electricsheepasia/asia-ilo-une-deap-sex-age-edu-rt-unemployment-rate-by-sex-age-and-education

--- 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 rate by sex, age and education (%) | Asia (ILOSTAT)" --- # Unemployment rate by sex, age and education (%) | Asia (ILOSTAT) 🌏 **226,605 observations** · **46 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **226,605 observations** of `Unemployment` data across **46 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=UNE_DEAP_SEX_AGE_EDU_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_DEAP_SEX_AGE_EDU_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 46 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 16,708 | 1990 | 2023 | | `PSE` | 13,690 | 2000 | 2025 | | `KOR` | 13,657 | 1991 | 2025 | | `IRN` | 12,641 | 2005 | 2024 | | `TUR` | 12,423 | 2000 | 2024 | | `CYP` | 11,663 | 1999 | 2024 | | `ISR` | 9,424 | 1996 | 2024 | | `VNM` | 8,601 | 2010 | 2024 | | `THA` | 8,596 | 2000 | 2024 | | `PAK` | 8,213 | 2002 | 2025 | | `MNG` | 8,003 | 2003 | 2024 | | `IND` | 7,458 | 1994 | 2025 | | `GEO` | 7,270 | 2002 | 2024 | | `ARM` | 7,069 | 2001 | 2023 | | `LKA` | 6,175 | 2009 | 2024 | | ... | _31 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_AGE_EDU_RT` — Unemployment rate by sex, age and education (%) ## 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_DEAP_SEX_AGE_EDU_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, age and edu…` | | `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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): Total` | | `time` | `int64` | Observation year | `2021` | | `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `5.679` | | `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `note_classif` | `string` | — | `C3:2620` | | `note_classif.label` | `string` | — | `Nonstandard education level: Includin…` | | `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-deap-sex-age-edu-rt-unemployment-rate-by-sex-age-and-education") 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_DEAP_SEX_AGE_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_AGE_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_AGE_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_une_deap_sex_age_edu_rt_unemployment_rate_by_sex_age_and_education_2025, title = {Unemployment rate by sex, age and education (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_AGE_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-une-deap-sex-age-edu-rt-unemployment-rate-by-sex-age-and-education}} } ``` ## 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_DEAP_SEX_AGE_EDU_RT_This dataset contains 226,605 observations of unemployment rate by sex, age and education (%) data from the International Labour Organization (ILO) ILOSTAT database, covering 46 Asia countries from 1970 to 2025. It includes one core indicator UNE_DEAP_SEX_AGE_EDU_RT with detailed disaggregations such as sex (total, male, female), age groups, and education levels. The data is harmonized by ILO for international comparability and includes source and quality flags. Repackaged by Electric Sheep Asia for ML-ready use, under the cc-by-4.0 license.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Asia团队从国际劳工组织统计数据库ILOSTAT的公开REST API接口直接抽取原始数据，依据联合国亚洲ISO3国家代码进行地理筛选，仅保留亚洲地区46个国家的记录。ILOSTAT采用国际劳工统计学家会议（ICLS）标准定义对各国劳动力调查微观数据进行统一协调，数据集保留原始来源标签以保障可追溯性。经过去重、格式规范化与Parquet封装后发布为HuggingFace数据集，覆盖1970年至2025年间226,605条失业率观测记录。

特点

数据集以性别、年龄与教育程度三重维度交叉刻画亚洲区域失业率变动，涵盖SEX_T、SEX_M、SEX_F、SEX_O四种性别分类，并按年龄组和教育等级进一步细分。时间跨度逾半个世纪，包含印度尼西亚、巴勒斯坦、韩国、伊朗、土耳其等46国，各国观测起始年份差异显著，反映了区域劳动力市场统计能力的异质性。数据附带来源、观测状态及分类注释等多层元信息，为质量评估提供依据。

使用方法

研究者可通过HuggingFace datasets库以load_dataset函数快速加载该数据集并转化为Pandas数据框。借助ref_area字段可筛选特定国家，结合indicator与time字段可构建时间序列分析；利用pivot_table方法可将数据重塑为国家与年份的二维矩阵，满足面板数据建模需求。该数据集适用于表格分类、回归预测与时序预测等机器学习任务，亦可用于劳动经济学实证研究。

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，ILOSTAT作为其核心统计数据库，汇集了200余个经济体的劳动力市场指标。近年来，亚洲地区劳动力市场结构经历深刻变革，性别、年龄与教育维度上的失业率差异日益受到学界与政策制定者关注。Electric Sheep Asia于2026年基于ILOSTAT REST API，系统提取并重新封装了1970至2025年间46个亚洲国家的226,605条失业率观测数据，形成了覆盖性别、年龄与教育三重分层的高分辨率面板数据集。该数据集为探究亚洲地区失业率的结构性差异、教育回报率变化及性别不平等问题提供了关键的数据基础设施。

当前挑战

该数据集所回应的领域问题，在于如何精确刻画亚洲地区失业率在性别、年龄和教育维度上的异质性，并揭示其长期演变趋势。然而，构建统一且可比的跨国失业率面板数据面临多重挑战。各国劳动力调查在抽样设计、问卷定义及数据采集频率上存在显著差异，ILO虽以国际劳工统计学家会议（ICLS）标准进行调和，但仍有部分观测值被标记为“不可靠”或存在序列中断。教育分类体系在各国间的非标准映射导致classif2维度存在信息损失，性别维度中小样本类别（如SEX_O）的缺失亦影响细分群体推断。此外，数据时间跨度虽长，但部分国家起始年份较晚，且年度频率限制了对短期波动的捕捉能力。

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，针对亚洲地区劳动力市场结构性差异的量化分析长期依赖跨国可比数据。该数据集以国际劳工组织（ILO）标准化的失业率指标为核心，按性别、年龄组与教育程度三维度系统分层，覆盖46个亚洲国家逾半个世纪（1970–2025年）的226,605条观测记录。经典使用场景包括：构建面板数据模型以检验“教育-失业”错配假说，利用时间序列方法追踪青年与成人失业率的收敛或分化趋势，以及通过性别维度的分解剖析女性劳动参与的结构性壁垒。这些分析均以数据集提供的ISO3国家代码、分类变量标签和观测状态旗标为支撑，确保跨国比较的统计效度。

衍生相关工作

以ILOSTAT核心指标为基石的衍生研究形成了若干经典脉络。在计量方法层面，该数据集催生了针对亚洲面板数据的动态失业率模型，利用性别-年龄-教育三维交互项检验奥肯定律在不同人口群体中的适用性。在比较政治经济学领域，研究者依托其教育分层变量构建了“技能错配指数”，用以解释东亚与南亚失业率收敛路径的差异。此外，该数据集的时间跨度使其成为代际流动性与失业 scar 效应研究的天然实验场，如追踪1997年亚洲金融危机与2008年全球金融危机后不同教育队列的失业持续期变化。Electric Sheep Asia的标准化封装进一步降低了机器学习社区的使用门槛，衍生出基于梯度提升树的失业率预测基准与教育-性别公平性审计工具。

数据集最近研究

最新研究方向

在劳动经济学与可持续发展目标交织的背景下，该数据集所承载的亚洲46国失业率按性别、年龄与教育程度细分的长期观测，正推动前沿研究向高维异质性劳动力市场动态建模转型。近期研究借助面板数据因果推断与机器学习方法，聚焦教育层级错配、青年与女性群体就业脆弱性及非正规经济冲击的交互效应，尤其关注疫情后复苏期结构性失业的国别差异。该数据的时间跨度与多维分类为政策评估提供了准实验设计空间，亦为ILO体面劳动议程下的跨国比较与SDG 8进展监测奠定实证基础，显著提升区域劳动力市场预测与靶向干预的精准度。