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

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - asia - ilostat - unemployment - ilo - labour - employment pretty_name: "Unemployment by sex, age and duration (thousands) | Asia (ILOSTAT)" --- # Unemployment by sex, age and duration (thousands) | Asia (ILOSTAT) 🌏 **163,920 observations** · **40 Asia countries** · **1990–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **163,920 observations** of `Unemployment` data across **40 Asia countries**, spanning **1990–2025**, 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_DUR_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_DUR_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 40 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 16,724 | 1990 | 2023 | | `TUR` | 14,491 | 2000 | 2024 | | `CYP` | 12,507 | 1999 | 2024 | | `PSE` | 11,298 | 2000 | 2025 | | `PHL` | 9,264 | 2001 | 2023 | | `JPN` | 8,352 | 2002 | 2017 | | `ISR` | 8,071 | 2012 | 2024 | | `KGZ` | 6,277 | 2009 | 2023 | | `VNM` | 6,015 | 2011 | 2024 | | `GEO` | 5,860 | 2011 | 2024 | | `MNG` | 5,186 | 2003 | 2024 | | `KOR` | 4,978 | 2000 | 2025 | | `ARM` | 4,911 | 2007 | 2023 | | `PAK` | 4,711 | 1993 | 2025 | | `AZE` | 4,240 | 2008 | 2022 | | ... | _25 more countries_ | | | ## Indicators (sample) - `UNE_TUNE_SEX_AGE_DUR_NB` — Unemployment by sex, age and duration (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_DUR_NB` | | `indicator.label` | `string` | Indicator name in English | `Unemployment by sex, age and duration…` | | `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 | `DUR_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Duration (Aggregate): 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` | — | `C7:2938` | | `note_classif.label` | `string` | — | `Nonstandard duration of unemployment:…` | | `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-dur-nb-unemployment-by-sex-age-and-duration-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_DUR_NB"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_TUNE_SEX_AGE_DUR_NB") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_TUNE_SEX_AGE_DUR_NB"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_une_tune_sex_age_dur_nb_unemployment_by_sex_age_and_duration_thousands_2025, title = {Unemployment by sex, age and duration (thousands) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_TUNE_SEX_AGE_DUR_NB}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-une-tune-sex-age-dur-nb-unemployment-by-sex-age-and-duration-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_DUR_NB_This dataset contains unemployment statistics from the International Labour Organization (ILO) ILOSTAT database, specifically focused on Asia. It includes 163,920 observations across 40 Asian countries spanning from 1990 to 2025, centered on one key indicator: unemployment by sex, age, and duration (in thousands). The data is sourced via the ILOSTAT REST API and filtered to Asian countries, harmonized using International Conference of Labour Statisticians (ICLS) definitions. The dataset features a detailed schema with columns such as country code, country name, data source, indicator code, sex disaggregation (total, male, female), age classification, time year, observed value, and status flags. Data is annual in frequency and uses the ILO-selected best source for quality assurance. It is suitable for tasks like tabular classification, regression, and time-series forecasting, designed to provide machine learning-ready labor market data for Asia.

搜集汇总

数据集介绍

构建方式

在全球劳动力市场监测与比较研究领域，国际劳工组织（ILO）的ILOSTAT数据库以其标准化与权威性著称。本数据集经由Electric Sheep Asia团队从ILOSTAT的REST API接口直接提取原始数据，并依据亚洲ISO3国家代码进行系统筛选与整合。其数据源自各国劳动力调查、家庭收入调查及行政记录，ILO统计部门采用国际劳工统计学家会议（ICLS）标准定义对原始微观数据进行统一调和，并在source.label列中标注来源信息以确保可追溯性。最终形成涵盖40个亚洲国家、163,920条观测值的结构化表格数据，时间跨度为1990年至2025年。

特点

该数据集以失业人口按性别、年龄与失业持续时间分类的统计指标为核心，提供高维度的劳动力市场刻画。其显著特征在于多维度分解能力，包括性别（总数、男性、女性）与两级分类变量（如年龄组与持续时间类型），同时附带完整的元数据列以标注来源、观察状态与分类注释。数据以年度频率发布，覆盖东亚、南亚、西亚等区域的主要经济体，空间与时间跨度兼具广度与深度。所有字段均经标准化编码，便于跨国比较与纵向分析，且采用CC-BY-4.0许可，兼顾开放性与合规性。

使用方法

研究人员可借助HuggingFace的datasets库以单行代码加载数据，并转换为Pandas数据框进行灵活操作。典型应用包括：筛选特定国家（如ref_area == 'IDN'）以开展国别失业结构分析；提取单一指标序列并绘制时间趋势图，观察失业持续时间的演变；或通过透视表将数据重塑为国家×年份矩阵，以支持面板回归或时间序列预测建模。该数据集亦适用于表格分类、回归及多变量可视化任务，为劳动经济学、社会政策评估及区域发展研究提供实证基础。

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汇集了涵盖就业、失业、工资等多维指标，成为劳动经济学与政策评估的权威基石。该数据集由Electric Sheep Asia于2025年重新封装发布，覆盖1990至2025年间40个亚洲国家的16万余条失业观测记录，聚焦于按性别、年龄和失业持续时间分类的失业人口（千人）。其核心研究问题在于揭示亚洲地区劳动力市场中失业结构的异质性及其时序演变，为比较劳动制度、评估青年与性别失业差异提供高粒度面板数据。该数据集对监测可持续发展目标中的体面工作议程、支撑跨国实证研究具有显著影响力。

当前挑战

本数据集所回应的领域问题在于传统失业统计往往缺乏按年龄与持续时间交叉分类的细粒度、长时间跨度且跨国可比的数据，致使失业动态的异质性分析受限。构建过程中的挑战尤为突出：各国劳动力调查的抽样设计、失业定义及年龄分组标准存在系统性差异，ILO虽依据国际劳工统计学家会议决议进行调和，但仍需处理来源断裂、方法修订及不可靠观测值等遗留问题；此外，部分国家数据年份不连续、分类维度非全覆盖，导致面板不平衡，对时序建模和缺失值插补构成方法学考验。

常用场景

经典使用场景

在劳动经济学与人口统计学领域，针对亚洲地区失业问题的量化分析长期依赖跨国可比的面板数据。该数据集凭借其涵盖40个亚洲国家、跨越1990至2025年、共计163,920条观测记录的结构化优势，成为探究失业率性别差异、年龄梯度及失业持续时间交互效应的经典数据源。研究者通常以国家为截面单元，以年份为时间维度，构建多维失业指标矩阵，进而运用面板回归、时间序列预测或聚类分析等方法，刻画亚洲劳动力市场失业动态的异质性与收敛性。

衍生相关工作

围绕该数据集已衍生出一系列具有影响力的后续研究。若干比较劳动经济学文献利用其长时序特性，构建亚洲失业率的动态因子模型，以分离区域共同冲击与国别特有冲击；部分研究将其与ILO其他指标数据集链接，探讨失业持续时间与工资水平、非正规就业之间的联动机制。此外，该数据集的结构化格式亦激发了机器学习社区在表格数据分类与回归任务上的基准测试，推动了劳动统计数据的自动化建模与因果推断方法的发展。

数据集最近研究

最新研究方向

在全球劳动力市场结构性变革与后疫情时代复苏分化的背景下，亚洲地区失业问题的异质性特征日益成为劳动经济学与区域科学交叉领域的研究焦点。该数据集凭借其涵盖40个亚洲国家、跨越1990至2025年的16万余条性别—年龄—失业持续时间三维细化观测，为前沿研究提供了独特支撑。当前研究前沿主要聚焦于：基于高频面板数据的失业持续期非线性动态建模与结构断点识别，以捕捉经济周期与制度变迁对青年及女性群体的非对称冲击；结合机器学习方法开展跨国别失业风险异质性分类与时空预测，尤其在ILO统计框架下探索非标准失业测度的可比性；以及围绕可持续发展目标（SDG 8）中体面工作议程，评估亚洲各国劳动力市场政策对缩短失业持续期的因果效应。该数据集在推动性别敏感型就业政策模拟、脆弱群体精准识别及区域一体化劳动力市场监测等方面具有显著的方法论价值与政策意义。