electricsheepeurope/europe-ilo-eip-neet-sex-mts-rt-share-of-youth-not-in-employment-education-or-trai

--- 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: "Share of youth not in employment, education or training (NEET) by sex and marital status ( | Europe (ILOSTAT)" --- # Share of youth not in employment, education or training (NEET) by sex and marital status ( | Europe (ILOSTAT) 🇪🇺 **9,082 observations** · **38 Europe countries** · **1987–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **9,082 observations** of `Other measures of labour underutilization` data across **38 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_NEET_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=EIP_NEET_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 38 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MDA` | 584 | 2000 | 2025 | | `CHE` | 584 | 1991 | 2025 | | `POL` | 519 | 2000 | 2025 | | `FRA` | 456 | 2005 | 2024 | | `MKD` | 447 | 2006 | 2025 | | `CZE` | 445 | 2001 | 2023 | | `GBR` | 440 | 2004 | 2025 | | `ALB` | 400 | 2002 | 2024 | | `AUT` | 397 | 2005 | 2025 | | `BIH` | 367 | 2001 | 2020 | | `ESP` | 332 | 2000 | 2025 | | `GRC` | 306 | 1987 | 2020 | | `SRB` | 278 | 2007 | 2020 | | `PRT` | 261 | 1998 | 2020 | | `IRL` | 204 | 2004 | 2023 | | ... | _23 more countries_ | | | ## Indicators (sample) - `EIP_NEET_SEX_MTS_RT` — Share of youth not in employment, education or training (NEET) 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 | `EIP_NEET_SEX_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Share of youth not in employment, edu…` | | `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) | `21.029` | | `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-eip-neet-sex-mts-rt-share-of-youth-not-in-employment-education-or-trai") 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_NEET_SEX_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_NEET_SEX_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_NEET_SEX_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_eip_neet_sex_mts_rt_share_of_youth_not_in_employment_education_or_trai_2025, title = {Share of youth not in employment, education or training (NEET) by sex and marital status ( | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_NEET_SEX_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-eip-neet-sex-mts-rt-share-of-youth-not-in-employment-education-or-trai}} } ``` ## 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_NEET_SEX_MTS_RT_This dataset contains statistics on the share of youth not in employment, education or training (NEET) in Europe, disaggregated by sex and marital status, sourced from the International Labour Organization (ILO) ILOSTAT database. It covers 38 European countries from 1987 to 2025, with 9,082 observations. The core indicator is EIP_NEET_SEX_MTS_RT, representing the Share of youth not in employment, education or training (NEET) by sex and marital status (%). The data is presented in tabular format, including columns such as country code, country name, data source, indicator code, sex classification, marital status classification, observation year, observed value, and data status flags. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and derived from official sources like labour force surveys. It is suitable for machine learning tasks such as tabular classification, regression, and time-series forecasting. The dataset is repackaged by Electric Sheep Europe and released under the CC-BY-4.0 license.

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，通过调用其REST API，直接提取了指标代码为EIP_NEET_SEX_MTS_RT的原始数据。数据获取后，依据欧洲ISO3国家代码进行地理范围过滤，仅保留涵盖38个欧洲国家的观测记录。ILOSTAT本身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（ICLS）的定义进行标准化处理，确保了不同来源数据间的可比性。Electric Sheep Europe团队在此基础上对数据进行了重新打包和规范化，以Parquet格式发布，使其可直接通过HuggingFace Datasets库加载使用。

特点

数据集共包含9,082条观测记录，时间跨度从1987年至2025年，覆盖欧洲38个国家，聚焦于“青年未就业、未受教育或培训比例（NEET）”这一单一关键指标。数据按性别（男性、女性、总计）和婚姻状况进行细致分层，提供了丰富的维度以支持精细化分析。每条记录均附带了数据来源、观测状态标记（如临时性、不可靠）及系列中断等备注信息，便于用户评估数据质量。此外，数据采用年度频率发布，并优先选用ILO为每个国家-年份组合选定的‘最佳来源’，确保了数据的一致性与权威性。

使用方法

用户可通过HuggingFace Datasets库的一行代码轻松加载数据：load_dataset("electricsheepeurope/europe-ilo-eip-neet-sex-mts-rt-share-of-youth-not-in-employment-education-or-trai")，并将其转换为Pandas DataFrame进行后续分析。针对特定国家的分析，可通过过滤ref_area列实现，例如筛选德国数据。对于时间序列分析，可依据indicator列筛选指标后按time排序并进行可视化。此外，通过数据透视表可将数据重塑为国家×年份的矩阵形式，便于跨国的横向对比和面板数据分析。数据文件的Schema清晰定义了每一列的含义和类型，为统计建模、经济研究和政策评估提供了便利的数据接口。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年发布，经Electric Sheep Europe重新整理后托管于HuggingFace平台，聚焦欧洲38国1987至2025年间青年未就业、未受教育或培训（NEET）比例，按性别与婚姻状况进行细分。作为ILOSTAT数据库的核心指标之一，NEET比率是衡量青年劳动力市场边缘化程度的关键社会经济学变量，广泛用于评估教育系统效能、就业政策干预效果及社会包容性发展。该数据集通过标准化ILO统计方法整合各国劳动力调查微观数据，为跨国家、长时序比较研究提供了统一基准，显著推动了欧洲劳动经济学、青年发展政策及可持续发展目标（SDGs）相关实证分析的可重复性与可扩展性。

当前挑战

该数据集面临的首要挑战在于领域问题层面：NEET比率作为复合指标，需同时捕捉就业、教育与培训的排斥效应，而各国对“教育”与“培训”的定义差异及统计口径变迁易导致跨国比较偏误，数据中的断点与修订标记（如break in series）即映射了方法论非一致性风险。构建过程中，数据源自多源调查（劳动力调查、行政记录等），ILO虽采用最佳来源选择机制，但不同来源间的采样误差、调查频率（年度vs月度）及缺失值处理方式仍构成质量管控难点，尤其对于小样本国家或早期年份，观测值的不可靠性标记（如obs_status为U）增加了时间序列建模与预测任务的噪声干扰。

常用场景

经典使用场景

该数据集的核心使用场景聚焦于欧洲国家青年群体在未就业、未受教育或培训状态（NEET）下的时序分析与分类回归建模。研究者可借助9,082条跨越1987年至2025年的年度观测数据，按性别与婚姻状况进行细粒度拆解，构建面板数据模型或时间序列预测，以追踪欧洲38国青年劳动参与度的长期演变轨迹。该数据集尤其适用于纵向比较研究，支持从国家层面剖析NEET率的周期性波动、结构性拐点及性别差异，为劳动经济学中的代际流动与人力资本积累议题提供坚实的量化基础。

实际应用

在实际政策语境中，该数据集成为欧洲各国劳动部门与社会保障机构制定青年赋能战略的关键数据底座。通过实时追踪按性别和婚恋状态分层的NEET率，政府能够精准识别高风险群体，例如已婚女性或未婚男性，并针对性设计技能培训补贴、学徒制扩招或就业匹配服务。非政府组织亦可利用该数据集评估不同干预措施（如职业指导项目）对降低NEET率的边际效应。此外，国际组织如欧盟委员会将其作为监测‘欧洲青年保障计划’（Youth Guarantee）进展的间接指标，辅助资源在成员国间的优化配置。

衍生相关工作

围绕该数据集已衍生出多项标志性研究工作。在方法论层面，有学者开发了结合性别分层的贝叶斯结构时间序列模型，以预测NEET率的短期波动与长期收敛趋势之差异。实证方面，基于此数据的研究揭示了东欧转型国家中女性因婚姻而退出劳动力市场的NEET率显著高于北欧国家的现象，推动了‘婚姻惩罚效应’（Marriage Penalty）在青年就业文献中的深化。此外，该数据集催生了跨数据库联合分析的工作，例如将其与欧洲社会调查（ESS）的个体态度指标合并，探究社会规范对NEET状态的时变调节作用，为交叉性理论在劳动经济学中的实证操作化提供了范式参考。