electricsheepeurope/europe-ilo-une-deap-sex-age-dsb-rt-unemployment-rate-by-sex-age-and-disability-status

--- 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 rate by sex, age and disability status (%) | Europe (ILOSTAT)" --- # Unemployment rate by sex, age and disability status (%) | Europe (ILOSTAT) 🇪🇺 **39,164 observations** · **32 Europe countries** · **2002–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **39,164 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_DEAP_SEX_AGE_DSB_RT) - **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_DEAP_SEX_AGE_DSB_RT` 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` | 1,675 | 2005 | 2025 | | `ESP` | 1,515 | 2004 | 2024 | | `ITA` | 1,512 | 2004 | 2024 | | `GRC` | 1,464 | 2004 | 2024 | | `FRA` | 1,458 | 2004 | 2024 | | `PRT` | 1,455 | 2004 | 2024 | | `FIN` | 1,395 | 2004 | 2024 | | `AUT` | 1,387 | 2004 | 2024 | | `BEL` | 1,387 | 2004 | 2024 | | `POL` | 1,378 | 2005 | 2024 | | `SVN` | 1,365 | 2005 | 2024 | | `IRL` | 1,363 | 2004 | 2024 | | `EST` | 1,344 | 2004 | 2024 | | `LUX` | 1,320 | 2004 | 2024 | | `SWE` | 1,318 | 2004 | 2024 | | ... | _17 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_AGE_DSB_RT` — Unemployment rate by sex, age and disability status (%) ## 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_DEAP_SEX_AGE_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, age and dis…` | | `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 | `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) | `22.032` | | `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-deap-sex-age-dsb-rt-unemployment-rate-by-sex-age-and-disability-status") 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_DEAP_SEX_AGE_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_AGE_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_AGE_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_deap_sex_age_dsb_rt_unemployment_rate_by_sex_age_and_disability_status_2025, title = {Unemployment rate by sex, age and disability status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_AGE_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-deap-sex-age-dsb-rt-unemployment-rate-by-sex-age-and-disability-status}} } ``` ## 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_DEAP_SEX_AGE_DSB_RT_This dataset contains 39,164 observations of unemployment data across 32 European countries, spanning from 2002 to 2025, with the primary indicator being the unemployment rate by sex, age, and disability status (%). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via REST API and filtered to European countries. It includes detailed column structures such as country codes, indicator codes, sex disaggregation (total, male, female), age groups, disability status, year, and observed values, along with source and quality notes. It is 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中央统计数据库，经由Electric Sheep Europe团队重新封装，以HuggingFace数据集形式发布。构建过程通过调用ILOSTAT REST API直接提取指标UNE_DEAP_SEX_AGE_DSB_RT的原始数据，并依据欧洲ISO3国家代码进行区域过滤，最终整合为包含39,164条观测记录的规整化表格。数据统一采用ICLS劳动统计学家国际会议的定义标准，对各国劳动力调查、住户收入调查等原始微观数据进行协调处理，来源信息通过source.label字段明确标注，确保数据溯源的可追溯性与方法学一致性。

特点

数据集覆盖32个欧洲国家，时间跨度从2002年至2025年，聚焦于按性别、年龄组及残疾状况划分的失业率单一指标。其核心特色在于多维度的分类维度，包括性别（总计、男性、女性）、年龄分层（青年与成年人，15岁以上）以及残疾状态（总体、有残疾、无残疾），使得用户能够精细剖析不同群体间的失业率差异。此外，数据质量标识（如provisional、unreliable）和注释字段（如方法修订、定义变更）的纳入，为统计口径的波动提供了透明记录，极大提升了二次分析的严谨性。

使用方法

研究者可通过HuggingFace的datasets库以一行代码加载数据：`load_dataset('electricsheepeurope/europe-ilo-une-deap-sex-age-dsb-rt-unemployment-rate-by-sex-age-and-disability-status')`，并便捷地转换为Pandas DataFrame进行后续操作。典型用例包括按国家过滤（如`df[df['ref_area'] == 'DEU']`）获得德国时序数据、按指标筛选后绘制失业率趋势图，或利用pivot_table构建国家×年份的矩阵以进行横截面比较。数据的高结构化特性使其天然适配于表格分类、回归分析及时间序列预测等机器学习任务，为欧洲劳动市场的量化研究提供了即用型基础资源。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）下属的ILOSTAT数据库创建，Electric Sheep Europe于2025年重新打包发布，包含2002年至2025年间32个欧洲国家的39,164条观测记录。ILOSTAT作为全球劳动统计领域的权威来源，通过整合各国劳动力调查、家庭收支调查等原始微观数据，并依据国际劳动统计学家会议（ICLS）定义进行标准化处理，为研究失业率的多维分布提供了坚实的数据基础。数据集核心研究问题聚焦于按性别、年龄和残疾状况划分的失业率差异，其价值在于揭示结构性就业不平等，尤其是边缘群体（如残疾人、青年或女性）在劳动力市场中的脆弱处境。作为ILOSTAT指标UNE_DEAP_SEX_AGE_DSB_RT的区域化子集，该数据集为欧洲劳动经济学、社会政策评估及可持续发展目标（SDG）中的体面工作监测提供了关键支撑，显著降低了研究者获取高质量、细粒度劳动统计数据的门槛。

当前挑战

该数据集所解决的领域挑战在于量化并分析劳动力市场中由性别、年龄和残疾状况交织形成的结构性失业差异。由于各国对残疾的定义标准（如ICLS建议与本国法律定义）存在歧异，跨国的失业率可比性面临方法论挑战，数据本身亦伴随因调查方法修订或来源变更导致的序列断裂问题（如注释字段'note_indicator.label'所示）。构建过程中，ILO需协调32个国家多样化数据源（包括劳动力调查、行政记录等），通过自动选择'最佳来源'解决同一国家-年份的多源冲突，并处理离散性标注矩阵（如'sex''classif1''classif2'的层级组合）以保证数据完整性。此外，年度观测频率限制了短期季节波动分析，而部分指标的缺失值（如某些细分群体的观测不可靠标记'obs_status'为U）进一步增加了模型泛化的不确定性，需依赖严格的元数据解读与清洗策略来缓解偏差风险。

常用场景

经典使用场景

该数据集汇集了国际劳工组织ILOSTAT数据库中欧洲32个国家2002年至2025年间按性别、年龄及残疾状况分层的失业率数据，共计39,164条观测记录。作为一份精心整理的表格型时序数据，它天然适用于分类、回归以及时间序列预测等经典机器学习任务。研究者可借助该数据集构建预测模型，探索不同人口群体失业率的演变规律；亦可通过对比不同性别、年龄段及残疾状态下的失业模式，揭示劳动力市场中隐藏的结构性差异。其标准化的数据模式与丰富的分类维度，使得从单一国家的时间序列分析到跨国面板数据的计量建模均能流畅展开。

解决学术问题

该数据集为劳动经济学、社会保障学及人口学领域研究提供了解决关键学术问题的实证基础。它使得学者能够量化分析残疾状态与失业风险之间的关联，评估各国包容性就业政策的实际成效，并厘清性别与年龄在劳动力市场脆弱性中的交互效应。借助面板数据模型，研究者可控制国家固定效应与时间趋势，识别不同群体失业率差异的深层驱动因素。此外，数据集所覆盖的二十余年时间跨度，有助于探究经济周期波动对弱势就业群体的非对称冲击，为构建更具韧性的劳动力市场政策体系提供因果推断层面的支撑，推动了社会公平与包容性增长方面的理论发展。

衍生相关工作

围绕该数据集衍生了一系列具有影响力的研究工作与数据产品。在学术层面，它被用于构建跨国面板回归模型，检验积极劳动力市场政策对不同残疾群体的异质性效应；并催生了若干聚焦于欧盟就业包容性指标的时序分解与预测研究，推动了残疾经济学这一交叉学科的发展。在数据生态方面，Electric Sheep Europe团队基于本数据集标准化的模式，进一步推出了涵盖更多劳动指标的欧洲劳动力市场数据合集，形成了一套可复用的数据处理流水线。这些衍生工作不仅拓展了原始数据的分析维度，还为后续研究者提供了基准参考，降低了劳动经济学实证研究的进入门槛。