Unemployment by sex and marital status in thousands), providing annual frequency data disaggregated by dimensions such as sex (total, male, female) and marital status. The dataset is suitable for tasks like tabular classification, tabular regression, and time-series forecasting, aiming to offer a standardized, ML-ready data layer for European labor market analysis. The schema includes columns like country code, year, observed value, data source, and quality flags for ease of use.

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）旗下的ILOSTAT中央统计数据库，聚焦于欧洲地区按性别与婚姻状况划分的失业人数（单位：千）。数据通过ILOSTAT提供的REST API接口直接获取，筛选出39个欧洲国家的ISO3国家代码，并依据国际劳工统计学家会议（ICLS）的标准化定义对原始调查微观数据进行协调处理。最终整合成包含14,179条观测记录、时间跨度从1983年至2025年的结构化表格，每条记录均附有数据来源标签以确保可追溯性。

特点

该数据集具备显著的跨时空覆盖性与精细的维度划分优势。它囊括了39个欧洲国家的长期失业数据，时间纵深超过四十年，为宏观劳动力市场分析提供了宝贵的历史与当代视角。核心特点在于其多维度的分类体系：不仅按性别（男性、女性、合计）进行拆解，还引入了婚姻状况作为关键分类变量，使得研究者能够深入探索不同社会群体在失业率上的结构性差异。数据还标注了观测状态与系列中断说明，极大增强了实证分析的可靠性。

使用方法

使用者可通过HuggingFace Datasets库的`load_dataset()`函数便捷加载本数据集，将其直接转化为Pandas DataFrame进行后续操作。典型用法包括：按国家代码（如'DEU'）筛选特定国家的子序列，从而绘制该国失业率的时间演化趋势；亦可将数据依据指标代码进行透视，生成以年份为行、国家为列的矩阵形式，便于开展面板数据分析或横向比较。数据集提供了标准化的列名与标签，方便进行集成与自动化处理，适用于时间序列预测、分类与回归等典型机器学习任务。

背景与挑战

背景概述

该数据集由Electric Sheep Europe基于国际劳工组织（ILO）的ILOSTAT数据库重新整理，创建于2025年，聚焦于1983至2025年间39个欧洲国家的失业数据，具体指标为“按性别和婚姻状况统计的失业人数（千人）”。ILOSTAT作为全球劳动统计的权威来源，依据国际劳工统计学家会议（ICLS）定义统合各国劳动力调查数据，为劳动经济学、社会政策及跨国比较研究提供了可靠基础。该数据集通过标准化架构将14,179条观测值以Parquet格式封装，支持时间序列分析与分类/回归任务，显著降低了研究者获取和利用欧洲失业数据的门槛，对于理解劳动力市场的性别与婚姻状况差异、推动相关计量建模与政策评估具有重要学术与实践价值。

当前挑战

该数据集应对的领域挑战在于揭示失业率的性别与婚姻状况分化，这是劳动经济学中长期关注但数据零散的核心问题，通过统一指标与跨国覆盖，使研究者能系统分析制度化差异与时间演变。构建过程中面临多重挑战：一是数据源头多样且频率不一，需从ILOSTAT REST API提取并筛选至欧洲国家，确保年度观测的连续性与可比性；二是融合多个国家的劳动力调查与行政记录时，须处理因方法论修订（如序列断裂）和来源标记导致的异质性，通过保留源标签与状态标志保证可追溯性；三是整理39国长时间序列数据时，需应对不完整记录（如某国缺失某些年份）及最佳来源选择，经由ILO默认的“最佳来源”策略平衡精度与覆盖率，最终产出高质量的时间序列数据集。

常用场景

经典使用场景

在欧洲劳动力市场研究领域，该数据集为学者提供了横跨39个国家、逾四十载时序的失业率微观统计样本。其经典使用场景聚焦于性别与婚姻状况双重维度下的失业人口分布建模，研究者可通过ILOSTAT统一协调的标准化指标，开展跨国面板数据分析。利用annual频率的观测值，能够捕捉经济周期对特定群体（如未婚男性或已婚女性）失业轨迹的差异化冲击，或运用分类特征构造马尔可夫链以模拟就业状态转换概率。数据集的年度粒度与长周期覆盖尤为适合构建自回归模型（ARIMA）、混合效应模型乃至基于深度学习的时间序列预测框架，为劳动经济学中的结构性失业研究提供了坚实的计量基础。

衍生相关工作

围绕此数据集，学术界与实践界已衍生出一系列标志性工作。在时间序列预测方向，研究者常将其作为基准数据集，评估季节性ARIMA与长短期记忆网络（LSTM）在捕捉失业周期非线性特征上的性能差异。因果关系推断领域涌现了利用性别-婚姻分层数据检验‘附加工作者假说’（Added Worker Effect）的经典论文，探讨当家庭主要劳动力失业时，其他成员（如已婚女性）是否被迫进入劳动市场。此外，该数据集推动了整合性就业景气指数的构建，例如学者将不同婚姻群组的失业率作为输入变量，合成能够提前预警经济衰退的复合先行指标。部分衍生工作还聚焦于数据质量，通过自动检测ILOSTAT中标注的‘断点’（Break in series）信息，开发鲁棒的数据插补算法以维持面板结构的连续性。

数据集最近研究

最新研究方向

在当前欧洲劳动力市场结构性变迁与性别平等议题持续升温的背景下，该数据集聚焦于性别与婚姻状况维度下的失业率时序分析，为探究婚姻状态如何调节男性与女性就业脆弱性提供了宝贵的高频观测窗口。其覆盖1983年至2025年、横跨39国的长周期面板数据，使得研究者能够捕捉后工业化时代家庭分工演变、单亲家庭经济风险以及婚育对女性劳动参与的远期影响。结合ILOSTAT统一的方法论框架与多种来源追踪标记，该数据集在时间序列预测、异质性劳动力群体分类以及宏观经济冲击下的性别差异化响应建模等前沿方向展现出独特价值，尤其为欧盟《性别平等战略2030》的量化评估与政策靶向优化提供了实证基础。