electricsheepeurope/europe-ilo-ged-xlu1-sex-hht-chl-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 presence of children (%) | Europe (ILOSTAT)" --- # Prime-age unemployment rate by sex, household type and presence of children (%) | Europe (ILOSTAT) 🇪🇺 **55,239 observations** · **30 Europe countries** · **2000–2024** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **55,239 observations** of `Unemployment` data across **30 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_CHL_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_CHL_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 30 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `ESP` | 2,762 | 2000 | 2023 | | `BEL` | 2,759 | 2000 | 2023 | | `HUN` | 2,731 | 2000 | 2023 | | `DEU` | 2,720 | 2000 | 2023 | | `AUT` | 2,717 | 2000 | 2023 | | `ROU` | 2,654 | 2000 | 2023 | | `SVN` | 2,604 | 2000 | 2023 | | `EST` | 2,505 | 2000 | 2023 | | `LVA` | 2,477 | 2001 | 2023 | | `BGR` | 2,461 | 2001 | 2023 | | `LTU` | 2,423 | 2002 | 2023 | | `GBR` | 2,311 | 2000 | 2019 | | `HRV` | 2,310 | 2002 | 2023 | | `GRC` | 2,289 | 2001 | 2024 | | `NLD` | 2,286 | 2000 | 2020 | | ... | _15 more countries_ | | | ## Indicators (sample) - `GED_XLU1_SEX_HHT_CHL_RT` — Prime-age unemployment rate by sex, household type and presence of children (%) ## 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 | `GED_XLU1_SEX_HHT_CHL_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 | `CHL_AGET6_TOTAL` | | `classif2.label` | `string` | — | `Presence of children under age 6: Total` | | `time` | `int64` | Observation year | `2023` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `9.395` | | `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-chl-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_CHL_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="GED_XLU1_SEX_HHT_CHL_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "GED_XLU1_SEX_HHT_CHL_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ged_xlu1_sex_hht_chl_rt_prime_age_unemployment_rate_by_sex_household_type_2024, title = {Prime-age unemployment rate by sex, household type and presence of children (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=GED_XLU1_SEX_HHT_CHL_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ged-xlu1-sex-hht-chl-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_CHL_RT_This dataset contains 55,239 observations across 30 European countries from 2000 to 2024, with the core indicator being Prime-age unemployment rate by sex, household type and presence of children (%). The data is sourced from the International Labour Organizations ILOSTAT database, retrieved via API and filtered to European country codes. It covers unemployment statistics disaggregated by dimensions such as sex (total, male, female), household type, and presence of children, 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）的ILOSTAT核心统计数据库，通过其REST API直接抽取指标代码为GED_XLU1_SEX_HHT_CHL_RT的原始数据，并依据国际劳工统计学家会议（ICLS）定义进行协调与标准化。随后，数据集限定为欧洲30个国家的观测值，利用ILO选定的‘最佳来源’对同一国家与年份的多源数据进行整合，最终经由Electric Sheep Europe的流水线处理，整理为结构化的表格形式，共包含55,239条年度观测记录。

特点

数据集覆盖2000年至2024年间30个欧洲国家的劳动力市场信息，核心指标为按性别、家庭类型及子女状况划分的壮年失业率。其独特之处在于提供了多维度的分类变量，包括性别（总、男、女）、家庭类型及子女年龄组，并附带详细的来源标签与数据质量标记，如观测状态和注释说明。数据以年度频率呈现，支持精细的子群体分析，且来源具有可追溯性，为研究者提供了高粒度的欧洲劳动力结构洞察。

使用方法

研究人员可通过HuggingFace的`datasets`库轻松加载，使用`load_dataset()`函数快速获取数据并转换为Pandas DataFrame。通过按国家代码（如`ref_area == 'DEU'`）筛选，可针对特定国家进行分析；按指标列排序则可构建单一指标的时间序列并可视化。此外，利用数据透视表功能，可将数据重塑为国家×年份的矩阵，便于进行跨国的面板数据分析或回归建模，契合时间序列预测与分类任务的需求。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计司于2024年发布，经Electric Sheep Europe重新整理后托管于HuggingFace平台，聚焦欧洲30国2000至2024年间按性别、家庭类型及儿童存在状况分层的壮年失业率数据。作为全球劳动统计的权威来源，ILOSTAT数据库通过整合各国劳动力调查、家庭收支调查等微观数据，并依据国际劳工统计学家会议定义进行标准化处理，为劳动力市场分析提供了坚实的数据基础。该数据集涵盖55,239条观测记录，在时间跨度与地理覆盖上具有显著优势，对于理解欧洲劳动力市场中家庭结构与性别差异对失业影响的动态演变具有重要价值。其发布不仅丰富了劳动经济学与公共政策研究的数据资源，也为跨国比较与面板数据分析提供了统一、可复用的基准，尤其在探讨失业率异质性、家庭决策与劳动力供给等议题上具有广泛的应用前景。

当前挑战

该数据集所应对的核心领域挑战在于如何精准刻画家庭结构对失业率的复杂影响机制，传统宏观指标常忽略性别、儿童照料责任等微观层面的交互效应，而本数据通过精细的分层设计（性别×家庭类型×儿童年龄）为揭示劳动力市场中家庭约束与性别不平等提供了切入点。在构建过程中，面临的挑战主要源于多源数据的整合与标准化：各国劳动力调查的抽样框架、问卷设计及时间跨度参差不齐，ILO虽通过选择‘最佳来源’优先保证数据一致性，但部分观测值因来源差异导致标记为‘不可靠’，加上年度频次限制与缺失的月度波动信息，可能影响时间序列的颗粒度与因果推断的精度。此外，跨国同家庭类型定义的非完全对齐与长时段内政策变化引发的结构性断点，也增加了长期分析的复杂性。

常用场景

经典使用场景

该数据集汇聚了2000年至2024年间30个欧洲国家按性别、家庭类型及有无子女分类的核心年龄失业率观测数据，共计55,239条记录。作为国际劳工组织ILOSTAT数据库的欧洲子集，经典使用场景聚焦于劳动力市场的多维结构分析——研究人员可借助该数据集中的性别、家庭构成与子女照护负担等维度，揭示不同人口群体在失业风险上的异质性。数据的时间序列属性使其特别适用于面板数据建模与纵向比较研究，为探讨经济周期、政策干预与就业脆弱性之间的动态关系提供了可靠的数据支撑。

衍生相关工作

基于该数据集衍生出的经典工作主要集中于三大方向：其一，利用固定效应模型或随机前沿方法估计家庭特征对失业持续期的影响，揭示子女数量与年龄如何调节性别失业差异；其二，构建多水平随机截距模型分解国家层面制度因素与家庭层面微观因素的交互效应；其三，该数据已被用于训练时间序列预测算法以模拟不同人口条件下的失业趋势，如基于LSTM的欧洲核心年龄失业率预测框架。这些工作不仅验证了数据集中分类维度的解释效力，也为后续交叉比较研究提供了方法论的范例与基准。

数据集最近研究

最新研究方向

该数据集聚焦于欧洲30国2000至2024年间分性别、家庭类型及未成年子女状况的劳动年龄人口失业率，为劳动力市场不平等与家庭结构变迁的交叉研究提供了高粒度的时间序列支撑。当前学界正借助此类数据深入探究后疫情时代欧洲就业复苏的结构性差异，特别是女性因照护责任导致的失业风险分化，以及单亲家庭在宏观经济波动中的脆弱性。结合ILOSTAT权威统计与标准化分类，该资源有力推动了劳动经济学与家庭社会学交叉领域的定量建模，为设计精准的社会保护政策与评估欧盟《欧洲就业战略》成效奠定了数据基础。