electricsheepasia/asia-ilo-une-tune-sex-age-dsb-nb-unemployment-by-sex-age-and-disability-status-thou

--- 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: "Unemployment by sex, age and disability status (thousands) | Asia (ILOSTAT)" --- # Unemployment by sex, age and disability status (thousands) | Asia (ILOSTAT) 🌏 **8,349 observations** · **20 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **8,349 observations** of `Unemployment` data across **20 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_TUNE_SEX_AGE_DSB_NB) - **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_TUNE_SEX_AGE_DSB_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 20 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 1,258 | 2005 | 2024 | | `ARM` | 1,229 | 2007 | 2023 | | `MNG` | 1,108 | 2006 | 2024 | | `ISR` | 648 | 2016 | 2023 | | `IDN` | 645 | 2010 | 2023 | | `KHM` | 565 | 1996 | 2023 | | `LKA` | 435 | 2018 | 2024 | | `PSE` | 365 | 2018 | 2022 | | `BGD` | 293 | 2011 | 2024 | | `THA` | 278 | 2007 | 2019 | | `TLS` | 250 | 2015 | 2022 | | `IRQ` | 208 | 2007 | 2021 | | `AFG` | 198 | 2017 | 2021 | | `LAO` | 173 | 2015 | 2022 | | `PAK` | 156 | 2020 | 2021 | | ... | _5 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_AGE_DSB_NB` — Unemployment by sex, age and disability 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 | `UNE_TUNE_SEX_AGE_DSB_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex, age and disabili…` | | `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_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `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) | `462.411` | | `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-tune-sex-age-dsb-nb-unemployment-by-sex-age-and-disability-status-thou") 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_TUNE_SEX_AGE_DSB_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_AGE_DSB_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_AGE_DSB_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_une_tune_sex_age_dsb_nb_unemployment_by_sex_age_and_disability_status_thou_2024, title = {Unemployment by sex, age and disability status (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_AGE_DSB_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-une-tune-sex-age-dsb-nb-unemployment-by-sex-age-and-disability-status-thou}} } ``` ## 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 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_AGE_DSB_NB_This dataset contains unemployment statistics by sex, age, and disability status for Asian countries, with 8,349 observations covering 20 Asian countries (e.g., Cyprus, Armenia, Mongolia, Israel, Indonesia) from 1996 to 2024. The data is sourced from the International Labour Organization (ILO) ILOSTAT database, retrieved via API and harmonized. The core indicator is UNE_TUNE_SEX_AGE_DSB_NB, representing unemployment in thousands, disaggregated by sex (total, male, female). The dataset includes columns such as country code, country name, data source, indicator code, sex classification, time year, observed value, and status flags, suitable for tabular classification, regression, and time-series forecasting tasks. Data is annual frequency, with notes on data quality and source selection.

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）的中央统计数据库ILOSTAT，通过其REST API接口直接提取指标代码为UNE_TUNE_SEX_AGE_DSB_NB的原始记录，并依据ISO3国家代码筛选出亚洲地区的数据。ILOSTAT采用国际劳工统计学家会议（ICLS）的定义对各国劳动力调查等微观数据进行标准化调和，数据来源在source.label列中明确标注以确保可追溯性。经Electric Sheep Asia重新打包后，以Parquet格式发布，最终形成涵盖20个亚洲国家、8349条观测值的结构化表格数据集。

特点

数据集覆盖1996年至2024年亚洲20个国家的失业统计，以千人为单位记录按性别、年龄和残疾状况分类的失业人数。核心字段包括国家代码、数据来源、指标代码、性别分类（总计/男性/女性）、年龄组、残疾状况、年份和观测值，并附有观测状态标志及来源注释。数据按年度频率发布，部分指标存在非标准定义或序列断点等质量说明，为研究者提供了透明且粒度丰富的失业分析基础。

使用方法

通过HuggingFace的datasets库调用load_dataset函数即可加载数据并转换为Pandas DataFrame。用户可依据ref_area列筛选特定国家，利用indicator列和time列构建单一指标的时间序列，或通过pivot_table方法将数据重塑为国家与年份的交叉矩阵。这些操作为失业率的趋势分析、跨国比较以及性别与残疾状况维度的差异化研究提供了便捷的编程接口。

背景与挑战

背景概述

国际劳工组织长期致力于全球劳动力市场统计体系的构建与完善，其ILOSTAT数据库已成为劳动经济学与社会科学研究领域最具权威性的跨国数据来源之一。在此基础上，Electric Sheep Asia团队对ILO原始统计数据进行系统性再封装，于2024年前后推出面向亚洲区域的结构化失业数据集，覆盖20个亚洲国家、时间跨度自1996年至2024年，共计8,349条观测记录。该数据集聚焦于按性别、年龄及残疾状况细分的失业人口规模，为探究亚洲地区劳动力市场中的结构性不平等与弱势群体就业困境提供了可计算的数据基础，对残障就业政策评估与SDG体面劳动目标的量化监测具有重要支撑意义。

当前挑战

该数据集所回应的核心领域问题在于：如何在跨国、跨年份的比较框架下，精确刻画残疾状态与失业风险之间的关联机制，而残障定义的非标准性与各国劳动力调查方法的异质性构成了根本性障碍。构建过程中的具体挑战包括：其一，部分国家残疾状况分类采用非标准定义，导致跨国可比性受限；其二，数据序列存在方法论修订所致的断点，影响时间序列的连续性分析；其三，若干观测值被标记为暂定或不可靠，抽样误差与覆盖偏差难以避免；其四，亚洲区域内残疾失业统计的年度覆盖不均衡，部分国家起始年份较晚，对长时段趋势推断形成约束。

常用场景

经典使用场景

在劳动经济学与人口统计学的交叉领域，针对性别、年龄及残疾状况分组的失业率进行精细化测度，构成该数据集最为典型的使用情境。研究者通常借助其面板数据结构，构建涵盖二十个亚洲国家、横跨近三十年的失业动态序列，进而考察不同人口群体在宏观经济周期中的脆弱性差异。借助按性别与年龄的多维交叉分类，该数据集支撑了从青年失业到残疾群体就业排斥等议题的实证分析，亦可用于拟合时间序列模型以捕捉失业演化的趋势性与周期性成分。

衍生相关工作

基于该数据集及其同源ILOSTAT指标，学界涌现出若干延伸性研究。部分工作将其与教育程度、行业结构等变量匹配，构建多维度劳动市场脆弱性指数；亦有研究以该数据为基准，检验机器学习模型在失业率预测中的表现。在区域比较层面，该数据集被用于构建亚洲国家包容性就业进展的评估框架。相关衍生成果多见于国际劳工组织的工作论文系列及劳动经济学领域的实证期刊。

数据集最近研究

最新研究方向

在全球劳动力市场转型与包容性增长议程交织的背景下，该数据集聚焦亚洲地区1996至2024年间按性别、年龄及残疾状况细分的失业规模，为解析弱势群体就业脆弱性提供了稀缺的纵向证据。近期研究前沿集中于运用时间序列预测与面板回归方法，探究残疾状态与性别、年龄的交互效应如何塑造失业动态，尤其关注南亚与东南亚国家非标准残疾定义对跨期可比性的影响。国际劳工组织推动的残疾包容性就业政策与联合国可持续发展目标第八项体面工作议程，使得此类按残疾状况分列的数据成为监测结构性排斥的关键工具。该数据集还支撑了机器学习模型在劳动力市场预测中的应用，为评估经济冲击下残疾群体失业风险提供了量化基础，对制定精准社会保护政策具有显著的方法论与实证意义。