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

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 1K<n<10K tags: - tabular - europe - ilostat - unemployment - ilo - labour - employment pretty_name: "Unemployment rate by sex and disability status (%) | Europe (ILOSTAT)" --- # Unemployment rate by sex and disability status (%) | Europe (ILOSTAT) 🇪🇺 **5,297 observations** · **34 Europe countries** · **2002–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **5,297 observations** of `Unemployment` data across **34 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_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_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 34 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `AUT` | 189 | 2004 | 2024 | | `BEL` | 189 | 2004 | 2024 | | `GBR` | 189 | 2005 | 2025 | | `ESP` | 189 | 2004 | 2024 | | `FRA` | 189 | 2004 | 2024 | | `FIN` | 189 | 2004 | 2024 | | `PRT` | 189 | 2004 | 2024 | | `ITA` | 189 | 2004 | 2024 | | `LUX` | 188 | 2004 | 2024 | | `GRC` | 188 | 2004 | 2024 | | `SWE` | 187 | 2004 | 2024 | | `IRL` | 183 | 2004 | 2024 | | `LVA` | 180 | 2005 | 2024 | | `SVN` | 180 | 2005 | 2024 | | `CZE` | 180 | 2005 | 2024 | | ... | _19 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_DSB_RT` — Unemployment rate by sex 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_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex and disabili…` | | `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.) | `DSB_STATUS_TOTAL` | | `classif1.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` | — | `I20:4077` | | `note_indicator.label` | `string` | — | `Employment definition: Excluding own-…` | | `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-dsb-rt-unemployment-rate-by-sex-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_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_deap_sex_dsb_rt_unemployment_rate_by_sex_and_disability_status_2025, title = {Unemployment rate by sex and disability status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-deap-sex-dsb-rt-unemployment-rate-by-sex-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_DSB_RT_This dataset contains unemployment rate data by sex and disability status (%) for Europe, sourced from the International Labour Organization (ILO) ILOSTAT database. It includes 5,297 observations across 34 European countries spanning from 2002 to 2025, with the key indicator being UNE_DEAP_SEX_DSB_RT, representing the unemployment rate percentage disaggregated by sex and disability status. The data is retrieved via the ILOSTAT REST API, filtered for European countries, and features columns such as country codes, sources, indicators, sex classifications, time years, observed values, and status flags. It is designed for tabular classification, regression, and time-series forecasting tasks, and is released under the CC-BY-4.0 license.

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）的核心统计数据库ILOSTAT，通过其REST API直接获取原始指标数据，并依据欧洲ISO3国家代码进行区域过滤。数据涵盖2002年至2025年期间34个欧洲国家的失业率观测值，共计5,297条记录。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查微观数据进行协调统一，并在source.label字段中标注数据来源，确保每一条观测的可追溯性。Electric Sheep Europe团队负责将原始数据重新打包为标准化格式，以Parquet文件形式发布，方便机器学习场景下的直接调用。

特点

本数据集的核心特点在于其聚焦于“按性别和残疾状况划分的失业率”这一细分指标（UNE_DEAP_SEX_DSB_RT），能够支持基于残疾状态的劳动力市场不平等分析。数据结构包含丰富的维度分层信息，如sex字段区分总人口、男性和女性，classif1字段提供残疾状态的分类标签。此外，数据集还附带了多个注释字段（note_classif、note_indicator、note_source），用于说明定义差异或数据可靠性标记（如obs_status），为用户理解数据质量边界提供了透明依据。时间跨度覆盖23年，空间维度囊括34个欧洲国家，为面板数据分析和时序预测研究奠定了坚实基础。

使用方法

用户可通过HuggingFace Datasets库中的load_dataset函数一键加载数据集，返回的Dataset对象可轻松转化为Pandas DataFrame进行后续操作。典型应用包括：按国家过滤以开展国别失业率分析，按时间排序绘制某一指标的时间序列折线图，或使用pivot_table方法将数据重塑为国家×年份的矩阵形式，便于面板回归或聚类分析。由于数据已按统一架构整理，研究人员可快速将其纳入现有的数据处理流程，无需对来源格式进行额外清洗。建议在使用时同时引用原始ILO数据和Electric Sheep Europe的重新包装版本，以尊重数据来源的规范性。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计司从ILOSTAT数据库中提取，经Electric Sheep Europe于2025年重新整理并发布于HuggingFace平台，专注于欧洲34个国家2002至2025年间按性别和残疾状态分组的失业率指标。ILOSTAT作为全球劳动统计的核心数据库，基于各国劳动力调查、家庭收入调查及行政记录等原始数据，并遵循国际劳动统计学家会议（ICLS）定义进行统一整合，为政策制定者、经济学家及社会科学家提供跨越二十余年时间维度的可比化微观数据。该数据集收录了5,297条观测记录，其精细化的维度拆分揭示出残疾群体在劳动力市场中长期存在的结构性劣势，为推进联合国可持续发展目标（SDGs）中体面工作与经济增长相关的实证研究提供了关键支撑。

当前挑战

该研究所针对的核心领域挑战在于：残疾群体在全球劳动力市场中长期面临系统性的就业障碍，而传统宏观失业率指标往往掩盖了按性别与残疾状况交叉切割后的深层不平等，导致针对性政策缺乏数据基础。构建过程中，ILOSTAT面临各国原始调查在残疾定义、调查周期及数据质量上的异质性问题，例如部分国别数据标注为“不可靠”（unreliable）或因非标准定义需要附加分类注释，同时跨年度序列中不同来源的优先选择规则（如“最佳来源”筛选）和仅在部分国家存在的细分维度增强了数据清洗与标准化难度。此外，该数据集仅收录年度频次，而部分指标亦存在月度或季度变体，限制了高频时序分析的适用性。

常用场景

经典使用场景

在欧洲劳动力市场研究中，该数据集常被用于分析性别与残疾状态双维度下的失业率差异及其长期演变趋势。研究者可借助其涵盖34国、跨度二十余年的面板数据结构，运用时间序列分析或混合效应模型，揭示不同社会群体在劳动力市场中的结构性脆弱性。数据集中精心标注的观测状态与来源说明，为构建稳健的计量模型提供了质量保障。

解决学术问题

该数据集直接回应了劳动经济学中关于多重弱势群体失业率测度的关键挑战。传统研究往往仅关注性别或残疾因素的单一影响，而本数据通过交叉分类的细化粒度，使学者能够系统评估性别与残疾状态的交互效应对就业机会的影响。其全面的地理和时间覆盖也支持跨国比较与政策冲击效应的识别，推动了包容性劳动力市场评估的方法论进步。

衍生相关工作

该数据已衍生出一系列关于社会包容与劳动力市场韧性的基准研究。基于其统计口径的清晰定义，学界发展出针对残疾失业率的预测模型，并与欧洲社会调查等微观数据融合分析。部分工作还将其作为核心输入，构建了衡量各国在联合国可持续发展目标中体面工作指标完成度的复合指数，推动了劳动统计数据的二次开发与整合应用。