electricsheepeurope/europe-ilo-ged-xlu3-sex-hht-geo-rt-prime-age-combined-rate-of-unemployment-and-potent

--- 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 - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Prime-age combined rate of unemployment and potential labour force (LU3) by sex, household | Europe (ILOSTAT)" --- # Prime-age combined rate of unemployment and potential labour force (LU3) by sex, household | Europe (ILOSTAT) 🇪🇺 **22,532 observations** · **27 Europe countries** · **2000–2024** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **22,532 observations** of `Other measures of labour underutilization` 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_XLU3_SEX_HHT_GEO_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Other measures of labour underutilization ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=GED_XLU3_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` | 1,295 | 2000 | 2023 | | `AUT` | 1,294 | 2000 | 2023 | | `BEL` | 1,286 | 2000 | 2023 | | `HUN` | 1,242 | 2001 | 2023 | | `LVA` | 1,233 | 2001 | 2023 | | `EST` | 1,199 | 2000 | 2023 | | `LTU` | 1,184 | 2002 | 2023 | | `GRC` | 1,103 | 2001 | 2024 | | `NLD` | 1,092 | 2000 | 2020 | | `GBR` | 1,076 | 2000 | 2019 | | `HRV` | 1,057 | 2002 | 2023 | | `POL` | 1,025 | 2006 | 2024 | | `SVN` | 1,015 | 2005 | 2023 | | `IRL` | 970 | 2006 | 2023 | | ... | _12 more countries_ | | | ## Indicators (sample) - `GED_XLU3_SEX_HHT_GEO_RT` — Prime-age combined rate of unemployment and potential labour force (LU3) 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_XLU3_SEX_HHT_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Prime-age combined rate of unemployme…` | | `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) | `26.631` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-xlu3-sex-hht-geo-rt-prime-age-combined-rate-of-unemployment-and-potent") 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_XLU3_SEX_HHT_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="GED_XLU3_SEX_HHT_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "GED_XLU3_SEX_HHT_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_ged_xlu3_sex_hht_geo_rt_prime_age_combined_rate_of_unemployment_and_potent_2024, title = {Prime-age combined rate of unemployment and potential labour force (LU3) by sex, household | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=GED_XLU3_SEX_HHT_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-ged-xlu3-sex-hht-geo-rt-prime-age-combined-rate-of-unemployment-and-potent}} } ``` ## 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_XLU3_SEX_HHT_GEO_RT_This dataset is a tabular dataset focusing on labor market conditions in European countries, specifically under the category Other measures of labour underutilization. It contains 22,532 observations across 27 European countries, spanning from 2000 to 2024. The key indicator is Prime-age combined rate of unemployment and potential labour force (LU3) by sex, household type and rural / urban areas (%), which measures the combined rate of unemployment and potential labor force for prime-age individuals, disaggregated by sex, household type, and rural/urban areas. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via its REST API and filtered to European ISO3 country codes. The dataset includes a detailed schema with columns such as country code, country name, source code, indicator code, sex disaggregation, household type classification, area type classification, observation year, observed value, and observation status. It is suitable for tasks like tabular classification, regression, and time-series forecasting. The data is published at an annual frequency and comes with caveats on data quality, such as the use of ILO-selected best sources and handling of missing values in disaggregation columns.

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数据库，聚焦于欧洲27国2000至2024年间“其他劳动力利用不足衡量指标”中的特定维度。通过调用ILOSTAT REST API，原始数据基于国际劳工统计学家会议（ICLS）定义，从各国劳动力调查、住户收支调查等来源中提取并归一化。Electric Sheep Europe团队将数据过滤至欧洲ISO3国家代码，并经过清洗与结构化处理，最终形成包含22,532条观测值的表格化数据集。

特点

数据集以单一核心指标“壮年失业与潜在劳动力联合比率（LU3）”为主导，按性别、住户类型及城乡地域进行细致划分，并附有完备的元数据标注。其覆盖27个欧洲国家、时间跨度达25年，数据来源标识清晰，便于追溯原始调查。此外，数据集提供了观测状态标记（如“不可靠”）以辅助质量评估，适合用于跨国面板分析、时间序列建模及劳动力市场结构性变迁研究。

使用方法

用户可通过HuggingFace的`datasets`库直接加载数据集，利用`load_dataset()`方法获取后，自如转换为pandas DataFrame进行探索。典型操作包括按国家代码筛选单国子集、针对特定指标排序后绘制时序变化曲线，或运用pivot_table将数据重塑为国家×年份的矩阵形式，以适配回归分析、分类任务或时间序列预测等建模需求。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2024年发布，经Electric Sheep Europe团队重新打包至HuggingFace平台，聚焦欧洲27国2000至2024年间适龄劳动人口失业与潜在劳动力联合比率（LU3），并按性别、家庭类型及城乡地域进行精细分层。作为ILOSTAT数据库的核心子集，该指标突破了传统失业率的单一视角，捕捉了失业群体与潜在劳动力中未充分就业人群的复合状态，为理解欧洲劳动力市场结构性失衡提供了量化工具。数据集涵盖22,532条观测，整合了多国劳动力调查与行政记录，其标准化架构推动了跨国比较研究，在劳动经济学、社会政策评估及机器学习预测模型中具有显著影响力。

当前挑战

数据集所解决的领域问题在于，传统失业率指标难以反映隐性失业与劳动力闲置的全貌，LU3通过纳入潜在劳动力群体补充了劳动力利用不足的测量维度，尤其适用于分析经济周期中就业弹性变化与结构性失业特征。构建过程中面临多重挑战：跨国家庭调查的指标定义与统计口径存在差异，需经ICLS国际标准进行协调统一；数据来源多样化导致观测值存在可靠性标注（如标记为不可靠的观测），需审慎处理异常值；时间序列非平衡覆盖问题突出（如部分国家数据起始年份不同），以及性别、家庭类型等分类维度的缺失值处理，均对建模与推断提出严苛要求。

常用场景

经典使用场景

在欧洲劳动经济学与社会政策研究的广袤领域中，该数据集扮演着不可或缺的基石角色。其最经典的运用当属针对欧盟成员国劳动市场弱势群体——特别是壮年劳动力（prime-age workers）——的就业困境进行跨国比较分析。研究者可依据性别、家庭类型以及城乡地域等维度进行精细化的数据切片，深入剖析不同社会群体在劳动参与中面临的潜在障碍。通过整合长达二十余载的时间序列数据，学者们得以系统性地追踪LU3指标（失业与潜在劳动力综合比率）的长期演变趋势，为理解欧洲劳动市场的结构性变化提供了实证根基。

衍生相关工作

依托该数据集的独特结构与丰富维度，衍生出了一系列颇具影响力的经典学术工作。在计量经济学领域，学者构建了多层级面板数据模型，用以探索失业与潜在劳动力之间在不同国家与家庭背景下的动态传导机制。机器学习方面，研究者利用该数据集的时间序列特征开发了LU3指标的预测模型，结合人口结构与经济周期变量，实现了对各国未来劳动市场松弛程度的前瞻性评估。此外，在社会分层研究中，该数据被用于探讨家庭类型如何放大或缓冲劳动市场的制度性不平等，揭示了性别、城乡与家庭责任交织下劳动力供给弹性的复杂图景。

数据集最近研究

最新研究方向

当前，围绕欧洲劳动力市场的结构性变革，该数据集聚焦于prime-age群体中失业与潜在劳动力（LU3）的综合比率，已成为探究后疫情时代劳动力闲置程度与家庭结构、城乡分布交织关系的前沿工具。借助ILOSTAT经过国际劳工统计学家会议标准协调的微观数据，研究者得以突破传统失业率的单一维度，从性别、家庭类型与地域异质性视角，精准刻画隐性失业与边缘劳动力动员的时空演化。这一方向不仅为欧洲各国设计包容性就业政策提供了量化基底，更与全球关于充分就业、体面工作的可持续发展目标监测紧密相连，推动劳动经济研究从宏观总量向多维精细测度迈进。