electricsheepeurope/europe-ilo-eip-teip-sex-edu-mts-nb-persons-outside-the-labour-force-by-sex-education

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - europe - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Persons outside the labour force by sex, education and marital status (thousands) | Europe (ILOSTAT)" --- # Persons outside the labour force by sex, education and marital status (thousands) | Europe (ILOSTAT) 🇪🇺 **156,754 observations** · **39 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **156,754 observations** of `Other measures of labour underutilization` data across **39 Europe countries**, spanning **1987–2025**, 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=EIP_TEIP_SEX_EDU_MTS_NB) - **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=EIP_TEIP_SEX_EDU_MTS_NB` 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 39 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CHE` | 12,727 | 1991 | 2025 | | `CZE` | 9,686 | 1998 | 2024 | | `GBR` | 9,420 | 1999 | 2025 | | `FRA` | 8,175 | 2005 | 2024 | | `MKD` | 7,962 | 2005 | 2025 | | `MDA` | 7,384 | 2000 | 2025 | | `AUT` | 6,765 | 1998 | 2025 | | `ALB` | 6,175 | 2002 | 2024 | | `BIH` | 5,027 | 2001 | 2020 | | `SRB` | 5,023 | 2007 | 2020 | | `RUS` | 4,724 | 2010 | 2025 | | `POL` | 4,428 | 1998 | 2025 | | `GRC` | 4,415 | 1987 | 2020 | | `IRL` | 3,966 | 1999 | 2023 | | `ESP` | 3,495 | 1998 | 2025 | | ... | _24 more countries_ | | | ## Indicators (sample) - `EIP_TEIP_SEX_EDU_MTS_NB` — Persons outside the labour force by sex, education and marital status (thousands) ## 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 | `EIP_TEIP_SEX_EDU_MTS_NB` | | `indicator.label` | `string` | Indicator name in English | `Persons outside the labour force by s…` | | `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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): Total` | | `time` | `int64` | Observation year | `2024` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `738.429` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:996` | | `note_classif.label` | `string` | — | `Nonstandard education level: Including 2` | | `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-eip-teip-sex-edu-mts-nb-persons-outside-the-labour-force-by-sex-education") 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"] == "EIP_TEIP_SEX_EDU_MTS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_TEIP_SEX_EDU_MTS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_TEIP_SEX_EDU_MTS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_eip_teip_sex_edu_mts_nb_persons_outside_the_labour_force_by_sex_education_2025, title = {Persons outside the labour force by sex, education and marital status (thousands) | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_TEIP_SEX_EDU_MTS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-eip-teip-sex-edu-mts-nb-persons-outside-the-labour-force-by-sex-education}} } ``` ## 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=EIP_TEIP_SEX_EDU_MTS_NB_This dataset contains statistical information on Persons outside the labour force by sex, education and marital status (thousands) for European countries, sourced from the International Labour Organization (ILO) ILOSTAT database and repackaged by Electric Sheep Europe for machine learning readiness. It covers 39 European countries, spans the years 1987 to 2025, and includes 156,754 observations, focusing on a single indicator: EIP_TEIP_SEX_EDU_MTS_NB, which measures the number of persons outside the labour force, disaggregated by sex, education level, and marital status. The data is organized in tabular format with columns such as country code, indicator code, sex classification, education classification, marital status classification, year, and observed value, supporting tasks like tabular classification, regression, and time-series forecasting. The dataset is retrieved via the ILOSTAT API and normalized for consistency and traceability, making it suitable for labor market analysis, economic research, and social policy evaluation.

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，该数据库作为全球劳动统计的权威来源，整合了来自各国劳动力调查、家庭收入调查、企业调查及行政记录等多源数据。数据集通过ILOSTAT的REST API接口直接提取，并依据国际劳动统计学家会议（ICLS）的定义进行标准化处理。Electric Sheep Europe团队对原始数据进行了针对性过滤，仅保留39个欧洲国家的观测值，经清洗与重组后形成156,754条记录，覆盖1987年至2025年的年度数据，确保了数据的跨区域一致性与时间序列的连贯性。

特点

该数据集以‘劳动力市场之外的群体’为核心观测对象，按性别、教育程度和婚姻状况三个维度进行精细分层，提供了总计1个核心指标（EIP_TEIP_SEX_EDU_MTS_NB），以千人为计量单位。数据包含了丰富的分类变量、来源标识及注释字段，如观测状态标记（provisional、unreliable）和方法论变更记录，便于使用者评估数据质量。地理覆盖广泛，涵盖从阿尔巴尼亚到俄罗斯的39个欧洲经济体，部分国家的时间跨度长达近四十年，为长期劳动力结构变迁分析提供了坚实的数据基础。

使用方法

研究人员可通过HuggingFace的datasets库便捷加载数据，使用load_dataset函数一键获取训练集，并直接转换为Pandas DataFrame进行后续分析。用户可针对特定国家（如德国）按‘ref_area’字段进行筛选，或基于‘time’列构建单指标的时间序列进行可视化。此外，数据支持通过pivot_table方法重塑为国家×年份的矩阵形式，方便进行面板数据分析或跨国家比较。值得注意的是，数据已按年频整理，且在多源冲突时采用ILO选定的‘最佳来源’，使用者应留意分类字段及注释列中的潜在断点与非标准编码。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年通过其核心统计数据库ILOSTAT创建，并由Electric Sheep Europe团队重新打包发布，收录了1987年至2025年间39个欧洲国家共156,754条观测记录，聚焦于劳动力市场边缘群体——即因性别、教育及婚姻状况而异的不在劳动力队伍人群（单位：千人）。ILOSTAT作为全球劳动统计的权威来源，依托各国劳动力调查等官方数据，依据国际劳动统计学家会议（ICLS）标准进行整合与标准化，为理解欧洲劳动力市场的结构性特征与非充分就业形态提供了关键数据支撑。该数据集在劳动经济学、社会政策及时间序列分析等领域具有重要影响力，尤其有助于探究教育水平与婚姻状况如何交互影响劳动参与率，揭示被传统失业率指标所忽视的劳动力闲置现象。

当前挑战

该数据集所揭示的核心领域挑战在于如何精准量化劳动力市场中的隐性闲置问题。传统失业率仅反映积极求职但未就业者，而大量因家庭责任、健康或丧失信心等原因退出劳动力市场的群体（尤其是低学历或特定婚姻状况的女性）长期被数据体系遮蔽，难以被政策制定者有效识别与干预。在构建过程中，数据集面临多重技术挑战：首先，需协调39个国家在近40年间采用的不同调查问卷、时间频率（年度为主，部分月/季度数据缺失）及分类标准（如教育层级非标准化标注）以实现跨时空可比性；其次，ILOSTAT虽整合了‘最佳来源’，但同一指标可能存在来源变更引发的序列断裂（如`note_indicator.label: Break in series`），需通过标志字段追溯与处理方法论断层；此外，性别、教育、婚姻状况等多维度交叉细分导致数据稀疏性，部分小国或早期年份的细分组合值可能存在可靠性标注（如`obs_status: Unreliable`），对统计建模的鲁棒性构成考验。

常用场景

经典使用场景

该数据集汇聚了国际劳工组织ILOSTAT数据库中关于欧洲39个国家1987至2025年间劳动年龄人口中非劳动力人群的精细统计，涵盖性别、教育程度与婚姻状况三个关键维度。经典使用场景集中在通过时间序列与面板数据模型，揭示欧洲各国非劳动力规模的长期演变趋势及其结构性差异。研究者常利用该数据集构建多元回归模型，量化性别、教育水平与婚姻状况如何交互作用，影响个体退出劳动力市场的概率，从而勾勒出欧洲劳动力参与率动态变化的宏观图景。

衍生相关工作

该数据集衍生的相关工作主要集中在利用其结构化特点构建跨国比较的劳动力未充分利用指数，并推动了融合经济计量与机器学习方法的预测框架发展。经典工作中，有学者基于该面板数据开发了似不相关回归模型，校正国家间不可观测异质性后，揭示了教育水平对非劳动力退出率的非线性门槛效应。此外，也有研究采用长短期记忆网络对各国非劳动力人数进行多步时间序列预测，并与传统ARIMA模型进行精度对比，为劳动经济学引入了时序深度学习的分析范式。这些工作不仅拓展了劳动力统计数据的分析边界，也为官方统计数据的自动化质量校验提供了方法论支撑。

数据集最近研究

最新研究方向

该数据集聚焦于欧洲劳动力市场边缘群体研究，尤其是因性别、教育背景与婚姻状况而退出劳动力市场的人群。当前前沿方向包括：利用时间序列建模与分类回归方法，解析经济周期、社会政策与结构性失业对非活跃人口的动态影响；结合ILOSTAT的标准化定义与多源微观数据，开展跨国比较研究，揭示教育水平与性别差异在劳动力退出决策中的交互作用，为欧盟包容性就业政策制定提供数据驱动依据。