electricsheepasia/asia-ilo-eip-neet-sex-cct-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: - n<1K tags: - tabular - asia - ilostat - international-migrant-stock - ilo - labour - employment pretty_name: "Share of youth not in employment, education or training (NEET) by sex and citizenship (%) | Asia (ILOSTAT)" --- # Share of youth not in employment, education or training (NEET) by sex and citizenship (%) | Asia (ILOSTAT) 🌏 **914 observations** · **19 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **914 observations** of `International migrant stock` data across **19 Asia countries**, spanning **1999–2025**, 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_CCT_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** International migrant stock ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EIP_NEET_SEX_CCT_RT` and filtered to Asia 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 19 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 230 | 1999 | 2024 | | `IRN` | 180 | 2005 | 2024 | | `ARM` | 75 | 2001 | 2023 | | `BRN` | 72 | 2014 | 2024 | | `GEO` | 63 | 2012 | 2018 | | `JOR` | 63 | 2017 | 2024 | | `SAU` | 54 | 2019 | 2025 | | `MDV` | 30 | 2009 | 2019 | | `TLS` | 28 | 2010 | 2022 | | `LAO` | 24 | 2015 | 2022 | | `IDN` | 21 | 2010 | 2023 | | `THA` | 18 | 2023 | 2024 | | `NPL` | 9 | 2008 | 2008 | | `KGZ` | 9 | 2009 | 2009 | | `LBN` | 9 | 2019 | 2019 | | ... | _4 more countries_ | | | ## Indicators (sample) - `EIP_NEET_SEX_CCT_RT` — Share of youth not in employment, education or training (NEET) by sex and citizenship (%) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `ARM` | | `ref_area.label` | `string` | Country name in English | `Armenia` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BB:173` | | `source.label` | `string` | Source name in English | `HIES - Households Living Conditions S…` | | `indicator` | `string` | ILOSTAT indicator code | `EIP_NEET_SEX_CCT_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.) | `CCT_CIT_TOTAL` | | `classif1.label` | `string` | — | `Citizenship: Total` | | `time` | `int64` | Observation year | `2023` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `20.177` | | `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_T3:240` | | `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("electricsheepasia/asia-ilo-eip-neet-sex-cct-rt-share-of-youth-not-in-employment-education-or-trai") df = ds["train"].to_pandas() print(df.head()) ``` ### Filter to one country ```python indonesia = df[df["ref_area"] == "IDN"] ``` ### Time-series for a single indicator ```python sample = (df[df["indicator"] == "EIP_NEET_SEX_CCT_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_NEET_SEX_CCT_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_NEET_SEX_CCT_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_neet_sex_cct_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 citizenship (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_NEET_SEX_CCT_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-neet-sex-cct-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 Asia repackaging. ## About Electric Sheep Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia 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/electricsheepasia](https://huggingface.co/electricsheepasia) --- _Provenance: ingested 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_NEET_SEX_CCT_RT_This dataset contains International migrant stock data from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator Share of youth not in employment, education or training (NEET) by sex and citizenship (%). It covers 19 Asian countries from 1999 to 2025, with 914 observations. Data is pulled directly from the ILOSTAT REST API and filtered to Asia ISO3 country codes. The dataset is presented in tabular format with columns such as country code (ref_area), indicator code (indicator), year (time), observed value (obs_value), and can be disaggregated by dimensions like sex (gender). Data is annual frequency and harmonized using ILO statistical definitions, suitable for tasks like tabular classification, regression, or time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集基于国际劳工组织（ILO）旗下的ILOSTAT数据库构建，通过其REST API直接提取指标代码为EIP_NEET_SEX_CCT_RT的原始数据。数据覆盖1999年至2025年间19个亚洲国家，共计914条观测记录。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对调查微观数据进行标准化处理，并在source.label列中标注数据来源以保障可追溯性。数据集由Electric Sheep Asia团队重新封装，以Parquet格式发布，便于机器学习工作流程直接调用。

特点

数据集聚焦于亚洲地区青年（15-24岁）未就业、未接受教育或培训（NEET）的比例，并按性别和公民身份进行细分。包含sex、classif1等分类维度，其中sex字段涵盖总计、男性和女性三类。数据以年度频率呈现，部分来源和指标附有注释（如breaks in series），帮助用户评估数据质量。19个国家的覆盖范围从高收入经济体到发展中国家，时间跨度长，为区域比较和时间序列分析提供了坚实基础。

使用方法

用户可通过HuggingFace Datasets库的load_dataset函数直接加载数据，并转换为Pandas DataFrame进行后续分析。典型用法包括按国家代码过滤特定国家数据（如印尼），对单个指标进行时间序列可视化，以及透视操作生成国家×年份矩阵。数据集支持表格分类、回归和时间序列预测任务，其规范化的字段结构便于快速集成到统计分析或机器学习模型中。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年整理发布，并由Electric Sheep Asia团队从ILOSTAT数据库重新打包，专注于亚洲地区19个国家1999至2025年间青年未就业、未接受教育或培训（NEET）的比例，按性别和公民身份分类。NEET指标作为衡量青年劳动力市场困境与社会排斥程度的核心工具，对理解亚洲地区人力资本积累、劳动力结构转型及可持续发展目标（SDG）的实现至关重要。该数据集涵盖了914条观测记录，整合了多源调查数据并经ILO统一标准化处理，为区域间比较研究提供了可靠的数据基础，显著推动了发展经济学与劳动经济学领域的定量分析。

当前挑战

该数据集面临的挑战首先在于NEET指标的领域适用性：亚洲各国青年群体的教育体系、非正规就业形态及家庭支持网络差异显著，统一的NEET定义可能掩盖结构性异质性，导致跨国比较的效度受限。其次，数据构建过程中遭遇多重困难：观测值仅为914条，样本量较小且国家间覆盖极不均衡（如塞浦路斯有230条记录而尼泊尔仅9条），部分数据标注为“不可靠”或存在方法学断裂，影响时间序列的一致性；同时，数据仅提供年度频率，缺乏更高时间分辨率的序列，无法捕捉短期劳动力市场波动对青年NEET率的冲击。

常用场景

经典使用场景

在劳动经济学与青年发展研究中，该数据集被广泛用于建模和预测亚洲各国青年NEET（未就业、未受教育、未接受培训）比率的时间演变趋势。研究人员常依性别与公民身份维度进行纵向比较，借助时间序列回归或面板数据分析，揭示不同国家青年群体在劳动力市场边缘化的动态特征。其年度观测覆盖1999至2025年，为构建区域性的青年失业预警模型提供了统一且规范的基础数据源。

实际应用

在实际政策制定层面，该数据集为国际组织及国家劳动部门监测和评估青年就业促进项目提供了量化工具。例如，可通过追踪特定国家女性青年NEET率的年度变化，检验技能培训计划或创业扶持政策的实效性。同时，数据有助于识别高NEET率的脆弱群体，指导社会救助资源的精准投放，并为跨区域劳动力流动政策的优化提供依据。

衍生相关工作

依托该数据集的规范结构，衍生出了多项关于亚洲青年劳动力市场状态的基准研究与模型工作。包括构建基于性别和公民身份的NEET率预测基线，开发区域间青年就业质量对比的可视化仪表盘，以及整合教育人口统计数据进行因果推断的分析框架。此外，该数据集也常作为特征工程的一部分，被纳入更广泛的亚太地区可持续发展目标（SDG）监测体系中，支持关于体面工作和经济增长的多维评估。