electricsheepeurope/europe-ilo-une-deap-sex-geo-rt-unemployment-rate-by-sex-and-rural-urban-areas

--- 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 rural / urban areas (%) | Europe (ILOSTAT)" --- # Unemployment rate by sex and rural / urban areas (%) | Europe (ILOSTAT) 🇪🇺 **8,188 observations** · **39 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **8,188 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_DEAP_SEX_GEO_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_GEO_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 39 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 351 | 1987 | 2025 | | `FRA` | 297 | 1992 | 2024 | | `BEL` | 297 | 1992 | 2024 | | `DNK` | 297 | 1992 | 2024 | | `ESP` | 297 | 1992 | 2024 | | `DEU` | 297 | 1992 | 2024 | | `ITA` | 297 | 1992 | 2024 | | `PRT` | 297 | 1992 | 2024 | | `NLD` | 297 | 1992 | 2024 | | `IRL` | 288 | 1993 | 2024 | | `AUT` | 279 | 1995 | 2025 | | `FIN` | 276 | 1995 | 2024 | | `GBR` | 274 | 1992 | 2019 | | `SWE` | 270 | 1995 | 2024 | | `LUX` | 252 | 1997 | 2024 | | ... | _24 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_GEO_RT` — Unemployment rate by sex and rural / urban areas (%) ## 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_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex and rural / …` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `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` | — | `C12:2951` | | `note_classif.label` | `string` | — | `Rural / urban areas coverage definiti…` | | `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-geo-rt-unemployment-rate-by-sex-and-rural-urban-areas") 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_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_deap_sex_geo_rt_unemployment_rate_by_sex_and_rural_urban_areas_2025, title = {Unemployment rate by sex and rural / urban areas (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-deap-sex-geo-rt-unemployment-rate-by-sex-and-rural-urban-areas}} } ``` ## 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_GEO_RT_This dataset contains 8,188 observations of unemployment rate by sex and rural/urban areas across 39 Europe countries from 1987 to 2025, based on ILOSTAT data from the International Labour Organization (ILO). It includes standardized fields such as country codes, sources, indicators, sex, time, and observed values, 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

该数据集源自国际劳工组织（ILO）旗下的ILOSTAT统计数据库，由Electric Sheep Europe团队从官方REST API直接采集并重新打包而成。数据通过调用特定指标接口（UNE_DEAP_SEX_GEO_RT）获取，并依据ISO 3166-1 alpha-3编码筛选出覆盖39个欧洲国家的观测记录。在构建过程中，ILOSTAT依据国际劳工统计学家会议（ICLS）的统一定义对各国原始调查微观数据进行标准化处理，确保跨国家、跨时段的劳动数据具有可比性。最终数据集以Parquet格式存储，通过HuggingFace Datasets库提供结构化访问，包含时间（1987-2025年）、性别及城乡地理分类等多维度信息。

特点

本数据集收录了8,188条覆盖39个欧洲国家的失业率观测值，时间跨度自1987年至2025年，聚焦于按性别和城乡区域划分的失业率百分比这一核心指标。数据具备精细的维粒度，包含性别（总、男、女）与地理覆盖类型（国家、城乡区域）两大分类，每条记录均附带来源标签与质量标注（如是否可靠、是否存在方法论修订），为追溯数据谱系提供了透明支持。此外，数据集经过权威机构标准化处理，具有跨年份、跨国的横向可比性，能够有效支撑时间序列分析、回归建模及分类任务。

使用方法

用户可通过HuggingFace Datasets库便捷加载该数据集，仅需调用`load_dataset`函数即可将数据读取为Pandas DataFrame格式，便于后续探索。针对单一国家分析，可通过`ref_area`列筛选特定ISO代码（如DEU代表德国）提取子集。对于时间序列应用，可依据`indicator`与`time`列排序并可视化失业率演变趋势。研究人员亦可利用`pivot_table`将数据重塑为国家×年份的矩阵结构，以进行面板数据分析或作为机器学习模型的输入特征。数据已覆盖训练集，直接适用于表格分类、回归及时间序列预测等任务场景。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年整理发布，经Electric Sheep Europe重新封装后收录于HuggingFace平台，聚焦于欧洲39个国家在1987至2025年间按性别和城乡区域划分的失业率数据，共计8,188条观测记录。ILOSTAT作为全球劳动统计的核心数据库，依托各国劳动力调查、家庭收支调查及行政记录等多元数据源，依据国际劳工统计学家会议（ICLS）标准进行协调统一，为劳动力市场研究提供了权威且标准化的数据基础。该数据集的发布显著增强了欧洲区域劳动经济学与政策分析的可及性，尤其适用于跨性别比较与城乡差异的时序研究，推动了基于细化维度的失业动态洞察。

当前挑战

该数据集核心解决的领域问题在于，传统失业率统计往往仅提供国家层面或总体性指标，难以揭示性别差异与城乡割裂对劳动力市场的异质性影响，从而制约了精准政策制定与社会公平性评估。构建过程中，主要挑战在于多国数据源的高度异质性——各国调查方法、抽样设计、问卷定义及质量控制标准不尽相同，ILO需通过复杂的清洗与协调流程以保证跨国可比性；此外，部分观测值因数据质量欠佳被标记为不可靠，且在时序上存在因方法论修订导致的序列断裂，这些因素共同构成了数据整合与长期趋势推导的核心难点。

常用场景

经典使用场景

该数据集汇聚了国际劳工组织ILOSTAT数据库中关于欧洲39个国家1987至2025年间失业率按性别与城乡区域划分的精细观测，共计8188条记录。在学术研究与政策分析中，它常被用于构建时间序列预测模型，以探析失业率的长期演变趋势及季节性波动特征。研究人员借助该数据集，可对欧洲各国劳动力市场的结构性差异进行横向比较，并利用其丰富的维度信息展开回归分析，从而揭示性别、城乡地理因素对就业机会分布的交互影响。

实际应用

在实际应用层面，该数据集是政府机构、国际组织及智库进行就业形势研判与政策制定的关键工具。政策制定者可据此监测各国及区域内部失业率的动态变化，针对性地设计面向女性及农村地区的就业促进计划。此外，数据集的高时间分辨率与标准化的分类体系使其成为经济预警系统的核心数据源，帮助预测就业市场拐点，并为企业投资布局与人力资源规划提供数据驱动的决策支持。

衍生相关工作

围绕该数据集，学术界衍生出多项具有影响力的研究工作。研究人员基于其长期序列数据，开发了针对欧洲劳动力市场的结构性向量自回归模型，用以量化货币与财政政策对就业的传导效应；同时，结合机器学习方法构建了失业率多步预测框架，显著提升了短期预警的精度。此外，该数据集还催生了关于城乡就业流动性与性别工资差距的动态面板数据分析，为欧盟“欧洲2020”战略中提升就业率的量化目标提供了复盘与修正的实证依据。