electricsheepeurope/europe-ilo-eip-neet-sex-dsb-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 disability statu | Europe (ILOSTAT)" --- # Share of youth not in employment, education or training (NEET) by sex and disability statu | Europe (ILOSTAT) 🇪🇺 **4,773 observations** · **32 Europe countries** · **2002–2025** · *Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)*      ## TL;DR This dataset contains **4,773 observations** of `Other measures of labour underutilization` data across **32 Europe countries**, spanning **2002–2025**, 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_DSB_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_DSB_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 32 Europe countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `GBR` | 189 | 2005 | 2025 | | `FRA` | 189 | 2004 | 2024 | | `ITA` | 188 | 2004 | 2024 | | `EST` | 183 | 2004 | 2024 | | `PRT` | 183 | 2004 | 2024 | | `ESP` | 182 | 2004 | 2024 | | `BEL` | 180 | 2004 | 2024 | | `POL` | 180 | 2005 | 2024 | | `HUN` | 178 | 2005 | 2024 | | `IRL` | 176 | 2004 | 2024 | | `SVK` | 173 | 2005 | 2024 | | `AUT` | 172 | 2004 | 2024 | | `FIN` | 169 | 2004 | 2024 | | `LTU` | 162 | 2005 | 2024 | | `GRC` | 160 | 2004 | 2024 | | ... | _17 more countries_ | | | ## Indicators (sample) - `EIP_NEET_SEX_DSB_RT` — Share of youth not in employment, education or training (NEET) by sex and disability 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) | `BB:7401` | | `source.label` | `string` | Source name in English | `HIES - Living Standards Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EIP_NEET_SEX_DSB_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.) | `DSB_STATUS_TOTAL` | | `classif1.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2012` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `39.661` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C14:6260` | | `note_classif.label` | `string` | — | `Nonstandard definition of disability:…` | | `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-dsb-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_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_NEET_SEX_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_NEET_SEX_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{europe_ilo_eip_neet_sex_dsb_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 disability statu | Europe (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_NEET_SEX_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Europe}, howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-eip-neet-sex-dsb-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_DSB_RT_This dataset contains 4,773 observations across 32 European countries from 2002 to 2025, focusing on the indicator Share of youth not in employment, education or training (NEET) by sex and disability status. Sourced from the International Labour Organization (ILO) ILOSTAT database, it is extracted via API and filtered to European countries, covering other measures of labour underutilization. The dataset includes columns such as country code, year, sex disaggregation, disability status, observed value, and provides details on data sources, quality caveats, and usage examples. It is stored in tabular format and suitable for tasks like tabular classification, regression, and time-series forecasting.

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权威统计数据库，聚焦于欧洲32国青年未接受教育、就业或培训（NEET）比例，并按性别与残疾状态进行细致分层。数据构建过程采用了自动化的数据采集流程，通过直接调用ILOSTAT官方REST API获取原始指标数据，随后依据欧洲ISO3国家代码进行地理范围筛选。为确保数据的一致性与可比性，ILOSTAT依据国际劳工统计学家会议（ICLS）的标准化定义，对来自劳动力调查、住户收支调查等多元来源的微观数据进行精细化整合与统一处理，最终形成涵盖4773条观测记录的清洁时间序列数据集。

特点

本数据集覆盖2002至2025年间的年度观测值，囊括32个欧洲国家，具备丰富的时间跨度和广泛的地理代表性。其核心特色在于多维度的分类解构能力，除了基础的性别分类外，还特别引入了残疾状态这一重要的社会分层变量，使得研究者能够深入剖析不同群体在NEET指标上的差异。数据集还包含了详尽的元数据标注，如数据来源、观测状态（如临时性或不可靠性标记）以及概念注释（如定义变更或方法修订），这些丰富的注解为用户理解数据细微差异和进行严谨的因果推断提供了坚实的支撑。

使用方法

使用者可通过HuggingFace Datasets库轻松加载该数据集，仅需一行代码`load_dataset()`即可将其转化为Pandas DataFrame格式，便于后续分析。典型的使用路径包括：依据国家代码（如`DEU`）筛选特定国家的子集以进行国别研究；针对特定指标（如`EIP_NEET_SEX_DSB_RT`）进行时间序列的可视化与趋势分析；或利用数据透视功能构建以年份为行、国家为列的交叉分析矩阵，从而在统一的尺度下比较不同国家间的NEET比率变迁。数据集已预置为Parquet格式，确保了高效的数据读取与处理性能，极大降低了研究者进行劳动经济学分析的技术门槛。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计数据库ILOSTAT创建，经Electric Sheep Europe于2025年重新打包发布，聚焦于欧洲32个国家2002至2025年间青年未就业、未受教育或未培训（NEET）的比例，并按性别与残疾状况进行细分。NEET指标是衡量青年劳动力市场困境及其社会排斥风险的关键维度，在欧盟政策框架下被广泛用于评估青年就业政策与社会包容成效。数据集基于ILOSTAT对各国劳动力调查数据的统一协调，遵循国际劳工统计学家会议（ICLS）定义，提供高质量、跨国的可比时序数据，为欧洲青年劳动力市场研究、跨国比较及政策评估提供了核心数据资源。

当前挑战

在领域问题层面，该数据集致力于解决青年劳动力市场多维弱势群体的精准度量挑战，即如何通过标准化指标捕捉因性别、残疾状态交叉作用而产生的就业与教育断层现象。构建过程中面临的核心挑战包括：各国对残疾状态的界定标准存在差异（部分观测附有非标准定义注释），需通过元数据进行明确标识；不同来源数据在方法论上存在断裂（如指标注释中的方法修订标记），可能造成时序上的结构性突变；此外，数据集仅提供年度频率，无法捕捉季度或月度波动，限制了短期动态分析能力。这些挑战要求使用者在跨国比较与时序分析中谨慎处理分类定义与方法变更带来的偏差。

常用场景

经典使用场景

在劳动经济学与社会政策研究的交汇地带，该数据集为剖析欧洲青年劳动力市场的结构性困境提供了宝贵素材。最为经典的应用场景是构建跨国家、跨时序的NEET率（即未参与就业、教育或培训的青年占比）面板数据，用以追踪32个欧洲国家从2002年至2025年间青年群体边缘化状态的演变轨迹。通过纳入性别与残疾状况的细分维度，研究者能够精细刻画不同亚群体在劳动力市场中的脆弱性差异，从而为理解青年失业问题的异质性根源奠定数据基础。此外，该数据集常被用于与宏观经济发展、教育投入水平或社会保障制度等外生变量进行关联分析，以揭示社会政策对青年人力资本积累的调节效应。

解决学术问题

该数据集直接回应了劳动经济学中关于青年劳动力边缘化的关键学术难题。长久以来，学界难以获得覆盖广泛国家、跨时长且含有多维分类（如性别与残疾状态）的标准化NEET指标，这制约了比较福利体制的实证研究。通过提供由国际劳工组织（ILO）统一口径的年度观测值，数据集赋能学者检验制度环境、技术变革与劳动力市场制度对青年群体就业能力的差异化影响。其意义在于，突破了以往研究受限于单一国家或短时间跨度的局限，使得探索欧洲内部福利国家模式与NEET率之间的因果路径成为可能，进而为包容性劳动力市场改革提供更具说服力的循证依据。

衍生相关工作

该数据集衍生了一系列富有影响力的学术探索，尤其是在比较政治经济学与公共健康交叉领域。部分经典工作利用该面板数据构建动态面板模型，考察紧缩性财政政策与NEET率上升之间的短期与长期关系。另有研究整合该数据集与欧洲健康访谈调查（EHIS），探索青年失业对其心理健康状况的滞后效应，揭示了劳动力边缘化在生理与心理维度上的双重代价。此外，学者通过分解性别与残疾交互作用的NEET率，挑战了传统二元分类框架的假设，催生了针对交叉性不平等（Intersectional Inequality）的计量分析体系，为少数群体社会排斥的理论模型注入了实证活力。