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

--- 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, marital status and disability status (thousands) | Europe (ILOSTAT)" --- # Unemployment by sex, marital status and disability status (thousands) | Europe (ILOSTAT) 🇪🇺 **38,010 observations** · **32 Europe countries** · **2002–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **38,010 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_MTS_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_MTS_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` | 1,582 | 2005 | 2025 | | `ESP` | 1,559 | 2004 | 2024 | | `ITA` | 1,535 | 2004 | 2024 | | `SVN` | 1,514 | 2005 | 2024 | | `PRT` | 1,504 | 2004 | 2024 | | `BEL` | 1,481 | 2004 | 2024 | | `FIN` | 1,453 | 2004 | 2024 | | `POL` | 1,411 | 2005 | 2024 | | `FRA` | 1,393 | 2004 | 2024 | | `GRC` | 1,380 | 2004 | 2024 | | `AUT` | 1,333 | 2004 | 2024 | | `HUN` | 1,302 | 2005 | 2024 | | `LVA` | 1,296 | 2005 | 2024 | | `CZE` | 1,288 | 2005 | 2024 | | `SVK` | 1,284 | 2005 | 2024 | | ... | _17 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_MTS_DSB_NB` — Unemployment by sex, marital status 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_MTS_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex, marital status a…` | | `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` | | `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-mts-dsb-nb-unemployment-by-sex-marital-status-and-disability") 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_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_MTS_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_MTS_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_tune_sex_mts_dsb_nb_unemployment_by_sex_marital_status_and_disability_2025, title = {Unemployment by sex, marital status and disability status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_MTS_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-tune-sex-mts-dsb-nb-unemployment-by-sex-marital-status-and-disability}} } ``` ## 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_DSB_NB_This dataset contains 38,010 observations of unemployment data across 32 European countries, spanning from 2002 to 2025, with a core indicator UNE_TUNE_SEX_MTS_DSB_NB representing unemployment by sex, marital status and disability status (in thousands). Sourced from the International Labour Organization (ILO)s ILOSTAT database, it covers harmonized labor statistics including employment, unemployment, wages, working time, child labor, informal economy, social protection, occupational injuries, and SDG decent work targets. The dataset provides detailed disaggregation dimensions such as sex (total, male, female), marital status, and disability status, and includes fields like country codes, year, observed values, data sources, observation status, and related notes. The data is published at annual frequency and is suitable for tasks like tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集由国际劳工组织（ILO）的ILOSTAT统计数据库原始数据经重加工而来。Electric Sheep Europe团队通过ILOSTAT REST API直接拉取指标代码为UNE_TUNE_SEX_MTS_DSB_NB的失业数据，并依据欧洲ISO3国家代码进行地理过滤。ILOSTAT本身采用国际劳工统计学家会议（ICLS）定义对各国劳动力调查的微观数据进行统一协调，确保不同来源的数据具备可比性，最终生成涵盖32个欧洲国家、时间跨度从2002年至2025年的38,010条观测记录。

使用方法

用户可通过HuggingFace Datasets库便捷调用，仅需一行代码`load_dataset`即可将数据加载为Pandas DataFrame进行后续分析。典型应用场景包括：按国家代码过滤以聚焦单一国家的失业特征；基于观测值列绘制指定指标的时间序列图；或利用数据透视功能构建以年份为行、国家为列的矩阵，用于面板数据回归或跨国产出对比。数据集兼容分类与回归任务，亦可用于时间序列预测建模。

背景与挑战

背景概述

在劳动经济学与社会政策研究中，失业率的精准测度与多维度分解是理解劳动力市场结构性失衡的关键。国际劳工组织（ILO）下属的ILOSTAT数据库作为全球劳动统计的权威来源，长期致力于整合各国劳动力调查数据，为政策制定提供科学依据。在此背景下，Electric Sheep Europe团队于2025年从ILOSTAT的REST API中提取并重封装了本数据集，聚焦欧洲32个国家从2002年至2025年间按性别、婚姻状况和残疾状态细分的失业人数（单位：千）。该数据集涵盖38,010条观测，通过统一的ICLS定义与ILO的“最优来源”选取机制进行数据协调，为研究劳动力市场中弱势群体的就业困境提供了跨国家、跨时间维度的标准化面板数据，有力推动了关于歧视、社会保障与包容性增长等议题的实证研究。

当前挑战

本数据集所致力解决的领域核心挑战在于，传统失业率统计常忽略人口子群体内部的异质性，导致针对女性、已婚者或残疾人群体的失业特征难以被精细刻画。构建过程中，数据集面临多重技术挑战：其一，ILOSTAT原始数据来自不同国家的劳动力调查、住户收入调查或行政记录，各来源在抽样设计、问卷措辞与定义条款上存在差异，ILO虽通过ICLS标准进行协调，但部分记录仍标注了“非标准残疾定义”或“方法论修订导致序列中断”等质量标记；其二，分类维度中婚姻状况与残疾状态的交叉分类常因样本量不足而产生不可靠观测值，数据集通过保留“obs_status”字段（如标记为“不可靠”）来警示使用者；其三，数据仅涵盖年度频率，未能纳入部分指标可能存在的月度或季度细分，且多来源情况下仅采用ILO选定的“最佳来源”，限制了数据颗粒度的进一步拓展。

常用场景

经典使用场景

该数据集汇聚了欧洲32个国家2002至2025年间按性别、婚姻状况及残疾状态划分的失业人数观测数据，共计38,010条记录。在时序预测与分类任务的学术探索中，研究者常将其作为基准数据集，用于构建和评估失业率动态演变的统计学习模型，例如ARIMA、Prophet或基于Transformer的时间序列预测架构。此外，结构化表格数据特性使其成为多分类逻辑回归、梯度提升树及图神经网络在劳动经济领域应用验证的经典案例，尤其适用于分析人口特征维度（如性别与残疾状态）对就业市场影响的交叉效应。

解决学术问题

该数据集直面劳动经济学中长期存在的交叉性失业归因难题，即如何量化性别、婚姻状况与残疾状态三重属性交织下的失业风险差异。通过提供ILO标准化统计口径下的年度面板数据，它使学者能够克服传统调查样本量不足的局限，利用固定效应模型或分层贝叶斯方法识别残障人群在劳务市场中的结构性脆弱性。其意义在于为欧盟社会包容性政策的循证评估提供了可复现的数据基座，推动了关于《欧洲残疾战略》实施效果的计量研究，并促使学界重新审视婚姻状态作为劳动力供给决策缓冲机制的学术假设。

实际应用

在政策制定与社会治理实践中，该数据集直接服务于欧洲各国劳动局与欧盟统计局的失业监测预警系统。数据科学家可基于其构建动态仪表盘，实时追踪特定人群（如已婚残疾女性）的就业波动，辅助社保支出的精准预算。非营利组织则利用其进行区域间就业公平性比对，为游说立法机构完善《残疾人权利公约》本土化落地提供量化证据。在商业领域，人力资源咨询公司通过分析历史失业趋势，为企业制定残障员工招聘配额及弹性工作制度设计提供数据支撑，优化雇主品牌的社会责任评估。

数据集最近研究

最新研究方向

当前，该数据集在劳动经济学与社会政策交叉领域的前沿研究聚焦于多维弱势群体的失业动态精细刻画。借助ILOSTAT统一方法论与ICLS定义，研究者得以穿透传统失业率均值，深入剖析性别、婚姻状况与残疾状态等交叉维度对就业脆弱性的差异化影响。伴随欧洲各国推动包容性劳动力市场与残疾人就业保障立法，该数据集为量化政策效果、揭示结构性不平等提供了高分辨率的时间序列证据。其覆盖2002至2025年的跨期跨度与32国面板结构，使得跨国比较与周期性失业风险的异质性分析成为可能，尤其在新冠疫情后复苏阶段及地缘冲突引致的经济波动情境下，对残障群体就业缓冲机制的实证研究正成为该领域的前沿热点。