electricsheepasia/asia-ilo-pop-3wap-sex-age-lms-nb-youth-working-age-population-by-sex-age-and-labour

--- 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, age and labour market status (thousands) | Asia (ILOSTAT)" --- # Youth working-age population by sex, age and labour market status (thousands) | Asia (ILOSTAT) 🌏 **22,046 observations** · **38 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **22,046 observations** of `Population` 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=POP_3WAP_SEX_AGE_LMS_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_AGE_LMS_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 38 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 1,584 | 1990 | 2023 | | `KOR` | 1,248 | 2000 | 2025 | | `CYP` | 1,243 | 1999 | 2024 | | `PSE` | 1,200 | 2000 | 2025 | | `TUR` | 1,200 | 2000 | 2024 | | `JPN` | 1,152 | 2000 | 2023 | | `PHL` | 1,056 | 2001 | 2023 | | `KHM` | 972 | 1996 | 2023 | | `IRN` | 960 | 2005 | 2024 | | `MNG` | 936 | 2000 | 2024 | | `ARM` | 864 | 2001 | 2023 | | `VNM` | 816 | 2007 | 2024 | | `PAK` | 815 | 2005 | 2025 | | `GEO` | 768 | 2009 | 2024 | | `KGZ` | 720 | 2009 | 2023 | | ... | _23 more countries_ | | | ## Indicators (sample) - `POP_3WAP_SEX_AGE_LMS_NB` — Youth working-age population by sex, age and labour market 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 | `POP_3WAP_SEX_AGE_LMS_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.) | `AGE_YTHBANDS_Y15-29` | | `classif1.label` | `string` | — | `Age (Youth bands): 15-29` | | `classif2` | `string` | Second classification variable where applicable | `LMS_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Labour market status: Total` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `8026.833` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C6:1042` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding ages…` | | `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-pop-3wap-sex-age-lms-nb-youth-working-age-population-by-sex-age-and-labour") 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_AGE_LMS_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="POP_3WAP_SEX_AGE_LMS_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "POP_3WAP_SEX_AGE_LMS_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_pop_3wap_sex_age_lms_nb_youth_working_age_population_by_sex_age_and_labour_2025, title = {Youth working-age population by sex, age and labour market status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=POP_3WAP_SEX_AGE_LMS_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-pop-3wap-sex-age-lms-nb-youth-working-age-population-by-sex-age-and-labour}} } ``` ## 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_AGE_LMS_NB_This dataset contains Youth working-age population by sex, age and labour market status (thousands) | Asia (ILOSTAT) data from the International Labour Organizations ILOSTAT database. It includes 22,046 observations across 38 Asia countries spanning 1970–2025. The core indicator is POP_3WAP_SEX_AGE_LMS_NB - Youth working-age population by sex, age and labour market status (thousands). Data is disaggregated by country, year, sex (total, male, female, other), age groups (youth bands 15-29), and labour market status. The dataset provides source information, observation status flags, and methodological notes, suitable for labour market analysis, demographic research, and economic forecasting. Repackaged by Electric Sheep Asia in a standardized format for machine learning readiness.

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）的核心统计数据库ILOSTAT，聚焦于亚洲地区青年劳动年龄人口的性别、年龄及劳动力市场状态分布。在构建过程中，数据通过ILOSTAT REST API接口直接提取，并依据亚洲ISO3国家代码进行地域筛选。ILOSTAT依据国际劳工统计学家会议（ICLS）的定义，对原始调查微观数据进行统一协调处理，确保了跨国家、跨年份数据的高度可比性。最终数据集以Parquet格式封装，包含22,046条观测记录，覆盖1970年至2025年间38个亚洲国家的单一核心指标，所有来源信息均在“source.label”列中清晰标注，便于追溯与验证。

使用方法

借助HuggingFace的datasets库，用户可通过load_dataset()函数便捷地加载该数据集，并将其快速转换为pandas DataFrame进行后续分析。典型应用包括按国家筛选进行国别研究，或基于时间列排序生成特定指标的连续时间序列。用户还可通过pivot_table方法构建国家×年份的透视矩阵，便于进行跨区域比较或面板数据分析。数据加载后，可利用obs_value字段进行统计建模或趋势预测，丰富的分类列（sex, classif1, classif2）则支持多维度分组分析，适配分类、回归及时间序列预测等多种下游任务场景。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）的统计数据库ILOSTAT于2025年创建，经Electric Sheep Asia重新封装后发布于HuggingFace平台。核心研究问题聚焦于亚洲地区青年劳动年龄人口按性别、年龄及劳动力市场状态的分布特征，旨在为区域劳动力供需分析、青年就业政策评估及可持续发展目标（SDGs）中的体面劳动指标提供结构化数据支持。数据集涵盖1970年至2025年间38个亚洲国家的22,046条观测记录，其权威性来源于ILO对各国劳动力调查、家庭收支调查等微观数据的统一协调与标准化处理。作为连接原始宏观统计与机器学习应用的桥梁，该数据集在亚洲劳动经济学、人口迁移研究及跨国比较分析领域具有重要影响力，尤其为长期时间序列建模和跨国面板数据分析提供了稀缺的高质量基础资源。

当前挑战

该数据集面临的核心领域挑战在于亚洲地区劳动力市场的高度异质性，包括各国统计口径差异（如青年年龄定义从15-24岁至15-29岁不等）、非正规就业的普遍存在导致官方调查漏报，以及年度时序数据无法捕捉季节性劳动力波动。在构建过程中，主要挑战源自ILOSTAT多源数据融合的复杂性：不同国家来源（如劳动力调查、行政记录）的统计方法随时间变更，需通过元数据（如break in series标记）识别序列断裂；性别与年龄分组的不完全覆盖（如classif1、classif2字段存在空值）限制了多维交叉分析；此外，观测状态标记（如不可靠值U）的解读与处理需领域知识，增加了数据清洗和建模的不确定性。

常用场景

经典使用场景

该数据集聚焦于亚洲38个国家1970年至2025年间青年劳动适龄人口按性别、年龄及劳动力市场状态的分布情况，包含超过2.2万条观测记录。研究者可借助其丰富的分类维度（如性别、年龄段、劳动力状态）进行面板数据建模，通过时间序列分析揭示亚洲青年劳动力供给的长期演变趋势，或运用分类与回归任务探究人口结构变迁与劳动参与率之间的内在关联。其标准化的ILOSTAT元数据字段（如来源标识、观测状态标记）为跨国的比较研究与数据质量评估提供了坚实基座。

解决学术问题

该数据集有效回应了劳动经济学与发展经济学中关于青年就业脆弱性的经典议题。它使得学者能够量化分析亚洲不同发展水平国家中，性别与年龄结构如何塑造青年群体的就业、失业与非经济活动状态差异，从而破解劳动力市场分割的微观机制。通过整合长期面板数据，研究者得以检验经济周期、教育扩张和社会保障政策对青年劳动参与率的异质性影响，为国际劳工组织倡导的体面劳动目标提供实证支撑，推动了针对亚洲青年群体就业质量与包容性增长的政策评估研究。

实际应用

在政策制定与国际发展援助领域，该数据集已被广泛应用于评估亚洲各国青年就业促进项目的成效。国际组织如ILO、联合国开发计划署及亚洲开发银行可据此监测青年失业率的动态变化，识别需要优先干预的国家与地区。劳动经济学家利用其构建预测模型，模拟人口老龄化与数字化转型叠加情境下青年劳动力市场的演变路径，为职业培训资源配置与最低工资制度设计提供数据驱动的决策依据。此外，非政府组织也能通过性别维度的细化数据，精准瞄准女性青年群体面临的就业壁垒，开展针对性帮扶。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲地区青年劳动适龄人口的性别、年龄与劳动力市场状态细分统计，为劳动经济学与人口学研究提供了高时间分辨率（1970–2025年）的长序列面板数据。在可持续发展目标（SDG）第八项体面工作与经济增长的全球议程下，该数据被广泛用于构建青年失业率预测模型、评估性别工资差距演变趋势，以及分析区域劳动力流动性对就业结构的影响。结合国际劳工组织（ILO）最新发布的《世界就业与社会展望》报告，研究者利用此类微观分层统计，可精准识别亚洲新兴经济体在数字化转型中的青年劳动参与模式变化，为制定包容性就业政策提供实证基础。其跨38国的可比性设计，亦推动了跨国劳动动态的比较计量研究，尤其在衡量非正式就业与性别不平等交互效应方面具有独特价值。