electricsheepasia/asia-ilo-eap-3eap-sex-age-stu-nb-youth-labour-force-by-sex-age-and-school-attendanc

--- 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 - labour-force - ilo - labour - employment pretty_name: "Youth labour force by sex, age and school attendance status (thousands) | Asia (ILOSTAT)" --- # Youth labour force by sex, age and school attendance status (thousands) | Asia (ILOSTAT) 🌏 **15,389 observations** · **36 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **15,389 observations** of `Labour force` data across **36 Asia countries**, spanning **1970–2025**, 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=EAP_3EAP_SEX_AGE_STU_NB) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Labour force ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EAP_3EAP_SEX_AGE_STU_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 36 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 1,008 | 1996 | 2023 | | `KHM` | 940 | 1996 | 2023 | | `CYP` | 931 | 1999 | 2024 | | `KOR` | 917 | 2000 | 2025 | | `TUR` | 900 | 2000 | 2024 | | `PSE` | 864 | 2000 | 2025 | | `IRN` | 720 | 2005 | 2024 | | `MNG` | 710 | 2000 | 2024 | | `VNM` | 663 | 2007 | 2024 | | `PHL` | 654 | 2006 | 2023 | | `PAK` | 636 | 2005 | 2025 | | `ARM` | 632 | 2001 | 2023 | | `IND` | 577 | 1994 | 2025 | | `KGZ` | 550 | 2007 | 2023 | | `THA` | 540 | 2000 | 2024 | | ... | _21 more countries_ | | | ## Indicators (sample) - `EAP_3EAP_SEX_AGE_STU_NB` — Youth labour force by sex, age and school attendance status (thousands) ## Schema | Column | Type | Description | Example | |--------|------|-------------|---------| | `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AFG` | | `ref_area.label` | `string` | Country name in English | `Afghanistan` | | `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:15715` | | `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` | | `indicator` | `string` | ILOSTAT indicator code | `EAP_3EAP_SEX_AGE_STU_NB` | | `indicator.label` | `string` | Indicator name in English | `Youth labour force by sex, age and sc…` | | `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.) | `AGE_YTHBANDS_Y15-29` | | `classif1.label` | `string` | — | `Age (Youth bands): 15-29` | | `classif2` | `string` | Second classification variable where applicable | `STU_EDU_TOTAL` | | `classif2.label` | `string` | — | `Educational attendance: Total` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `3519.29` | | `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_S3:8` | | `note_source.label` | `string` | — | `Repository: ILO-STATISTICS - Micro da…` | ## Disaggregation dimensions The following columns provide disaggregation dimensions: - **`sex`** (4 unique values): `SEX_T`, `SEX_M`, `SEX_F`, `SEX_O` ## 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-eap-3eap-sex-age-stu-nb-youth-labour-force-by-sex-age-and-school-attendanc") 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"] == "EAP_3EAP_SEX_AGE_STU_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EAP_3EAP_SEX_AGE_STU_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EAP_3EAP_SEX_AGE_STU_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eap_3eap_sex_age_stu_nb_youth_labour_force_by_sex_age_and_school_attendanc_2025, title = {Youth labour force by sex, age and school attendance status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_3EAP_SEX_AGE_STU_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eap-3eap-sex-age-stu-nb-youth-labour-force-by-sex-age-and-school-attendanc}} } ``` ## 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=EAP_3EAP_SEX_AGE_STU_NB_This dataset contains 15,389 observations of youth labour force data (in thousands) disaggregated by sex, age, and school attendance status across 36 Asian countries from 1970 to 2025. Sourced from the International Labour Organization (ILO) ILOSTAT database via API and filtered for Asian countries, it includes one key indicator (EAP_3EAP_SEX_AGE_STU_NB) with fields such as country codes, indicator codes, sex classification, age groups, education status, year, observed values, and data quality flags. Suitable for tabular classification, regression, and time-series forecasting tasks, it is released under the CC-BY-4.0 license and repackaged by Electric Sheep Asia.

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）下属的ILOSTAT统计数据库，通过REST API直接获取指标代码为EAP_3EAP_SEX_AGE_STU_NB的原始数据，并依据亚洲国家ISO3代码进行地域筛选。数据采集范围覆盖36个亚洲国家，时间跨度从1970年至2025年，共计15,389条观测记录。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查微观数据进行统一清洗与标准化处理，数据来源在source.label字段中予以标注，确保每一条记录均可追溯。数据集由Electric Sheep Asia团队重新封装，以Parquet格式发布，便于机器学习场景下的高效加载与使用。

使用方法

可通过HuggingFace Datasets库的load_dataset函数直接加载该数据集，返回Dataset对象，并支持一键转换为Pandas DataFrame以便后续分析。用户可利用ref_area字段按国家代码筛选特定国家子集，如印度尼西亚（IDN）或韩国（KOR）的时序数据。针对时间序列分析，可对indicator字段进行过滤后按time字段排序，绘制观测值随年份变化的趋势图。此外，利用pivot_table函数可以轻松构建以年份为行、国家为列的矩阵式面板数据，便于进行跨国的横向对比与统计建模。数据集结构规范、列名清晰，适合作为社会经济学、劳动经济学以及亚洲发展研究领域的定量分析基础。

背景与挑战

背景概述

青年劳动力参与率是衡量一国人力资本储备与未来经济发展潜力的核心指标，尤其在亚洲这一全球人口最为稠密的地区，其变化趋势对国际劳动力市场格局与社会政策制定具有深远影响。由国际劳工组织（ILO）统计部门基于ILOSTAT数据库构建的该数据集，于2025年由Electric Sheep Asia重新封装并发布，涵盖36个亚洲国家1970年至2025年间共15,389条观测记录。核心研究问题聚焦于按性别、年龄及在校状态分层的青年劳动力数量分布，旨在揭示教育参与如何与劳动力供给结构相互作用，为理解亚洲地区青年就业困境、性别不平等及人力资本投资决策提供关键量化依据。作为ILO权威统计数据在亚洲区域的专项集成，该数据集为发展经济学、劳动经济学及公共政策研究提供了跨越半个世纪的高质量时间序列数据，显著增强了跨国比较分析与长期趋势建模的可操作性。

当前挑战

该数据集所应对的领域挑战在于，亚洲各国青年劳动力统计数据长期存在来源分散、定义标准不一、时间序列断裂等痼疾，制约了区域层面就业政策的精准制定与影响评估。构建过程中首先面临数据整合的挑战，ILOSTAT需从各国劳动力调查、家庭收支调查及行政记录等多源异构数据中，依据国际劳工统计学家大会（ICLS）定义进行协调统一，确保跨年代与跨国别的可比性；其次，数据质量标识的复杂性构成显著难题，观测值的状态标记（如“不可靠”或“方法修订导致序列断裂”）需要用户精细甄别，同时分类维度中的缺失值处理、最佳来源选择逻辑以及年度频率对月度或季度波动信息的掩盖，均对下游建模的稳健性与推断精度提出了严格要求。

常用场景

经典使用场景

该数据集汇集了1970年至2025年间亚洲36个国家的青年劳动力数据，按性别、年龄组及在校状态进行细分，共计超过1.5万条观测记录。经典的使用场景包括构建多元时间序列模型，用以分析亚洲青年劳动力参与率的长期演变趋势，以及运用面板数据计量经济学方法，探讨经济发展、教育扩张与青年就业之间的动态关联。研究者常以此数据为基准，对比不同性别与年龄段的劳动力供给差异，揭示亚洲地区青年就业的结构性特征。

解决学术问题

数据集有效解决了亚洲青年劳动力市场研究中长期存在的样本覆盖不足与数据标准不统一的问题。它使学者能够系统性地衡量性别工资差距、教育回报率以及在校与不在校青年群体的就业差异，从而深入剖析“尼特族”（NEET）现象的区域分布与影响因素。该数据为检验人力资本理论、劳动力市场分割理论在发展中国家的适用性提供了可靠的实证基础，推动了劳动经济学与教育经济学的交叉研究。

实际应用

在实际应用层面，该数据集为国际劳工组织、各国劳动部门及政策研究机构提供了关键决策支持。政府部门可依据不同性别与年龄段的青年劳动力参与率，评估现有就业促进政策的成效，并针对性地制定青年技能培训计划与职业指导项目。非政府组织与开发银行亦能利用该数据识别弱势青年群体，设计精准的社会干预措施，助力实现联合国可持续发展目标中关于体面工作与经济增长的具体指标。

数据集最近研究

最新研究方向

当前，随着全球人口结构变迁与劳动力市场格局重塑，青年就业议题已成为国际发展议程的聚焦核心。该数据集针对亚太地区36国横跨半世纪的青年劳动力动态，为时序建模与面板数据分析提供了珍贵素材。前沿研究趋向于利用此类高颗粒度数据，结合因果推断与机器学习方法，深入探究教育参与率提升对青年劳动供给弹性的异质性影响，尤其关注失学青年群体的脆弱性。同时，数据中所蕴含的性别维度与年龄分层信息，正被用以剖析新兴经济体在数字化转型浪潮下，青年劳动参与模式的结构性变迁及其与可持续发展目标的交互关联。该数据集的系统整合与开放共享，为精准描绘亚洲青年就业图景、审视区域政策干预效果提供了坚实的定量支撑。