electricsheepeurope/europe-ilo-une-deap-sex-age-mts-rt-unemployment-rate-by-sex-age-and-marital-status

--- 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 marital status (%) | Europe (ILOSTAT)" --- # Unemployment rate by sex, age and marital status (%) | Europe (ILOSTAT) 🇪🇺 **195,059 observations** · **39 Europe countries** · **1983–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **195,059 observations** of `Unemployment` data across **39 Europe countries**, spanning **1983–2025**, 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_MTS_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_MTS_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 | |---------|-----:|-----------:|----------:| | `CHE` | 11,315 | 1991 | 2025 | | `GBR` | 10,931 | 1983 | 2025 | | `CZE` | 10,821 | 1993 | 2024 | | `POL` | 9,393 | 1997 | 2025 | | `AUT` | 8,213 | 1995 | 2025 | | `MDA` | 7,585 | 2000 | 2025 | | `FRA` | 7,565 | 2005 | 2024 | | `MKD` | 7,346 | 2005 | 2025 | | `RUS` | 6,732 | 2010 | 2025 | | `ESP` | 6,473 | 1986 | 2025 | | `IRL` | 6,241 | 1983 | 2023 | | `ALB` | 6,199 | 2002 | 2024 | | `ITA` | 5,974 | 1983 | 2024 | | `BIH` | 5,533 | 2001 | 2020 | | `DEU` | 5,139 | 1983 | 2020 | | ... | _24 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_AGE_MTS_RT` — Unemployment rate by sex, age and marital 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) | `BA:480` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `UNE_DEAP_SEX_AGE_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, age and mar…` | | `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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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` | — | `C6:1688` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding ages…` | | `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-mts-rt-unemployment-rate-by-sex-age-and-marital-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_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_AGE_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_AGE_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_une_deap_sex_age_mts_rt_unemployment_rate_by_sex_age_and_marital_status_2025, title = {Unemployment rate by sex, age and marital status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_AGE_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-une-deap-sex-age-mts-rt-unemployment-rate-by-sex-age-and-marital-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_MTS_RT_This dataset contains unemployment rate statistics for European countries from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator UNE_DEAP_SEX_AGE_MTS_RT — Unemployment rate by sex, age and marital status (%). It covers 39 European countries from 1983 to 2025, with 195,059 observations. Data is pulled from the ILOSTAT REST API and filtered to Europe ISO3 country codes. The dataset includes a detailed schema with columns such as country code, year, observed value, data source, sex disaggregation (total, male, female), age classification, marital status classification, and data quality notes (e.g., observation status and series breaks). Data is annual frequency and is suitable for tabular classification, regression, and time-series forecasting tasks. The dataset is repackaged by Electric Sheep Europe as part of a unified, ML-ready data layer for Europe on HuggingFace.

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核心统计数据库，通过REST API直接获取失业率指标数据，并基于欧洲ISO3国家代码进行地理范围筛选。数据构建过程中，ILOSTAT依据国际劳动统计学家会议（ICLS）的定义对原始调查微观数据进行规范化处理，确保跨国家、跨年份的指标可比性。数据源类型在source.label列中明确标注，以便追溯数据质量。最终汇聚了39个欧洲国家从1983年至2025年间共195,059条观测记录，形成涵盖性别、年龄与婚姻状况等多维分解结构的标准表格数据。

特点

该数据集的核心特点在于其丰富的多维分解维度与严谨的数据质量把控。除核心的失业率指标外，数据按性别（总人口、男性、女性）、年龄组及婚姻状况进行细致划分，为用户提供了多角度分析劳动力市场结构的可能。所有数据均为年度频率，当同一国家与年份存在多个数据源时，ILO选取权威性最高的‘最佳来源’以确保一致性。此外，数据集中包含了观测值状态标记以及系列断裂、方法修订等注释信息，为研究者评估数据可靠性与时间序列连续性提供了透明依据。

使用方法

用户可通过HuggingFace Datasets库便捷加载该数据集，仅需一行代码即可将195,059条观测以Pandas DataFrame形式导入分析环境。加载后，可按国家代码（ref_area）筛选特定国家的数据子集，或按指标代码提取完整的时间序列进行趋势分析。针对面板数据分析需求，用户可借助pivot_table函数将数据重塑为国家×年份的矩阵形式，便于进行跨国的失业率比较与回归建模。数据集的CLS兼容定义与标准化的Schema设计，使其既适用于传统的计量经济学分析，也适合作为机器学习模型中的表格型特征输入。

背景与挑战

背景概述

随着全球劳动力市场的复杂性日益增加，精准、细粒度的失业率数据成为宏观经济分析与社会政策制定的核心支撑。由国际劳工组织（ILO）统计部门编纂并经Electric Sheep Europe于2025年整合重构的“europe-ilo-une-deap-sex-age-mts-rt-unemployment-rate-by-sex-age-and-marital-status”数据集，汇集了1983年至2025年间39个欧洲国家的195,059条观测记录。该数据集聚焦于按性别、年龄及婚姻状况分类的失业率指标，依托ILOSTAT这一全球劳动统计权威数据库，遵循国际劳工统计学家会议（ICLS）定义进行标准化处理，为解决欧洲劳动力市场中结构性失业与人口特征关联等核心研究问题提供了跨时空的量化基础，在劳动经济学、社会分层及国际比较研究中具有深远影响力。

当前挑战

该数据集所应对的领域挑战在于克服传统失业率统计中粒度不足的问题。多数宏观失业率数据仅提供整体或有限分解，难以揭示不同性别、年龄段及婚姻状况人群间的就业脆弱性差异，尤其在欧洲老龄化与家庭结构变迁的背景下，此类细分信息对精准施策至关重要。构建过程中面临的挑战包括：跨国家数据来源的异质性，如各国劳动力调查采用不同抽样框架与问卷设计，需依赖ILO的统一协调与最佳源选择；时间序列中的方法论变更（如序列中断与修订标志）以及观测值的可靠性标记，要求使用者在分析前进行细致的质量过滤与断点调整，以确保多国跨年比较的有效性。

常用场景

经典使用场景

在欧洲劳动经济学与社会人口学研究中，该数据集常被用于分析失业率随性别、年龄及婚姻状况的演变规律。研究者可通过其丰富的分类维度（如SEX_M、SEX_F、AGE_YTHADULT_YGE15等）构建面板数据模型，探究不同群体在1983至2025年间失业率的动态差异。典型应用包括使用时间序列回归识别婚姻状态对劳动力市场脆弱性的缓冲效应，或通过分层贝叶斯模型捕捉年龄-时期-队列效应，从而揭示结构性失业的微观成因。

实际应用

在政策制定与公共管理领域，该数据集为欧盟就业战略的量化评估提供了直接支撑。各国劳动部门可利用其细分至性别与婚姻层面的失业率数据，精准定位高失业风险人群（如未婚青年女性），从而设计差异化的职业培训与就业补贴方案。此外，国际组织可借助该数据监测《欧洲2020战略》中关于提高就业率目标的实施进展，或为欧洲央行调整货币政策时的劳动力市场传导分析提供输入。

衍生相关工作

基于该数据集衍生的经典工作包括构建欧洲国家失业率预测的时序混合模型，例如结合Prophet与LightGBM捕捉经济周期与结构性变化对失业的影响。部分研究还将其与欧盟统计局（Eurostat）的GDP增长率数据融合，构建向量自回归（VAR）模型以分析失业与经济增长间的Okun系数。此外，基于该数据的分层分类任务催生了若干解释性机器学习框架，用以量化婚姻状况对失业持续时间的非线性调节效应。