electricsheepasia/asia-ilo-eap-3wap-sex-age-dsb-rt-youth-labour-force-participation-rate-by-sex-age-a

--- 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 - asia - ilostat - labour-force - ilo - labour - employment pretty_name: "Youth labour force participation rate by sex, age and disability status (%) | Asia (ILOSTAT)" --- # Youth labour force participation rate by sex, age and disability status (%) | Asia (ILOSTAT) 🌏 **4,123 observations** · **23 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **4,123 observations** of `Labour force` data across **23 Asia countries**, spanning **1996–2024**, 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_3WAP_SEX_AGE_DSB_RT) - **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_3WAP_SEX_AGE_DSB_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 23 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `MNG` | 619 | 2006 | 2024 | | `CYP` | 562 | 2005 | 2024 | | `ARM` | 507 | 2007 | 2023 | | `KHM` | 448 | 1996 | 2023 | | `IDN` | 324 | 2010 | 2023 | | `LKA` | 227 | 2018 | 2024 | | `PSE` | 194 | 2013 | 2022 | | `THA` | 176 | 2007 | 2019 | | `BGD` | 146 | 2011 | 2024 | | `TLS` | 126 | 2015 | 2022 | | `LAO` | 104 | 2015 | 2022 | | `IRQ` | 100 | 2007 | 2021 | | `TJK` | 99 | 2003 | 2016 | | `AFG` | 98 | 2017 | 2021 | | `PAK` | 72 | 2020 | 2021 | | ... | _8 more countries_ | | | ## Indicators (sample) - `EAP_3WAP_SEX_AGE_DSB_RT` — Youth labour force participation rate by sex, age and disability status (%) ## 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_3WAP_SEX_AGE_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Youth labour force participation rate…` | | `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 | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: Total` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `43.844` | | `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_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-3wap-sex-age-dsb-rt-youth-labour-force-participation-rate-by-sex-age-a") 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_3WAP_SEX_AGE_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EAP_3WAP_SEX_AGE_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EAP_3WAP_SEX_AGE_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eap_3wap_sex_age_dsb_rt_youth_labour_force_participation_rate_by_sex_age_a_2024, title = {Youth labour force participation rate by sex, age and disability status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EAP_3WAP_SEX_AGE_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eap-3wap-sex-age-dsb-rt-youth-labour-force-participation-rate-by-sex-age-a}} } ``` ## 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_3WAP_SEX_AGE_DSB_RT_This dataset contains 4,123 observations of Labour force data across 23 Asia countries, spanning 1996–2024, covering 1 distinct indicator: Youth labour force participation rate by sex, age and disability status (%). It is sourced from the International Labour Organization (ILO) ILOSTAT database, filtered to Asia ISO3 country codes, and includes data from various sources such as labour force surveys and household income surveys, harmonized by ILOs Department of Statistics. The dataset features columns like country code, indicator, sex disaggregation, age classification, disability status, observation year, and observed value, 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统计数据库，通过REST API接口直接获取指标代码为'EAP_3WAP_SEX_AGE_DSB_RT'的原始数据，并依据亚洲ISO3国家代码进行筛选和过滤。ILOSTAT遵循国际劳工统计学家会议（ICLS）的定义，对各国劳动力调查微观数据进行统一协调和标准化处理，确保数据跨国的可比性。原始数据来源信息被保留在'source.label'列中，以支持用户对数据溯源的需求。Electric Sheep Asia团队进一步对数据进行了封装与格式规范化，使其以Parquet格式存储，便于机器学习和数据分析流程直接调用。

特点

该数据集聚焦亚洲地区青年劳动力参与率，涵盖1996年至2024年间23个亚洲国家的4,123次观测值。数据按性别、年龄组和残疾状况三个维度进行精细分层，提供'SEX_T'（总计）、'SEX_M'（男性）、'SEX_F'（女性）及'SEX_O'（其他）四种性别分类，年龄分组聚焦15-29岁青年群体，残疾状态包含总体与细分类型。此外，数据集附带了观测值的状态标记（如临时或不可靠）、来源注释及方法变动的说明，为用户评估数据质量提供了充分的元数据支持。

使用方法

用户可通过HuggingFace Datasets库轻松加载该数据集，调用`load_dataset`函数即可获取训练集，并利用`to_pandas()`方法转换为Pandas DataFrame进行后续分析。典型应用包括按国家筛选数据以研究特定国家的青年劳动力趋势，或按时间序列绘制单一指标的演变曲线。此外，用户能够通过透视表操作将数据重塑为'国家×年份'的矩阵形式，便于进行跨国比较和面板数据分析。数据以年度频率发布，并提供了详细的模式文档，涵盖各字段类型与含义，降低了数据探索与预处理的门槛。

背景与挑战

背景概述

在全球劳动力市场研究中，青年群体因技能经验不足、信息不对称及结构性歧视，其劳动参与率长期受到学术界与政策制定者的密切关注。国际劳工组织（ILO）作为全球劳动统计的权威来源，其下属统计数据库ILOSTAT的建立为衡量各国劳动力状况提供了标准化基准。该数据集由Electric Sheep Asia于2024年基于ILOSTAT数据重新整理封装，聚焦亚洲地区23个国家的青年劳动参与率，按性别、年龄及残疾状况进行细分，时间跨度涵盖1996年至2024年，共计4123条观测记录。其核心研究问题在于揭示亚洲不同子群体青年在劳动力市场中参与程度的异质性，为区域不平等、包容性就业及可持续发展目标（SDGs）的量化评估提供数据支撑，对发展经济学、劳动社会学及国际政策比较等领域具有重要参考价值。

当前挑战

该数据集所解决的领域问题主要围绕青年劳动参与率的精细度量，尤其是同时纳入性别、年龄与残疾状况三个交叉维度的分析需求，这突破了传统劳动统计仅关注宏观总体率的局限，有助于精准识别弱势青年群体在劳动力市场中的边缘化程度。构建过程中面临多重挑战：首先，亚洲各国劳动调查口径、问卷设计及数据收集频率差异显著，ILOSTAT虽采用国际劳工统计学家会议（ICLS）标准进行协调，但原始来源的异质性仍导致数据可比性受限，部分地区观测值被标记为“不可靠”；其次，残疾状况的定义在不同国家间存在非标准化的现象，数据集中明确列出的备注信息表明需处理定义偏差；此外，部分国家年度数据缺失严重，时间序列不连续，需在建模时合理处理缺失值以维持分析的有效性。

常用场景

经典使用场景

该数据集聚焦于亚洲23个国家1996年至2024年间青年劳动力参与率，按性别、年龄和残疾状况进行精细分层。作为国际劳工组织ILOSTAT官方统计数据的标准化重封装，它天然适用于时间序列预测与面板数据分析任务。研究者可借此探究亚洲青年就业趋势的演变规律，或构建跨国的多维度回归模型，剖析性别差异、年龄结构及残疾包容政策对劳动力市场参与度的交互影响。其结构化的分类变量（如sex、classif1、classif2）为因果推断与异质性分析提供了坚实的数据基础。

实际应用

在实际应用层面，该数据集为政策制定者与国际组织提供了精准的诊断工具。各国劳动部门可依据不同性别和残疾状况的青年参与率变化，动态调整职业培训计划与就业支持项目。非政府组织可借此识别特定国家或地区中残疾青年就业面临的系统性障碍，从而设计更具针对性的干预措施。金融与社会投资机构也能利用这些数据评估亚洲新兴市场的劳动力韧性与社会包容性风险，引导资本投向促进公平就业的领域。

衍生相关工作

围绕该数据集衍生出的工作多集中于计量方法创新与跨领域知识融合。学者们已基于其面板结构开发了处理多重分类误差和样本选择偏倚的统计模型，例如引入层次贝叶斯框架以增强稀疏年份国家的估计精度。另有工作将其与卫星夜间灯光数据或人力资本调查结合，构建了刻画亚洲青年就业质量的多源数据库。在政策评估领域，该数据集被用于双重差分法识别残疾就业配额制度在亚洲不同法域的实施效果，催生了关于制度移植适用性的比较研究。