electricsheepeurope/europe-ilo-ged-xlu1-sex-hht-geo-rt-prime-age-unemployment-rate-by-sex-household-type

--- 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 - unemployment - ilo - labour - employment pretty_name: "Prime-age unemployment rate by sex, household type and rural / urban areas (%) | Europe (ILOSTAT)" --- # Prime-age unemployment rate by sex, household type and rural / urban areas (%) | Europe (ILOSTAT) 🇪🇺 **22,370 observations** · **27 Europe countries** · **2000–2024** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **22,370 observations** of `Unemployment` data across **27 Europe countries**, spanning **2000–2024**, 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=GED_XLU1_SEX_HHT_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=GED_XLU1_SEX_HHT_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 27 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ESP` | 1,296 | 2000 | 2023 | | `DEU` | 1,294 | 2000 | 2023 | | `AUT` | 1,293 | 2000 | 2023 | | `BEL` | 1,280 | 2000 | 2023 | | `HUN` | 1,241 | 2001 | 2023 | | `LVA` | 1,226 | 2001 | 2023 | | `EST` | 1,196 | 2000 | 2023 | | `LTU` | 1,184 | 2002 | 2023 | | `GRC` | 1,091 | 2001 | 2024 | | `GBR` | 1,075 | 2000 | 2019 | | `NLD` | 1,075 | 2000 | 2020 | | `HRV` | 1,028 | 2002 | 2023 | | `POL` | 1,025 | 2006 | 2024 | | `SVN` | 1,011 | 2005 | 2023 | | `IRL` | 970 | 2006 | 2023 | | ... | _12 more countries_ | | | ## Indicators (sample) - `GED_XLU1_SEX_HHT_GEO_RT` — Prime-age unemployment rate by sex, household type 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 | `GED_XLU1_SEX_HHT_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Prime-age unemployment rate by sex, h…` | | `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.) | `HHT_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Household type: Total` | | `classif2` | `string` | Second classification variable where applicable | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `time` | `int64` | Observation year | `2012` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `18.284` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_indicator` | `string` | — | `—` | | `note_indicator.label` | `string` | — | `—` | | `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-ged-xlu1-sex-hht-geo-rt-prime-age-unemployment-rate-by-sex-household-type") 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"] == "GED_XLU1_SEX_HHT_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="GED_XLU1_SEX_HHT_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "GED_XLU1_SEX_HHT_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ged_xlu1_sex_hht_geo_rt_prime_age_unemployment_rate_by_sex_household_type_2024, title = {Prime-age unemployment rate by sex, household type and rural / urban areas (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=GED_XLU1_SEX_HHT_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ged-xlu1-sex-hht-geo-rt-prime-age-unemployment-rate-by-sex-household-type}} } ``` ## 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=GED_XLU1_SEX_HHT_GEO_RT_This dataset contains prime-age unemployment rate data for 27 European countries from 2000 to 2024, disaggregated by sex, household type, and rural/urban areas. Sourced from the International Labour Organizations (ILO) ILOSTAT database, it is extracted via API and harmonized, covering 22,370 observations. The dataset includes the indicator code GED_XLU1_SEX_HHT_GEO_RT, with columns for country codes, years, observed values, and classification variables (e.g., sex with total, male, and female categories), suitable for analysis and forecasting of unemployment trends. Organized in tabular format, it supports tasks such as classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

本数据集源自国际劳工组织（ILO）的ILOSTAT数据库，通过REST API接口直接采集指标代码为GED_XLU1_SEX_HHT_GEO_RT的原始数据，并依据欧洲ISO3国家代码进行地理范围过滤。数据经过ILO统计部门依据国际劳工统计学家会议（ICLS）定义进行统一化处理，来源信息在source.label列中完整标记，确保了数据的可追溯性。最终由Electric Sheep Europe重新打包，形成包含22,370条观测值、覆盖27个欧洲国家、时间跨度从2000年至2024年的标准化表格数据集。

特点

该数据集聚焦于欧洲核心劳动力市场指标——按性别、家庭类型及城乡区域划分的壮年失业率，提供了多维度的细粒度分解。数据结构完整，包含sex（性别）、classif1（家庭类型）、classif2（地理覆盖范围）等多个分类维度，支持从总体到特定子群体的灵活分析。数据质量经过严格把控，优先采用ILO选定的最佳来源，并对不可靠的观测值进行标注，同时以年度频率呈现，适合进行跨国家、跨时间维度的比较研究与时间序列建模。

使用方法

研究者可通过HuggingFace Datasets库直接加载该数据集，使用load_dataset函数即可获取训练集并快速转换为Pandas DataFrame进行后续分析。典型应用包括按国家代码筛选特定国别数据、按指标列进行时序排序与可视化，或通过数据透视表构建国家×年份的指标矩阵。数据集兼容表格分类、回归及时间序列预测等多种任务类型，尤其适用于欧洲劳动力市场结构的纵向研究、区域差异比较以及机器学习模型的特征工程与预测建模工作。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2024年创建，经Electric Sheep Europe重新打包后发布于HuggingFace平台，聚焦欧洲27国2000至2024年间按性别、家庭类型及城乡区域划分的黄金年龄失业率。作为ILOSTAT数据库的核心指标之一，该数据集旨在揭示劳动力市场中结构性失业的微观差异，为经济学、社会学及政策研究提供精细化的时间序列数据支撑。其研究问题直指失业率在人口亚群中的异质性分布，尤其关注家庭结构与地理空间对就业状况的交叉影响。凭借逾2.2万条观测记录及标准化的ILO统计框架，该数据集已成为欧洲区域劳动力市场比较研究的重要基础资源，对推动公平就业政策评估与社会保障体系优化具有显著学术价值。

当前挑战

该数据集所应对的领域核心挑战为劳动力市场中的群体异质性失业问题——传统宏观失业率常掩盖不同性别、家庭类型及城乡群体间的结构性差异，导致政策干预难以精准施策。例如，单亲家庭女性或农村地区的黄金年龄劳动者可能面临系统性就业障碍，而汇总数据无法捕捉此类深层不公。在数据构建层面，挑战主要来自跨国家、跨年度原始调查数据的高度异质性：欧洲各国劳动力调查的样本设计、问卷框架及统计口径存在差异，ILO需通过ICLS标准进行协调统一；同时，部分小国或年份的数据因调查覆盖不足而出现缺失或标记为不可靠（如‘obs_status’字段中的不可用标记），需依赖插补或多源择优策略（如选择‘最佳来源’）以平衡完整性与准确性，这一过程对数据质量与透明档案管理提出了严苛要求。

常用场景

经典使用场景

该数据集收录了2000年至2024年间欧洲27个国家的黄金年龄段失业率数据，并按照性别、家庭类型以及城乡地域进行了精细分层。其最具代表性的应用场景在于构建面板数据回归模型，用以探究宏观经济政策、劳动力市场结构变迁对不同人群失业率的异质性影响。研究者可借助该数据集分析金融危机、经济周期或制度变革如何作用于特定性别或家庭类型群体的就业状态，从而揭示劳动力市场中隐藏的结构性矛盾。

解决学术问题

该数据集为解决劳动经济学中关于失业率结构性差异的经典学术问题提供了关键支撑。例如，它使得量化分析性别与家庭类型交互作用下失业率的动态演化成为可能，突破了传统宏观失业率研究仅关注总量的局限。通过对27国长期面板数据的挖掘，学者得以检验不同福利体制与劳动力市场监管强度对弱势群体就业韧性的调节效用，深化了对欧洲劳动力市场分割与不平等再生产机制的理解。

衍生相关工作

依托该数据集，已衍生出一系列聚焦欧洲劳动力市场的经典研究工作。其中，基于分性别与时序的失业率序列，学者构建了多层级混合效应模型，量化了家庭类型与空间区位对就业机会的联合约束。另有一些工作利用该数据开展空间计量分析，揭示欧洲各国失业率的空间溢出效应与俱乐部收敛特征。此外，该数据还为探讨自动化与全球化冲击下不同人口群体的就业脆弱性提供了基准实证支撑。