electricsheepeurope/europe-ilo-luu-xlu4-sex-geo-mts-rt-composite-rate-of-labour-underutilization-lu4-by-s

--- 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: "Composite rate of labour underutilization (LU4) by sex, rural / urban area and marital sta | Europe (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex, rural / urban area and marital sta | Europe (ILOSTAT) 🇪🇺 **15,534 observations** · **33 Europe countries** · **1998–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **15,534 observations** of `Other measures of labour underutilization` data across **33 Europe countries**, spanning **1998–2025**, 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=LUU_XLU4_SEX_GEO_MTS_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=LUU_XLU4_SEX_GEO_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 33 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `AUT` | 756 | 1998 | 2025 | | `FIN` | 676 | 1999 | 2020 | | `GBR` | 627 | 1999 | 2019 | | `PRT` | 621 | 1998 | 2020 | | `SWE` | 607 | 2000 | 2020 | | `LUX` | 600 | 1999 | 2020 | | `BEL` | 594 | 1999 | 2020 | | `FRA` | 587 | 2005 | 2024 | | `ESP` | 567 | 1999 | 2020 | | `NLD` | 567 | 2000 | 2020 | | `DNK` | 567 | 2000 | 2020 | | `EST` | 558 | 1998 | 2020 | | `LVA` | 540 | 2001 | 2020 | | `LTU` | 540 | 2001 | 2020 | | `MDA` | 540 | 2006 | 2025 | | ... | _18 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_GEO_MTS_RT` — Composite rate of labour underutilization (LU4) by sex, rural / urban area and marital status (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AUT` | | `ref_area.label` | `string` | Country name in English | `Austria` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:103` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `LUU_XLU4_SEX_GEO_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Composite rate of labour underutiliza…` | | `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.) | `GEO_COV_NAT` | | `classif1.label` | `string` | — | `Area type: National` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2025` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `10.939` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-luu-xlu4-sex-geo-mts-rt-composite-rate-of-labour-underutilization-lu4-by-s") 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"] == "LUU_XLU4_SEX_GEO_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_GEO_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_GEO_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu4_sex_geo_mts_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex, rural / urban area and marital sta | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_GEO_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu4-sex-geo-mts-rt-composite-rate-of-labour-underutilization-lu4-by-s}} } ``` ## 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=LUU_XLU4_SEX_GEO_MTS_RT_This dataset is a tabular dataset on the composite rate of labour underutilization (LU4) for 33 European countries from 1998 to 2025, containing 15,534 observations. The data is sourced from the International Labour Organization (ILO)s ILOSTAT database, retrieved via its REST API and filtered to European countries. The core indicator is LUU_XLU4_SEX_GEO_MTS_RT, which represents the composite rate of labour underutilization by sex, rural/urban area, and marital status (%). The dataset provides multi-dimensional disaggregation variables, including sex (total, male, female), area type (e.g., national), and marital status (total). It also includes fields such as country codes, year, observed value, data source, observation status, and related notes. The data is published at an annual frequency and has been harmonized by the ILO.

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统计数据库，聚焦于欧洲地区劳动力利用不足的综合率（LU4）。数据通过ILOSTAT的REST API直接获取，原始指标代码为LUU_XLU4_SEX_GEO_MTS_RT，并依据欧洲国家的ISO3代码进行地理过滤。ILOSTAT依据国际劳工统计学家会议（ICLS）定义，对各国劳动力调查的微观数据进行标准化处理，确保跨国家与跨年份的可比性。本数据集由Electric Sheep Europe团队重新整合，以Parquet格式封装，提供了统一的Schema和可靠的元数据标记，便于直接使用。

特点

本数据集涵盖1998年至2025年间33个欧洲国家的15,534条观测记录，包含一个核心指标即劳动力利用不足综合率（LU4）。数据按性别（男性、女性、总计）、城乡地域以及婚姻状况三个维度进行交叉分类，形成了精细的结构化面板数据。每条记录均包含详细的来源标签、观测状态标志及注释信息，便于用户追溯数据质量和统计方法的变化。数据集覆盖了奥地利、芬兰、英国等多个国家，每个国家的时间序列长度不一，为纵向比较和时间序列分析提供了丰富的基础。

使用方法

用户可通过HuggingFace的`datasets`库便捷加载数据，执行`load_dataset("electricsheepeurope/europe-ilo-luu-xlu4-sex-geo-mts-rt-composite-rate-of-labour-underutilization-lu4-by-s")`即可获得可直接使用的DataFrame。利用`ref_area`字段可筛选特定国家，如德国（DEU）。借助`indicator`列定位到LU4指标后，可对`obs_value`按时间排序进行趋势绘图。进一步，用户可将数据透视为国家×年份的矩阵格式，便于进行跨国的面板数据分析或作为机器学习模型的输入特征，尤其适用于序列预测与分类任务。

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2025年整理发布，由Electric Sheep Europe团队在HuggingFace平台上重新封装，聚焦欧洲地区劳动力利用不足的综合率（LU4）。作为ILOSTAT数据库的核心指标之一，LU4通过整合就业、失业及潜在劳动力等多维度信息，为衡量劳动力市场闲置程度提供了超越传统失业率的综合视角。数据集覆盖33个欧洲国家，时间跨度从1998年至2025年，包含15,534条观测记录，并依据性别、城乡区域及婚姻状况进行细致分层。其发布显著推动了劳动经济学领域对劳动力利用不足问题的跨国比较研究，为政策制定者与学术界提供了标准化、可复现的实证基础。

当前挑战

该数据集所解决的领域问题在于传统失业率指标难以全面反映劳动力市场的闲置现象，例如隐性失业、就业不足及潜在劳动力退出市场等情况。LU4通过综合多维度指标，弥补了这一结构性缺陷，实现了对劳动力利用不足更精准的量化。然而，构建过程面临多重挑战：首先，数据源自33国不同调查体系（如劳动力调查），需经由ILO基于国际劳工统计学家会议（ICLS）定义进行复杂协调与标准化，以消除各国统计口径差异；其次，数据包含年度频率且部分指标存在来源更替，需处理序列中断与方法论修订带来的非一致性；最后，分层变量如城乡分类与婚姻状况在各国定义未尽相同，需在保持粒度与可比性之间取得平衡，确保跨国分析的稳健性。

常用场景

经典使用场景

在劳动经济学与政策评估领域，欧洲劳动力利用不足综合率（LU4）数据集为学者提供了横跨33个欧洲国家、覆盖近三十年（1998—2025年）的精细化面板数据。该数据集通过性别、城乡区域及婚姻状况等多维度分层，展现劳动力市场边缘化群体的结构性特征，常被用于构建时间序列预测模型与回归分析，以追踪劳动力利用不足率的动态演变。研究人员可借助该数据探究经济周期、制度变革或移民浪潮对劳动力吸纳能力的影响，其完整的时间跨度和标准化的ILO统计口径确保了跨国产出比较的可靠性与复现性。

实际应用

在实际政策制定中，该数据集为国际劳工组织、欧盟统计局及各成员国劳动部门提供了数据驱动的决策支撑。通过实时监测各国劳动力利用不足率及其细分维度的变化，决策者能够精准识别就业弱势区域与人群，从而针对性地设计职业培训、创业扶持或社会保障扩容方案。例如，结合性别与城乡交叉分析的结果可指导女性就业促进计划的资源分配，而时间序列模型则能预警经济衰退期劳动力闲置的急剧攀升，助力危机响应政策的提前部署与效果评估。

衍生相关工作

基于此数据集，学界已衍生出多项具有深度的经典研究工作。一方面，研究者利用其高维分类特性构建了劳动力市场分割的计量经济学模型，探究婚姻状况对女性劳动力退出的调节效应；另一方面，该数据被整合进多源面板以进行欧洲就业政策的准自然实验评估，如最低工资调整对LU4的动态冲击。此外，机器学习领域的工作者将其转化为监督学习任务，通过历史序列训练预测模型来模拟周期性失业行为的区域异质性，从而在宏观经济预警体系中发挥了锚定作用。