electricsheepasia/asia-ilo-pop-3wap-sex-edu-tra-nb-youth-working-age-population-by-sex-education-and

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 10K<n<100K tags: - tabular - asia - ilostat - population - ilo - labour - employment pretty_name: "Youth working-age population by sex, education and forms of transition (thousands) | Asia (ILOSTAT)" --- # Youth working-age population by sex, education and forms of transition (thousands) | Asia (ILOSTAT) 🌏 **11,739 observations** · **17 Asia countries** · **1999–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **11,739 observations** of `Population` data across **17 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=POP_3WAP_SEX_EDU_TRA_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Population ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=POP_3WAP_SEX_EDU_TRA_NB` 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 17 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 2,862 | 1999 | 2024 | | `PAK` | 1,441 | 2006 | 2021 | | `KOR` | 1,235 | 2015 | 2025 | | `JOR` | 1,010 | 2012 | 2024 | | `VNM` | 775 | 2013 | 2020 | | `MNG` | 745 | 2019 | 2024 | | `GEO` | 674 | 2019 | 2024 | | `PSE` | 594 | 2013 | 2025 | | `MMR` | 546 | 2015 | 2020 | | `TLS` | 388 | 2010 | 2021 | | `KHM` | 374 | 2012 | 2019 | | `LAO` | 274 | 2017 | 2022 | | `NPL` | 245 | 2013 | 2017 | | `ARM` | 196 | 2012 | 2014 | | `BGD` | 146 | 2013 | 2013 | | ... | _2 more countries_ | | | ## Indicators (sample) - `POP_3WAP_SEX_EDU_TRA_NB` — Youth working-age population by sex, education and forms of transition (thousands) ## 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) | `BX:6366` | | `source.label` | `string` | Source name in English | `HS - School to Work Transition Survey` | | `indicator` | `string` | ILOSTAT indicator code | `POP_3WAP_SEX_EDU_TRA_NB` | | `indicator.label` | `string` | Indicator name in English | `Youth working-age population by sex, …` | | `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 | `TRA_FORMS_TOTAL` | | `classif2.label` | `string` | — | `Transition forms: Total` | | `time` | `int64` | Observation year | `2014` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `747.805` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `note_indicator` | `string` | — | `I11:264` | | `note_indicator.label` | `string` | — | `Break in series: Methodology revised` | | `note_source` | `string` | — | `R1:3513_T3:2481` | | `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-pop-3wap-sex-edu-tra-nb-youth-working-age-population-by-sex-education-and") 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"] == "POP_3WAP_SEX_EDU_TRA_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_3WAP_SEX_EDU_TRA_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_3WAP_SEX_EDU_TRA_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_pop_3wap_sex_edu_tra_nb_youth_working_age_population_by_sex_education_and_2025, title = {Youth working-age population by sex, education and forms of transition (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_3WAP_SEX_EDU_TRA_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-pop-3wap-sex-edu-tra-nb-youth-working-age-population-by-sex-education-and}} } ``` ## 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=POP_3WAP_SEX_EDU_TRA_NB_This dataset contains 11,739 observations of youth working-age population data across 17 Asia countries, spanning from 1999 to 2025, disaggregated by sex, education, and forms of transition, in thousands. It covers one distinct indicator (POP_3WAP_SEX_EDU_TRA_NB), sourced from the ILOSTAT database of the International Labour Organization (ILO), obtained via the ILOSTAT REST API and filtered for Asia ISO3 country codes. The data is presented in tabular format with columns such as country code, year, sex classification, education level, transition forms, observed value, etc., suitable for tabular classification, regression, and time-series forecasting tasks. Data quality notes include annual frequency, potential unreliable observations, and disaggregation columns being non-null only when breakdowns are published.

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数据库，经由Electric Sheep Asia团队重新封装整理。原始数据通过ILOSTAT REST API接口获取，筛选出亚洲地区ISO3国家代码的观测记录，并以Parquet格式发布，确保机器学习场景下的高效加载与处理。数据集构建过程中严格遵循ILO基于国际劳工统计学家会议（ICLS）定义的数据协调流程，在source.label列中标注数据来源，以保障数据的可追溯性与权威性。

特点

此数据集汇聚了17个亚洲国家在1999至2025年间关于青年劳动适龄人口按性别、教育程度及过渡形式分类的11,739条观测记录。其核心特征在于提供精细化的多维分类维度，包括性别（男、女、合计）以及按教育与过渡形式划分的聚合层级，能够支持复杂的交叉分析与时间序列建模。数据集还包含了观测状态标记及数据断点等质量注释，为用户评估数据可靠性提供了透明依据。

使用方法

研究人员可通过HuggingFace Datasets库直接加载数据集，利用一行代码即可获取完整的训练集DataFrame。对于特定国家的分析，可基于ref_area列进行筛选；欲进行时间序列分析，则可按indicator与time列排序并可视化观测值。此外，数据集支持通过pivot_table方法构建国家×年份的矩阵视图，便于进行跨国的比较研究或面板数据分析。所有用法均遵循cc-by-4.0许可协议，使用时应同时引用原始ILO数据及Electric Sheep Asia的再封装版本。

背景与挑战

背景概述

在全球劳动力市场研究中，青年就业与教育衔接问题始终是政策制定与学术探索的核心议题。由国际劳工组织（ILO）于2025年发布的“asia-ilo-pop-3wap-sex-edu-tra-nb-youth-working-age-population-by-sex-education-and”数据集，源自其旗舰统计数据库ILOSTAT，并由Electric Sheep Asia重新封装为机器学习就绪格式。该数据集聚焦亚洲17个国家1999至2025年间按性别、教育水平和过渡形式划分的青年工作年龄人口（单位：千），收录11,739条观测记录。其核心研究问题在于揭示教育体系与劳动力市场之间的动态关联，尤其关注青年从学校向工作过渡的多元路径与结构性差异。作为ILO在劳动统计领域权威性的体现，该数据集为跨国比较、时间序列分析与可持续生计研究提供了标准化基础，助力实现体面劳动与经济增长（SDG 8）等全球目标。

当前挑战

该数据集在领域问题与构建层面面临多重挑战。领域层面，亚洲国家间教育统计口径、劳动力调查方法与性别分类标准的异质性，使得跨区域比较与模型泛化面临严峻考验；青年就业过渡形式（如正式与非正式就业、季节性迁移）的界定模糊，进一步增加了时序预测与分类任务的复杂性。构建过程中，数据源自各国劳动力调查、行政记录等多源异构资料，ILO虽通过国际劳工统计学家会议（ICLS）定义进行协调，但观测状态标志（如“不可靠”）与系列中断记录（如方法论修订）提示了数据质量与一致性的显著挑战；此外，部分国家年份覆盖稀疏（如孟加拉国仅2013年数据），导致缺失值与样本不平衡问题突出，对多变量回归与时空建模构成障碍。

常用场景

经典使用场景

该数据集汇集了国际劳工组织ILOSTAT数据库中亚洲17个国家1999至2025年间青年劳动适龄人口的翔实数据，按性别、教育程度及过渡形式进行精细化分层。经典的研究范式通常利用这些时序与截面交织的数据，构建面板数据模型，以剖析青年劳动力供给的结构性变迁。研究者可借此探察不同教育背景的青年群体在就业、失业或非经济活动间的流动路径，亦或追踪特定地区性别差异的演变轨迹。其严谨的编码体系和多年跨度，为劳动经济学与人口学中的比较研究提供了坚实的实证基础。

衍生相关工作

围绕该数据集所包含的ILOSTAT原始指标，衍生了一系列具有影响力的研究工作。其中，基于面板数据计量方法的劳动供给弹性估算与教育回报率分析是该领域最经典的应用之一。借助其多年跨国结构，研究者拓展了早期静态比较的边界，动态地评估了经济增长、政策干预与人口流动对青年就业结构的异质性影响。此外，该数据还催生了一批利用机器学习预测劳动力市场趋势的实证研究，将传统的时间序列分析技术与现代计算模型相结合，为劳动经济学的前沿方法探索提供了宝贵的数据土壤和基准测试平台。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲青年劳动年龄人口按性别、教育水平及过渡形态的细分统计，为劳动经济学与人口学交叉领域的前沿研究提供了关键支撑。当前，亚洲作为全球青年人口最为集中的区域，其就业转型与技能错配问题备受国际组织关注。该数据集整合了ILOSTAT官方数据，覆盖1999至2025年间17个亚洲国家的近12,000条观测，能够支撑多维度时间序列分析，助力探究教育对青年从学校到职场过渡路径的影响。结合近年来联合国可持续发展目标（SDG 8.6）对青年就业的强调，此类细粒度人口数据为评估区域劳动力市场韧性、性别平等进展及教育投资回报率提供了量化基础。通过重封装为HuggingFace格式，该数据集显著降低了机器学习与数据科学领域的接入门槛，有望推动自动化预测建模与政策模拟研究的发展。