electricsheepasia/asia-ilo-une-deap-sex-age-mts-rt-unemployment-rate-by-sex-age-and-marital-status

--- 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 marital status (%) | Asia (ILOSTAT)" --- # Unemployment rate by sex, age and marital status (%) | Asia (ILOSTAT) 🌏 **124,058 observations** · **36 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **124,058 observations** of `Unemployment` data across **36 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_MTS_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_MTS_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 36 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 9,457 | 1996 | 2023 | | `KOR` | 9,050 | 2000 | 2025 | | `TUR` | 8,806 | 2000 | 2024 | | `PHL` | 7,940 | 2001 | 2023 | | `IRN` | 6,721 | 2005 | 2024 | | `CYP` | 6,155 | 1999 | 2020 | | `ARM` | 6,048 | 2001 | 2023 | | `VNM` | 5,239 | 2010 | 2024 | | `MNG` | 5,003 | 2009 | 2024 | | `THA` | 4,980 | 2000 | 2024 | | `ISR` | 4,648 | 2012 | 2024 | | `PAK` | 4,547 | 2005 | 2025 | | `JPN` | 4,481 | 2000 | 2023 | | `IND` | 4,321 | 1994 | 2025 | | `LKA` | 3,932 | 2010 | 2024 | | ... | _21 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_AGE_MTS_RT` — Unemployment rate by sex, age and marital 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_DEAP_SEX_AGE_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex, age and mar…` | | `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 | `MTS_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Marital status (Aggregate): 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` | — | `—` | | `note_classif.label` | `string` | — | `—` | | `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-mts-rt-unemployment-rate-by-sex-age-and-marital-status") 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_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_AGE_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_AGE_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_une_deap_sex_age_mts_rt_unemployment_rate_by_sex_age_and_marital_status_2025, title = {Unemployment rate by sex, age and marital status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_AGE_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-une-deap-sex-age-mts-rt-unemployment-rate-by-sex-age-and-marital-status}} } ``` ## 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_MTS_RT_This dataset contains unemployment rate statistics for Asian countries from the International Labour Organization (ILO) ILOSTAT database, specifically the indicator for unemployment rate by sex, age and marital status (%). It covers 36 Asian countries, spans the years 1970 to 2025, and includes 124,058 observations. 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 provides a detailed schema with columns such as country code, country name, data source, indicator code, sex classification, age classification, marital status classification, observation year, observed value, observation status flags, etc., supporting tasks like tabular classification, tabular regression, and time-series forecasting. The data is published at annual frequency and includes caveats on data quality, such as the use of ILO-selected best source and handling of missing values.

搜集汇总

数据集介绍

构建方式

本数据集源于国际劳工组织（ILO）的ILOSTAT数据库，由Electric Sheep Asia通过ILOSTAT REST API接口获取指标UNE_DEAP_SEX_AGE_MTS_RT的原始数据，并筛选亚洲ISO3国家代码进行重新打包。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标准化调和，数据来源在source.label列中标注以确保可追溯。最终形成覆盖36个亚洲国家、1970至2025年、共计124,058条观测的表格化数据集。

使用方法

研究者可通过HuggingFace的datasets库使用load_dataset函数加载数据集，并转换为Pandas数据框进行灵活操作。典型用法包括按国家代码筛选特定国家的失业率序列、针对单一指标提取时间序列并绘制趋势图，或利用透视表生成国家与年份的矩阵视图。数据中的分类变量和观测值可直接用于统计建模与机器学习任务，引用时需同时注明ILO原始来源与Electric Sheep Asia的重新打包工作。

背景与挑战

背景概述

国际劳工组织（ILO）长期致力于全球劳动力市场统计的标准化与传播，ILOSTAT作为其核心统计数据库，汇集了200余个经济体的就业、失业及体面劳动指标。此数据集由Electric Sheep Asia于2025年重新封装，源自ILOSTAT的REST API，聚焦亚洲36个国家1970至2025年间的失业率，按性别、年龄与婚姻状况细分，共计124,058条观测。作为亚洲劳动力市场微观分析的精细化数据资源，该数据集为探究失业率的性别差异、年龄梯度及婚姻状态效应提供了跨国长时序的实证基础，对劳动经济学、社会政策评估及可持续发展目标监测具有重要参考价值。

当前挑战

该数据集所应对的核心领域问题在于多维失业率测度的跨国可比性与时序一致性。构建过程中面临多重挑战：其一，亚洲各国劳动力调查方法、抽样框架及统计能力差异显著，ILO虽依据国际劳工统计学家会议（ICLS）定义进行协调，但源数据仍存在断点、修订及不可靠标记；其二，性别、年龄与婚姻状况的三维交叉分类导致部分国家或年份数据稀疏甚至缺失，需依赖模型估算或选择性发布，削弱了直接观测的完整性；其三，非标准就业形态及婚姻状态分类的国别差异，进一步增加了指标跨年、跨国比较的复杂性。

常用场景

经典使用场景

在劳动经济学与人口统计学的实证研究中，该数据集最经典的用途在于构建跨国家、跨时期的面板数据模型，以探究性别、年龄组与婚姻状况如何交织影响失业风险。研究者常利用其124,058条观测记录，采用双向固定效应或分组回归方法，识别不同婚姻状态下青年与成年劳动力市场表现的异质性。例如，通过对比已婚与未婚女性在亚洲各国的失业率演变轨迹，可揭示家庭角色分工对女性劳动参与的抑制或促进效应，为劳动供给理论提供跨国证据。

解决学术问题

该数据集有效回应了劳动经济学中关于失业结构性差异的长期争论，尤其是婚姻状况作为失业风险调节变量的作用机制。过往研究多局限于单一国家或发达经济体，难以检验制度与文化背景的调节效应。本数据集覆盖36个亚洲国家逾半个世纪的数据，使学者能够系统分析婚姻状况与性别、年龄的交互项对失业率的边际影响，从而厘清家庭结构变迁、性别规范与劳动力市场分割之间的因果路径，为比较政治经济学与家庭经济学交叉领域提供坚实的经验基础。

实际应用

在政策制定与劳动力市场监测层面，该数据集为亚洲各国政府及国际组织提供了精准施策的依据。就业部门可借助其细分至性别、年龄与婚姻状况的失业率时序数据，识别高风险群体并设计靶向就业援助计划。例如，针对未婚青年或离异女性失业率异常攀升的国家，可据此调整职业培训与 childcare 补贴政策。国际劳工组织亦可将此数据用于国别就业政策审议与可持续发展目标中体面工作指标的进展评估，提升政策干预的时效性与针对性。

数据集最近研究

最新研究方向

在劳动经济学与性别研究的交汇处，该数据集正推动着婚姻状况作为失业异质性调节变量的前沿探索。研究者日益关注亚洲地区婚姻状态（未婚、已婚、离异、丧偶）与性别、年龄的交互效应如何重塑失业风险的结构性差异，尤其在经济转型与家庭模式变迁背景下，已婚女性与青年单身群体的失业脆弱性成为热点议题。数据覆盖1970至2025年36个亚洲国家，为跨国比较与时间序列预测提供了独特资源，有助于揭示文化规范、劳动力市场制度对失业性别差距的长期影响。此类研究对完善社会保障政策、促进包容性就业具有显著意义，尤其在应对青年失业与性别不平等这类全球性挑战时，该数据集的精细分类能力为实证检验提供了关键支撑。