electricsheepeurope/europe-ilo-luu-xlu4-sex-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: - 1K<n<10K tags: - tabular - europe - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Composite rate of labour underutilization (LU4) by sex and marital status (%) | Europe (ILOSTAT)" --- # Composite rate of labour underutilization (LU4) by sex and marital status (%) | Europe (ILOSTAT) 🇪🇺 **9,931 observations** · **37 Europe countries** · **1991–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **9,931 observations** of `Other measures of labour underutilization` data across **37 Europe countries**, spanning **1991–2025**, 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_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_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 37 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 810 | 1991 | 2025 | | `GBR` | 618 | 1999 | 2025 | | `AUT` | 582 | 1998 | 2025 | | `CZE` | 551 | 2002 | 2024 | | `FRA` | 528 | 2005 | 2024 | | `MDA` | 525 | 2006 | 2025 | | `BIH` | 444 | 2006 | 2020 | | `ESP` | 333 | 1999 | 2025 | | `MKD` | 324 | 2014 | 2025 | | `ALB` | 318 | 2011 | 2024 | | `SRB` | 312 | 2008 | 2020 | | `POL` | 252 | 2003 | 2020 | | `BLR` | 240 | 2017 | 2024 | | `ROU` | 231 | 1999 | 2020 | | `FIN` | 223 | 1999 | 2020 | | ... | _22 more countries_ | | | ## Indicators (sample) - `LUU_XLU4_SEX_MTS_RT` — Composite rate of labour underutilization (LU4) by sex 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 | `LUU_XLU4_SEX_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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `13.165` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-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_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="LUU_XLU4_SEX_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "LUU_XLU4_SEX_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_luu_xlu4_sex_mts_rt_composite_rate_of_labour_underutilization_lu4_by_s_2025, title = {Composite rate of labour underutilization (LU4) by sex and marital status (%) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU4_SEX_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-luu-xlu4-sex-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_MTS_RT_This dataset contains composite rates of labour underutilization (LU4) by sex and marital status (%) for 37 European countries from 1991 to 2025. It includes 9,931 observations covering one key indicator (LUU_XLU4_SEX_MTS_RT), which measures the overall level of labour underutilization in the labour market, encompassing unemployment, underemployment, and potential labour force. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via official API and normalized, with fields such as country codes, year, observed values, data sources, sex disaggregation (total, male, female), and marital status classification. The dataset is suitable for machine learning tasks like tabular classification, regression, and time-series forecasting, supporting analysis and modeling of European labour market trends.

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，通过REST API从官方端点直接采集原始指标数据，并依据国际劳工统计学家会议（ICLS）的定义对调查微观数据进行协调统一。随后，数据按欧洲ISO3国家编码进行地理过滤，仅保留37个欧洲国家的观测记录，最终整合为9,931条时间序列观测值，覆盖1991年至2025年的年度数据。每一行数据均通过source.label列追溯原始调查来源，确保数据可溯源与可复现。

特点

数据集聚焦于劳动力利用不足综合率（LU4）这一核心指标，按性别与婚姻状况进行分层统计，包含总人数、男性与女性三组性别分类。数据涵盖37个欧洲国家，时间跨度长达35年，每条记录均附有观测状态标记及方法修订等注释信息，便于用户识别数据质量差异。数据集采用国际标准化编码与标签，结构清晰，适用于面板数据分析、时间序列预测及分类回归等多种任务场景。

使用方法

用户可通过HuggingFace Datasets库的load_dataset()函数一键加载数据集，并转换为Pandas DataFrame进行后续分析。支持按国家编码筛选特定国家的时间序列，也可按指标代码提取单一指标的观测值并绘制趋势图。此外，可通过数据透视功能将数据重塑为国家×年份的矩阵形式，便于进行跨国的面板数据比较与计量模型构建，极大提升了研究的便捷性与效率。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年创建，经Electric Sheep Europe重新打包后发布，聚焦欧洲37个国家1991至2025年间劳动力利用不足的综合率（LU4），按性别和婚姻状况进行细致分层。作为ILOSTAT数据库的核心组成部分，该数据集旨在揭示欧洲劳动力市场中被传统失业率所掩盖的隐性冗余——那些因心灰意冷停止求职或被迫从事非充分就业的群体。ILO依托国际劳工统计学家会议（ICLS）的标准化定义，通过各国劳动力调查及行政记录等多元渠道进行数据整合与协调，为评估欧洲劳动力市场的结构性弱点提供了跨越三十余年的宝贵纵向视角，有力推动了劳动经济学与社会政策领域的实证研究。

当前挑战

该数据集面临的核心挑战在于如何精准捕捉并量化多维度的劳动力利用不足现象。LU4指标本身就比标准失业率复杂，需同时考虑就业不足者、潜在劳动力以及与劳动力市场松散联系者，其综合性对数据采集与统计划一性提出了极高要求。构建过程中，ILO需应对跨37个国家在抽样方法、调查频率及问卷设计上的巨大异质性，尤其当同一国家同一年份存在多个数据源时，必须通过复杂算法遴选出“最佳来源”。此外，数据因方法修订导致的时间序列断裂、观测值可靠性标记参差不齐，以及部分指标仅有年度数据而无更细粒度信息，均对分析模型的时间一致性与预测精度构成了显著障碍。

常用场景

经典使用场景

在劳动经济学与社会科学研究领域，该数据集为分析欧洲各国劳动力利用不足的复合率（LU4）提供了标准化的时序面板数据。研究者可借助其按性别和婚姻状况分类的观察值，构建固定效应模型或随机效应模型，系统考察经济周期、政策变革与社会结构变迁对劳动力市场吸纳能力的动态影响。数据覆盖1991至2025年间37个欧洲国家，时间跨度长且地域范围广，特别适用于开展跨国比较研究、趋势分析以及劳动参与率与失业率之外更广义的劳动力资源错配度量研究。

解决学术问题

该数据集有效突破了传统失业率指标在刻画劳动力市场健康度时的局限性。LU4指标综合反映了失业、时间关联就业不足、潜在劳动力供给等维度，为解决如何全面测度劳动力剩余这一长期困扰劳动经济学的核心难题提供了实证基础。通过对性别与婚姻状况分层的细致刻画，研究者得以深入探讨不同社会群体在就业质量与劳动力利用效率上的差异，进而为揭示结构性失业、隐性失业以及劳动力市场的性别不平等提供关键数据支撑。

衍生相关工作

围绕该数据集已衍生出一系列具有影响力的学术与实践工作。在方法论层面，研究者基于其年度观测值开发了多种劳动力利用不足的预测模型，如结合宏观经济变量的自回归移动平均模型（ARIMA）以及考虑空间依赖性的面板贝叶斯模型。在实证研究中，有经典工作利用该数据验证了经济危机后欧洲各国劳动力市场复苏路径的异质性，并量化分析了灵活就业政策对LU4指标长短期效应的影响。此外，数据集的标准化格式使其成为机器学习和时间序列预测领域的基准测试资源。