electricsheepasia/asia-ilo-une-tune-sex-age-nb-unemployment-by-sex-and-age-thousands

--- 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 - unemployment - ilo - labour - employment pretty_name: "Unemployment by sex and age (thousands) | Asia (ILOSTAT)" --- # Unemployment by sex and age (thousands) | Asia (ILOSTAT) 🌏 **68,156 observations** · **48 Asia countries** · **1952–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **68,156 observations** of `Unemployment` data across **48 Asia countries**, spanning **1952–2025**, 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=UNE_TUNE_SEX_AGE_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_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 48 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `JPN` | 5,052 | 1953 | 2025 | | `SGP` | 3,554 | 1970 | 2024 | | `TWN` | 3,445 | 1978 | 2020 | | `IDN` | 3,152 | 1973 | 2023 | | `CYP` | 2,904 | 1974 | 2025 | | `THA` | 2,884 | 1971 | 2024 | | `ISR` | 2,855 | 1969 | 2024 | | `PAK` | 2,638 | 1969 | 2025 | | `PHL` | 2,570 | 1969 | 2023 | | `TUR` | 2,440 | 1982 | 2025 | | `MYS` | 2,410 | 1970 | 2022 | | `KOR` | 2,317 | 1969 | 2025 | | `GEO` | 2,275 | 1991 | 2024 | | `VNM` | 2,148 | 1996 | 2024 | | `PSE` | 2,018 | 2000 | 2025 | | ... | _33 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_AGE_NB` — Unemployment by sex and age (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_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex and age (thousands)` | | `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+` | | `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` | — | `C6:2300_C6:1058` | | `note_classif.label` | `string` | — | `Nonstandard age group: Including 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-une-tune-sex-age-nb-unemployment-by-sex-and-age-thousands") 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_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_AGE_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_AGE_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_une_tune_sex_age_nb_unemployment_by_sex_and_age_thousands_2025, title = {Unemployment by sex and age (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_AGE_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-une-tune-sex-age-nb-unemployment-by-sex-and-age-thousands}} } ``` ## 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_NB_This dataset contains 68,156 observations of unemployment data across 48 Asia countries, spanning from 1952 to 2025, with the indicator Unemployment by sex and age (thousands). The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), retrieved via REST API and filtered for Asian countries. It includes variables such as country code, year, sex disaggregation, age classification, observed values, and source metadata, suitable for tabular classification, regression, and time-series forecasting tasks.

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，通过其REST API接口直接提取指标UNE_TUNE_SEX_AGE_NB的原始记录，并依据ISO3国家代码筛选出亚洲区域数据。ILOSTAT依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查等微观数据进行调和处理，确保跨国可比性；源数据标签字段保留了原始调查来源信息，便于溯源与验证。

使用方法

通过HuggingFace datasets库的load_dataset函数即可加载数据并转换为Pandas DataFrame。用户可按国家代码筛选特定区域，按指标与时间排序绘制时间序列趋势图，或利用pivot_table将数据重塑为国家×年份矩阵。加载后可直接用于统计分析或机器学习建模，使用时需遵循CC-BY-4.0许可并同时引用ILO原始来源与Electric Sheep Asia的再包装工作。

背景与挑战

背景概述

国际劳工组织（ILO）自1919年成立以来，始终致力于全球劳动统计的标准化与传播，其核心数据库ILOSTAT汇集了200余个经济体的劳动力调查数据，成为劳动经济学与政策研究的权威基石。在此背景下，2025年由Electric Sheep Asia重新打包发布的亚洲失业数据集，覆盖48个亚洲国家、1952至2025年间68,156条观测记录，按性别与年龄维度细致刻画了失业规模。该数据集直面亚洲劳动力市场长期缺乏统一、可比的失业测度这一核心问题，为区域就业政策评估与SDG体面劳动目标监测提供了关键的量化支撑，其影响力已渗透至发展经济学、人口学及社会政策分析等多个领域。

当前挑战

该数据集所应对的领域问题在于亚洲各国劳动力调查方法、年龄分组与失业定义长期异质，导致跨国可比性严重不足。构建过程中，ILO虽以国际劳工统计学家会议（ICLS）标准进行调和，但原始数据仍面临来源多样、统计能力不均、观测状态标注复杂（如临时性、不可靠）等难题，且部分国家时间序列存在方法修订导致的结构性断点。此外，性别维度中仅少数国家报告SEX_O类别，年龄分类的非标准分组进一步加剧了数据稀疏性与建模难度，对时间序列预测与跨国面板分析构成实质性挑战。

常用场景

经典使用场景

在劳动经济学与人口统计学的研究中，该数据集最经典的使用场景在于构建亚洲地区失业率的性别与年龄别时间序列面板，用以刻画不同国家与地区失业结构的长期演变轨迹。研究者常以年度为观测单元，将48个亚洲经济体的失业人数按性别（男性、女性、总计）与年龄组（青年、成年）进行交叉分类，进而借助面板回归、序列分解或聚类分析等方法，揭示失业率在人口维度上的异质性特征。此类分析尤其适用于比较不同经济发展阶段下青年失业与女性失业的相对严重程度，并为跨国劳动力市场比较研究提供标准化、可复现的数据基础。

解决学术问题

该数据集所应对的核心学术问题在于跨国失业统计中定义不一致、年龄分组标准各异以及性别维度缺失所导致的比较困境。通过采用国际劳工组织基于国际劳工统计学家会议决议的统一口径，并借助ILOSTAT的‘最佳来源’筛选机制，数据集将不同国家的劳动力调查与行政记录进行了系统化调和，从而有效缓解了因统计标准差异而产生的测量偏误。其意义在于为失业率的性别差距与年龄梯度研究提供了具备跨时空可比性的量化依据，推动了关于亚洲劳动力市场结构性失衡的实证讨论，并为后续的因果推断与政策评估奠定了可信的数据根基。

实际应用

在实际应用层面，该数据集为政府部门、国际组织与智库机构提供了评估亚洲各国失业形势与设计劳动力市场干预措施的关键参考。劳工部门可利用分性别与分年龄的失业人数数据，精准识别青年群体或女性劳动者中失业风险较高的子群，从而优化职业培训、就业补贴与社会保障的资源配置。国际发展机构亦可借助该数据集监测可持续发展目标中关于充分生产性就业与体面工作的进展，并开展跨国减贫与就业促进项目的成效评估。此外，金融机构与咨询公司在进行国别风险评估与劳动力市场前景研判时，亦常将该数据作为基准输入之一。

数据集最近研究

最新研究方向

在全球劳动力市场结构性转型与后疫情时代复苏分化的背景下，该数据集为亚洲地区失业问题的性别与年龄异质性研究提供了长时段、跨国别的精细化观测基础。前沿研究日益聚焦于运用时间序列预测与面板回归方法，识别青年与女性群体失业的周期性敏感度及结构性驱动因素，并借助可解释机器学习模型揭示教育分类、非标准年龄组等分层变量对失业动态的调节效应。该数据集亦支撑ILO体面劳动议程下关于非正规就业冲击传导、人口转型与劳动参与率联动的实证检验。其跨1952至2025年的连续覆盖与来源可追溯性，为政策评估中的因果推断与跨国比较研究奠定了可靠的数据基石，对亚洲新兴经济体劳动力市场韧性评估具有重要参考价值。