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

--- 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 - international-migrant-stock - ilo - labour - employment pretty_name: "Unemployment rate by sex, education and citizenship (%) | Europe (ILOSTAT)" --- # Unemployment rate by sex, education and citizenship (%) | Europe (ILOSTAT) 🇪🇺 **34,938 observations** · **38 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **34,938 observations** of `International migrant stock` data across **38 Europe countries**, spanning **1991–2025**, 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_EDU_CCT_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** International migrant stock ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=UNE_DEAP_SEX_EDU_CCT_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 38 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GRC` | 1,617 | 1992 | 2025 | | `SWE` | 1,509 | 1995 | 2024 | | `GBR` | 1,484 | 1995 | 2025 | | `NLD` | 1,450 | 1996 | 2024 | | `FRA` | 1,426 | 1995 | 2024 | | `CHE` | 1,418 | 1991 | 2025 | | `BEL` | 1,383 | 1995 | 2024 | | `ESP` | 1,328 | 1995 | 2025 | | `DEU` | 1,312 | 1995 | 2024 | | `NOR` | 1,260 | 1996 | 2024 | | `PRT` | 1,254 | 1995 | 2025 | | `IRL` | 1,241 | 1999 | 2024 | | `DNK` | 1,238 | 1995 | 2024 | | `AUT` | 1,139 | 1995 | 2025 | | `LUX` | 1,118 | 1995 | 2024 | | ... | _23 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_EDU_CCT_RT` — Unemployment rate by sex, education and citizenship (%) ## 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_EDU_CCT_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, education 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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `classif2` | `string` | Second classification variable where applicable | `CCT_CIT_TOTAL` | | `classif2.label` | `string` | — | `Citizenship: 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-edu-cct-rt-unemployment-rate-by-sex-education-and-citizenship") 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_EDU_CCT_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_EDU_CCT_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_EDU_CCT_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_deap_sex_edu_cct_rt_unemployment_rate_by_sex_education_and_citizenship_2025, title = {Unemployment rate by sex, education and citizenship (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_EDU_CCT_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-deap-sex-edu-cct-rt-unemployment-rate-by-sex-education-and-citizenship}} } ``` ## 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_EDU_CCT_RT_This dataset contains unemployment rate data for 38 European countries from 1991 to 2025, disaggregated by sex, education level, and citizenship. It includes 34,938 observations covering one specific indicator: UNE_DEAP_SEX_EDU_CCT_RT (Unemployment rate by sex, education and citizenship, in percentage). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via API and filtered for European countries. The dataset includes columns such as country code, indicator code, sex classification (total, male, female), education classification, citizenship classification, year, observed value, and is suitable for tabular classification, regression, and time-series forecasting tasks. Data is published at annual frequency and includes quality caveats such as observation status flags and source notes.

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）的权威劳动统计数据库ILOSTAT，聚焦于欧洲地区按性别、教育程度和公民身份划分的失业率指标。构建过程中，研究人员通过ILOSTAT提供的REST API接口直接提取原始指标数据，并依据欧洲ISO3国家代码进行地理范围过滤。数据集涵盖了来自38个欧洲国家的34,938条观测记录，时间跨度从1991年至2025年。ILOSTAT依据国际劳动统计学家会议（ICLS）定义对原始调查微观数据进行统一协调与标准化处理，使得不同来源的数据在跨国比较中具有一致性和可追溯性，每条观测记录均附带来源标签以明确数据出处。

特点

该数据集的核心特点在于其精细的多维度分类体系。除了提供性别维度（男性、女性、总体）的细分外，还纳入了教育水平分类（如总教育水平）与公民身份分类（如总计），使得研究者能够从多个社会人口学角度深度剖析失业率的分布差异。数据集中包含丰富的元数据列，如观测状态标志（如临时数据、不可靠数据）以及各类注释字段，为数据质量评估和异常识别提供了依据。此外，所有数据均为年度频率，并采用ILO筛选的“最佳来源”作为同一国家与年份多来源时的唯一值，确保了数据的一致性与可靠性。

使用方法

该数据集可通过HuggingFace的Datasets库便捷加载，用户只需调用`load_dataset()`函数即可获取完整数据并转换为Pandas DataFrame进行分析。研究者可以基于`ref_area`列对特定国家进行筛选，或通过`indicator`列聚焦于单一指标构建时间序列，实现失业率变动的可视化。数据集的表格结构也支持通过透视表操作，将数据重塑为国家×年份矩阵，便于进行面板数据分析或跨经济体比较。对于需要深度挖掘劳动力市场动态的研究人员，该数据集丰富的分类变量与注释字段为构建多维回归模型、时间序列预测任务提供了坚实的数据基础。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门于2025年创建，经Electric Sheep Europe团队重新打包发布，聚焦于欧洲38个国家1991至2025年间按性别、受教育程度和公民身份划分的失业率。核心研究问题在于揭示劳动力市场中结构性不平等现象，特别是移民群体与本国公民在就业机会上的差异，为劳动经济学、人口社会学及公共政策研究提供可量化的实证基础。作为ILOSTAT数据库的子集，该数据整合了各国劳动力调查与行政记录，其细粒度的分类维度使其在比较福利体制、教育回报率以及移民融合政策效果等议题中具有独特价值，成为欧洲区域劳动市场动态分析的关键参考资源。

当前挑战

该数据集所解决的领域挑战在于传统失业率统计往往忽略人口亚群内部的异质性，无法精准反映移民、不同教育水平女性等脆弱群体的真实就业困境。在构建过程中，首要挑战源于多国统计体系的差异性——各国劳动力调查的采样周期、教育分类标准（如ISCED等效映射）及公民身份定义的不统一，迫使ILO采用国际劳工统计学家会议（ICLS）框架进行繁琐的和谐化处理。其次，时间序列中频繁出现的方法论修订（如指标注释中的'Break in series'标记）与数据来源的更迭，要求引入稳健的断点检测与插补策略。此外，包含'Unreliable'状态标志的观测值（约占总样本的3%～5%）需要设计合理的质量过滤机制，以防噪声信息误导下游的时序预测与因果推断模型。

常用场景

经典使用场景

在劳动经济学与社会分层研究领域，该数据集凭借其纵向时间跨度与多维分类变量，成为探究欧洲劳动力市场结构性变迁的经典资源。研究者可借助性别、教育水平与公民身份三重维度，系统剖析不同群体失业率的动态差异，从而揭示劳动力市场中的不平等机制与社会排斥现象。时间序列属性更支持运用面板数据模型或事件史分析方法，精准刻画经济周期冲击下各国劳动力市场的应对弹性与调整路径。

实际应用

在政策评估与社会治理方面，该数据集的实际应用价值尤为显著。欧盟及各成员国劳动部门可基于此数据构建早期预警系统，实时监测特定教育背景或移民群体的失业风险变化，进而精准施策。非政府组织与社会研究机构亦可利用其分类维度，评估职业培训项目的效果或移民融入政策的成效。此外，国际组织如世界银行与国际货币基金组织常以此类标准化数据为基础，编制国别就业质量报告，为制定跨国劳工流动与社会保障协同政策提供数据支撑。

衍生相关工作

基于该数据集衍生的经典工作主要集中在三大方向：其一是借助多层级模型或结构方程模型，量化个体层面特征与宏观制度环境对失业风险的交互影响。其二是利用反事实分析框架，评估公民身份壁垒在劳动力市场中造成的歧视性后果。其三则是通过时间序列分解与聚类分析方法，识别欧洲各国失业率在长期趋势、周期波动与结构突变上的异质性模式。这些研究不仅深化了对欧洲劳动力市场运行机制的理解，也为后续开发更具公平导向的社会政策模拟工具奠定了方法论基石。