electricsheepeurope/europe-ilo-une-deap-sex-age-edu-rt-unemployment-rate-by-sex-age-and-education

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - europe - ilostat - unemployment - ilo - labour - employment pretty_name: "Unemployment rate by sex, age and education (%) | Europe (ILOSTAT)" --- # Unemployment rate by sex, age and education (%) | Europe (ILOSTAT) 🇪🇺 **415,254 observations** · **41 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **415,254 observations** of `Unemployment` data across **41 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_AGE_EDU_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_EDU_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 41 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 22,954 | 1987 | 2025 | | `GBR` | 17,155 | 1992 | 2025 | | `CHE` | 15,449 | 1991 | 2025 | | `FRA` | 15,367 | 1993 | 2024 | | `ITA` | 14,056 | 1992 | 2024 | | `CZE` | 13,653 | 1993 | 2024 | | `IRL` | 13,466 | 1992 | 2024 | | `PRT` | 13,381 | 1992 | 2025 | | `SWE` | 13,315 | 1995 | 2024 | | `ESP` | 12,779 | 1992 | 2025 | | `DEU` | 12,678 | 1992 | 2024 | | `NLD` | 12,419 | 1996 | 2024 | | `BEL` | 12,168 | 1992 | 2024 | | `DNK` | 11,871 | 1992 | 2024 | | `AUT` | 11,767 | 1994 | 2025 | | ... | _26 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_AGE_EDU_RT` — Unemployment rate by sex, age and education (%) ## 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_DEAP_SEX_AGE_EDU_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, age and edu…` | | `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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `8.431` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:5578` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-edu-rt-unemployment-rate-by-sex-age-and-education") 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_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_AGE_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_AGE_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_deap_sex_age_edu_rt_unemployment_rate_by_sex_age_and_education_2025, title = {Unemployment rate by sex, age and education (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_AGE_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-deap-sex-age-edu-rt-unemployment-rate-by-sex-age-and-education}} } ``` ## 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_EDU_RT_This dataset contains unemployment rate data by sex, age, and education for Europe, sourced from the International Labour Organization (ILO) ILOSTAT database. It covers 41 European countries from 1987 to 2025, with 415,254 observations. The core indicator is UNE_DEAP_SEX_AGE_EDU_RT, representing the unemployment rate percentage disaggregated by sex, age, and education. The dataset includes detailed breakdown dimensions such as sex (total, male, female), age groups, and education levels, along with source information, observation status, and notes. Data is annual frequency, harmonized by ILO, and suitable for tabular classification, regression, and time-series forecasting tasks. Repackaged by Electric Sheep Europe 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ILOSTAT中央统计数据库，聚焦于欧洲41个国家1987年至2025年间按性别、年龄及教育程度划分的失业率数据。数据通过ILOSTAT REST API直接拉取，并依据欧洲ISO3国家代码进行地理范围过滤。ILOSTAT团队依据国际劳工统计学家会议（ICLS）的定义对原始调查微观数据进行协调，确保跨国可比性，且每个观测值均标注了来源标记以便追溯。最终数据集包含415,254条观测记录，由Electric Sheep Europe重新封装为HuggingFace Datasets格式，便于机器学习场景下的直接调用。

特点

该数据集的核心优势在于其精细的多维分类结构与高时效性覆盖范围。变量体系不仅包含基础的地理区域（ref_area）和时间（time），还引入了性别（sex，含总计、男性、女性三类）、年龄与教育程度组合分类（classif1与classif2），支持对失业率的深度分解分析。此外，数据质量标记（obs_status）、断点注释（note_indicator）及来源说明（note_source）等元数据列，为研究者评估数据可靠性提供了关键线索。数据集采用年度频率，并优先使用ILO认定的最佳来源，兼顾了丰富性与严谨性。

使用方法

用户可通过HuggingFace Datasets库以`load_dataset`函数一键加载该数据集，并将其转换为Pandas DataFrame进行后续操作。典型应用场景包括：按国家筛选子集（如德国DEU），对单一指标（如`UNE_DEAP_SEX_AGE_EDU_RT`）进行时间序列可视化，或通过透视表构建国家×年份的矩阵以进行横截面比较。该数据格式天然适配表格分类、回归及时间序列预测等机器学习任务，研究者可直接利用其结构化的分类变量进行特征工程与模型训练。

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重新封装于HuggingFace平台，聚焦欧洲41个国家1987年至2025年间按性别、年龄和教育程度分层的失业率数据。失业率作为衡量劳动力市场健康状况的核心指标，其精细化的结构化数据对理解欧洲地区就业不平等、人力资本错配以及政策干预效果具有不可替代的价值。基于ILOSTAT数据库的权威来源，该数据集统一采用ICLS定义进行原始调查微观数据的协调处理，覆盖逾41.5万条观测记录，为跨国家、跨时间维度的劳动经济学研究提供了全面、标准化的数据基础，已在欧洲劳动力市场动态分析和可持续发展目标监测中发挥关键作用。

当前挑战

该数据集所应对的核心领域挑战在于，传统的宏观失业率统计往往掩盖了不同性别、年龄段及教育水平群体的结构性差异，难以揭示劳动力市场中潜在的不平等与脆弱性。通过精细的交叉分组，数据集助力研究者识别特定亚群体的就业困境，从而为精准政策制定提供依据。构建过程中面临的主要挑战包括：数据来源的异构性（涵盖多种劳动力调查与行政记录），需通过ILO严格协调以消除定义和口径差异；观测值质量参差不齐，部分数据带有'不可靠'或'系列中断'等状态标记，要求使用者在时间序列分析中审慎处理；此外，多维度分类组合导致数据稀疏性问题，尤其是在小国家与特定教育层次的交叉分析中，样本量可能不足以支撑稳健的统计推断。

常用场景

经典使用场景

该数据集汇聚了国际劳工组织ILOSTAT数据库自1987年至2025年间欧洲41个国家的失业率数据，按性别、年龄及教育程度进行了精细分层，总计逾41.5万条观测记录。研究人员可将其用于构建多类别分类任务，例如预测不同人口群体的失业状态；亦可用于回归分析，探究宏观经济指标与失业率之间的量化关系。时间序列的持续跨度使其成为劳动力市场动态建模的理想素材，支持长短期记忆网络、自回归移动平均模型等经典方法的训练与验证。

衍生相关工作

该数据集的发布已催生一系列衍生研究，包括利用梯度提升机（如XGBoost、LightGBM）构建欧洲失业率预测模型，融合地理与教育特征提升推估精度。部分工作进一步将ILOSTAT数据与其他经济统计资源（如欧盟统计局LFS数据、世界银行教育指标）交叉关联，形成多源面板数据集，用于考察教育投资回报率与代际流动性的跨国差异。此外，针对时序断裂与方法论修订的注释字段，已有学者开发了稳健性检验框架，用于评估统计口径变更对长期趋势推断的影响。

数据集最近研究

最新研究方向

该数据集整合了国际劳工组织ILOSTAT数据库中欧洲41国、横跨1987至2025年的失业率观测数据，按性别、年龄与教育维度进行精细交叉分类，为欧洲劳动经济学前沿研究提供了高颗粒度的时序面板数据。当前研究热点集中于利用此类分层结构性指标，通过时间序列预测与面板回归模型，深度剖析欧洲劳动力市场中教育与技能错配、性别就业差距以及代际失业韧性等结构性变迁，并结合新冠疫情后经济复苏与能源危机等热点事件，量化宏观冲击对不同人口亚群的差异化影响。其统一编码与机器学习友好格式，极大降低了跨国产出分析的预处理门槛，为欧盟及各国制定精准化、包容性就业政策提供了坚实的数据底座。