electricsheepasia/asia-ilo-emp-3wap-sex-age-edu-rt-youth-employment-to-population-ratio-by-sex-age-an

--- 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 - employment - ilo - labour pretty_name: "Youth employment-to-population ratio by sex, age and education (%) | Asia (ILOSTAT)" --- # Youth employment-to-population ratio by sex, age and education (%) | Asia (ILOSTAT) 🌏 **23,528 observations** · **38 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **23,528 observations** of `Employment` data across **38 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=EMP_3WAP_SEX_AGE_EDU_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Employment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_3WAP_SEX_AGE_EDU_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 38 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 1,969 | 1990 | 2023 | | `TUR` | 1,487 | 2000 | 2024 | | `PSE` | 1,381 | 2000 | 2025 | | `KHM` | 1,377 | 1996 | 2023 | | `KOR` | 1,367 | 2000 | 2025 | | `IRN` | 1,364 | 2005 | 2024 | | `CYP` | 1,245 | 1999 | 2024 | | `MNG` | 1,177 | 2000 | 2024 | | `PAK` | 1,037 | 2005 | 2025 | | `THA` | 1,029 | 2000 | 2024 | | `VNM` | 962 | 2010 | 2024 | | `ARM` | 836 | 2001 | 2023 | | `IND` | 810 | 1994 | 2025 | | `LKA` | 785 | 2010 | 2024 | | `GEO` | 780 | 2009 | 2024 | | ... | _23 more countries_ | | | ## Indicators (sample) - `EMP_3WAP_SEX_AGE_EDU_RT` — Youth employment-to-population ratio by sex, age and education (%) ## 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 | `EMP_3WAP_SEX_AGE_EDU_RT` | | `indicator.label` | `string` | Indicator name in English | `Youth employment-to-population ratio …` | | `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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `40.379` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-emp-3wap-sex-age-edu-rt-youth-employment-to-population-ratio-by-sex-age-an") 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"] == "EMP_3WAP_SEX_AGE_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EMP_3WAP_SEX_AGE_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EMP_3WAP_SEX_AGE_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_emp_3wap_sex_age_edu_rt_youth_employment_to_population_ratio_by_sex_age_an_2025, title = {Youth employment-to-population ratio by sex, age and education (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_3WAP_SEX_AGE_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-3wap-sex-age-edu-rt-youth-employment-to-population-ratio-by-sex-age-an}} } ``` ## 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=EMP_3WAP_SEX_AGE_EDU_RT_This dataset contains 23,528 observations of youth employment-to-population ratio by sex, age and education (%) across 38 Asia countries, spanning from 1970 to 2025. It is sourced from the ILOSTAT database of the International Labour Organization (ILO), filtered to Asia ISO3 country codes, and includes disaggregation dimensions such as sex, age, and education, along with data quality notes and usage examples.

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）的ILOSTAT中央统计数据库，通过其REST API接口直接提取核心指标EMP_3WAP_SEX_AGE_EDU_RT，并依据ISO 3166-1 alpha-3编码筛选出亚洲38个国家的数据记录。原始微观调查数据由ILO依据国际劳工统计学家会议（ICLS）定义进行统一协调处理，数据来源在source.label列中予以明确标注。随后由Electric Sheep Asia团队对数据进行重封装，规范化为Parquet格式，并整合为统一的机器学习就绪状态，方便研究人员直接调用。

特点

该数据集涵盖1970年至2025年间亚洲38个国家的23,528条观测记录，聚焦于青年就业人口比率这一关键劳动力指标，并按性别、年龄及教育程度进行了精细的分层统计。数据维度丰富，包含性别、年龄分组与教育水平等分类变量，同时提供数据来源编号、观测状态标志及各类注释信息，有助于用户评估数据质量与潜在偏差。数据以年度频率呈现，确保了时间序列分析的连贯性与可比性。

使用方法

用户可通过HuggingFace Datasets库中的load_dataset函数直接加载数据集，随后转换为pandas DataFrame进行深入分析。支持按国家代码筛选特定地区子集，也可基于指标代码提取单一时间序列数据进行趋势可视化。此外，利用pivot_table方法可将数据重塑为国家×年份的矩阵形式，便于开展跨国比较与面板数据分析。数据集以cc-by-4.0许可协议发布，使用时需同时引用ILO原始来源及Electric Sheep Asia的重封装版本。

背景与挑战

背景概述

青年就业是衡量一个地区劳动力市场健康程度与社会发展潜力的关键指标。在此背景下，国际劳工组织（ILO）依托其权威统计数据库ILOSTAT，构建了覆盖亚洲38个国家、时间跨度从1970年至2025年的青年就业人口比率数据集（EMP_3WAP_SEX_AGE_EDU_RT）。该数据集由Electric Sheep Asia于2025年重新打包发布，专注于按性别、年龄和教育层次细分的青年就业与人口比率，共包含23,528条观测记录。作为全球劳动统计的基准来源，ILO通过整合各国劳动力调查、家庭收支调查等多源行政数据，为该领域提供了高质量的标准化数据支撑。该数据集的发布不仅推动了亚洲区域劳动经济学研究的深化，也为可持续发展目标中体面工作的监测评估提供了不可或缺的实证基础。

当前挑战

该数据集所解决的核心领域问题在于，亚洲青年就业市场长期面临数据碎片化、指标口径不一以及时间序列不连续的困境，致使跨国家、跨时期的比较分析困难重重。在构建过程中，ILO需应对各国统计体系差异带来的挑战，如不同国家对年龄分组（如15-24岁与15-29岁）、教育层次划分（如ISCED标准与各国自定义标准）以及劳动力定义（如是否包含非正规就业）的歧义，需进行复杂的国际标准化调和。此外，数据质量标注中存在的“不可靠”“方法修订”“非标准教育层级”等异常状态表明，原始来源的统计误差与数据断点在整合中难以完全消除，需要研究者在使用时审慎评估和清洗。

常用场景

经典使用场景

在劳动经济学与发展研究领域，该数据集最经典的使用场景是作为时间序列面板数据，用于分析亚洲各国青年就业率的长期演变趋势及性别、年龄与教育水平维度的结构性差异。研究者可借助其覆盖1970至2025年、囊括38个亚洲国家的丰富观测值，构建固定效应或随机效应模型，系统探究经济发展阶段、教育扩张政策与劳动力市场制度对青年就业参与率的异质性影响。同时，数据集支持按性别（男/女/总）、年龄段（如15-29岁青年组）及教育水平（如总教育、各层级教育）进行交叉分组，为揭示青年失业脆弱群体、评估教育回报率以及比较不同国家青年就业转型路径提供了标准化的量化基础。

衍生相关工作

该数据集为一系列具有影响力的衍生学术工作与开放数据产品提供了基石。基于其面板结构，研究者可提取长期时序数据用于训练和验证时间序列预测模型，预测未来五年内不同亚洲国家的青年就业走势，进而催生了关于‘就业弹性’与‘劳动吸收率’的比较研究。数据集的标准化格式还促进了其与教育统计、宏观经济指标（如GDP增长率、青年人口占比）的融合，衍生出探讨产业结构转型与青年就业关系、教育扩张对劳动力市场回报影响的计量经济学论文。在国际劳工组织内部，该数据常被用于其旗舰报告《全球青年就业趋势》的亚洲章节编制；在开放数据社区中，它作为高质量、机器就绪的面板数据集，成为数据科学课程中‘多类别时间序列回归’与‘分层分析’的教学案例，加速了数据驱动政策分析的方法论传播。

数据集最近研究

最新研究方向

在亚太地区劳动力市场结构性变迁的宏大背景下，该数据集聚焦于青年就业人口比率的性别、年龄与教育水平交叉分解，为解析后疫情时代亚洲经济体‘就业脆弱性’与‘人力资本错配’提供了高分辨率的时间序列证据。当前前沿研究正借助此数据揭示数字转型与绿色转型过程中青年群体从非正规就业向知识密集型岗位转移的异质性路径，特别是在东南亚与南亚新兴经济体之间。结合ILO‘全球青年就业趋势’报告中的热点警示，该数据集使学者能够量化教育与技能不匹配对青年劳动参与率的长期压抑效应，从而为设计定向职业培训与包容性增长政策提供了不可替代的实证基石，其跨度为1970至2025年的长序列记录更成为评估代际公平与社会流动性的宝贵时空标尺。