electricsheepasia/asia-ilo-une-3eap-sex-age-dsb-rt-youth-unemployment-rate-by-sex-age-and-disability

--- 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 - unemployment - ilo - labour - employment pretty_name: "Youth unemployment rate by sex, age and disability status (%) | Asia (ILOSTAT)" --- # Youth unemployment rate by sex, age and disability status (%) | Asia (ILOSTAT) 🌏 **3,297 observations** · **23 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **3,297 observations** of `Unemployment` 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=UNE_3EAP_SEX_AGE_DSB_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Unemployment ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=UNE_3EAP_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 | |---------|-----:|-----------:|----------:| | `CYP` | 475 | 2005 | 2024 | | `MNG` | 464 | 2006 | 2024 | | `ARM` | 445 | 2007 | 2023 | | `KHM` | 272 | 1996 | 2023 | | `IDN` | 261 | 2010 | 2023 | | `PSE` | 185 | 2013 | 2022 | | `LKA` | 175 | 2018 | 2024 | | `BGD` | 129 | 2011 | 2024 | | `THA` | 126 | 2007 | 2019 | | `TLS` | 106 | 2015 | 2022 | | `IRQ` | 92 | 2007 | 2021 | | `AFG` | 85 | 2017 | 2021 | | `LAO` | 84 | 2015 | 2022 | | `PAK` | 68 | 2020 | 2021 | | `TUR` | 65 | 2000 | 2024 | | ... | _8 more countries_ | | | ## Indicators (sample) - `UNE_3EAP_SEX_AGE_DSB_RT` — Youth unemployment 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 | `UNE_3EAP_SEX_AGE_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Youth unemployment rate by sex, age a…` | | `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) | `7.902` | | `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`** (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-une-3eap-sex-age-dsb-rt-youth-unemployment-rate-by-sex-age-and-disability") 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"] == "UNE_3EAP_SEX_AGE_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_3EAP_SEX_AGE_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_3EAP_SEX_AGE_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_une_3eap_sex_age_dsb_rt_youth_unemployment_rate_by_sex_age_and_disability_2024, title = {Youth unemployment rate by sex, age and disability status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_3EAP_SEX_AGE_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-une-3eap-sex-age-dsb-rt-youth-unemployment-rate-by-sex-age-and-disability}} } ``` ## 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=UNE_3EAP_SEX_AGE_DSB_RT_This dataset contains 3,297 observations of unemployment data across 23 Asia countries, spanning 1996–2024, covering 1 distinct indicator: Youth unemployment rate by sex, age and disability status (%). It is sourced from the ILOSTAT database of the International Labour Organization (ILO), filtered to Asia ISO3 country codes, and includes dimensions such as country, source, sex, age, disability status, year, and observed values for labor market analysis.

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）的核心劳动统计数据库ILOSTAT，通过调用其REST API接口，直接提取了指标为UNE_3EAP_SEX_AGE_DSB_RT的原始数据。在获取数据后，依据亚洲ISO3国家代码进行地理筛选，最终汇集了23个亚洲经济体在1996年至2024年间的青年失业率观测值，共计3,297条记录。所有数据均经过ILO统计部门的统一协调处理，采用了国际劳工统计学家会议（ICLS）的定义标准，并对原始调查微观数据进行了标准化整合，原始来源在source.label字段中清晰标注，确保了数据的可追溯性和跨国的可比性。

特点

该数据集的独特之处在于其精细的多维度拆解能力，能够同时按性别、具体年龄组（如15-29岁青年区间）以及残疾状况对青年失业率进行分层统计。其字段设计涵盖了国家代码、来源标识、指标定义、时间序列、观测数值及状态标记，并包含了详细的分类注释（如方法修订或定义非标），便于用户理解数据背后的统计口径变化。数据集收录了来自不同国家、时间跨度近三十年的长序列数据，并以Parquet格式打包，具备了机器学习就绪（ML-ready）的特性，极大地方便了区域经济学、劳动市场不平等及社会政策领域的比较研究。

使用方法

用户可通过HuggingFace Datasets库直接调用数据集，执行`load_dataset`命令即可将数据加载为 DataFrame 格式，便于后续的探索性分析。典型的使用案例包括按国家代码过滤特定国家的青年失业状况，或利用`time`和`obs_value`字段绘制单指标的时间序列趋势图。对于跨国的面板数据分析，可按指标筛选后，以`time`为索引、`ref_area`为列名进行数据透视，构建出国家-年份矩阵，进而开展区域差异分析和计量建模。研究人员在使用时需留意`obs_status`字段标注的数据可靠性状态和注释中的方法变更说明，以确保分析结论的稳健性。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2024年通过其统计数据库ILOSTAT创建，并由Electric Sheep Asia团队重新打包发布，旨在系统性地提供亚洲地区按性别、年龄和残疾状态分层的青年失业率数据。涵盖1996至2024年间23个亚洲国家的3297条观测记录，数据集聚焦于联合国可持续发展目标中体面工作与经济增长的核心指标，填补了区域精细化劳动力统计数据的空白。通过标准化抽样与分类方法，该数据为研究青年就业不平等、残疾包容性政策及跨性别劳动参与差异提供了实证基础，推动了亚洲劳动力市场比较分析的量化研究进程。

当前挑战

该数据集面临的核心挑战在于解决劳动力统计中多维交叉分层（性别、年龄、残疾状态）导致的数据稀疏性与可比性问题。由于各国调查方法、残疾定义（如ICLS标准差异）及数据来源（劳动力调查、行政记录）存在非标准化分歧，观测值常带有‘不可靠’或‘方法修订’标记，影响时间序列的连贯性。同时，构建过程中需处理多源头数据的整合难题，例如调和不同调查框架下的‘最佳来源’选择、处理国家间年度频次不匹配，并保留元数据注释以确保数据质量的可追溯性，这对自动化数据管道提出了严格的清洗与对齐要求。

常用场景

经典使用场景

在劳动力市场分析与社会科学计量研究领域，该数据集作为一项横跨1996至2024年、涵盖23个亚洲国家、包含3297条观测的纵向面板数据，其最经典的用途在于对青年失业率进行跨性别、跨年龄组与残疾状态的精细化解构。研究者常借助其多维度分类变量（如性别、年龄区间与残疾状况），运用时间序列回归或分层线性模型，揭示亚洲地区青年就业脆弱性的结构性特征与演变趋势。该数据集尤为适合开展区域间比较研究，通过构建国家—年份面板，分析经济发展水平、教育政策与劳动力市场制度对特定青年亚群体失业率的差异化影响。

实际应用

在实际应用场景中，该数据集为亚洲各国政府、国际发展机构与非政府组织提供了量化决策依据，用于评估青年就业促进政策的覆盖效果与资源配置效率。例如，政策制定者可利用性别与残疾状态的分层失业率数据，精准定位亟需干预的弱势青年群体，并针对性地设计职业培训计划或创业扶持项目。此外，该数据集亦被广泛用于构建经济预测模型与可持续发展目标（SDG 8体面工作）的进展监测，国际劳工组织及相关研究机构可通过历年数据跟踪各成员国在减少青年失业方面的成效，并为区域劳动市场一体化政策提供实证支撑。

衍生相关工作

围绕该数据集衍生出一系列具有影响力的学术研究工作，涵盖青年就业决定因素分析、劳动力市场歧视量化评估及包容性就业政策效果评价等方向。基于该数据构建的计量模型，已有研究揭示了产业结构转型、教育扩张与最低工资制度对亚洲青年失业率的非线性影响。进一步地，部分学者通过将本数据与ILOSTAT其他指标（如非正规就业率、劳动参与率）进行关联分析，构建了多维青年就业脆弱性指数，推动了“脆弱就业”这一概念的操作化。此外，该数据集也为劳动经济学中传统的“技能缺口”与“岗位错配”理论提供了来自亚洲新兴经济体的实证证据，促进了相关理论模型在非西方语境下的验证与修正。