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

--- 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 rate by sex and marital status (%) | Asia (ILOSTAT)" --- # Unemployment rate by sex and marital status (%) | Asia (ILOSTAT) 🌏 **9,405 observations** · **36 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **9,405 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_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_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` | 672 | 1996 | 2023 | | `KOR` | 615 | 2000 | 2025 | | `TUR` | 611 | 2000 | 2024 | | `PHL` | 568 | 2001 | 2023 | | `CYP` | 497 | 1999 | 2020 | | `IRN` | 480 | 2005 | 2024 | | `ARM` | 458 | 2001 | 2023 | | `MNG` | 427 | 2009 | 2024 | | `THA` | 384 | 2000 | 2024 | | `VNM` | 382 | 2010 | 2024 | | `PAK` | 362 | 2005 | 2025 | | `KHM` | 348 | 1996 | 2023 | | `IND` | 315 | 1994 | 2025 | | `LKA` | 315 | 2010 | 2024 | | `ISR` | 312 | 2012 | 2024 | | ... | _21 more countries_ | | | ## Indicators (sample) - `UNE_DEAP_SEX_MTS_RT` — Unemployment rate by sex 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_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Unemployment rate by sex and marital …` | | `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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.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_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-mts-rt-unemployment-rate-by-sex-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_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="UNE_DEAP_SEX_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "UNE_DEAP_SEX_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_une_deap_sex_mts_rt_unemployment_rate_by_sex_and_marital_status_2025, title = {Unemployment rate by sex and marital status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=UNE_DEAP_SEX_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-une-deap-sex-mts-rt-unemployment-rate-by-sex-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_MTS_RT_This dataset contains 9,405 observations of the Unemployment rate by sex and marital status (%) indicator across 36 Asia countries, spanning from 1970 to 2025. The data is sourced from the International Labour Organizations (ILO) ILOSTAT statistical database, retrieved via API and harmonized, covering disaggregations by sex (total, male, female, other) and marital status (total). It is presented in tabular format with columns including country code, year, observed value, data source, indicator code, sex classification, observation status, and more, suitable for tasks such as tabular classification, regression, and time-series forecasting. The dataset is repackaged by Electric Sheep Asia in Parquet format for machine learning readiness.

搜集汇总

数据集介绍

构建方式

国际劳工组织统计数据库（ILOSTAT）作为全球劳工统计的权威来源，依托各国劳动力调查、家庭收入调查及行政记录等多元数据源，经国际劳工统计学家会议（ICLS）定义体系进行跨国标准化调和。该数据集由Electric Sheep Asia团队自ILOSTAT REST API接口（id=UNE_DEAP_SEX_MTS_RT）直接提取原始记录，以亚洲ISO3国家代码为筛选条件，保留国际劳工组织甄选的最佳来源数据，并经模式规范化后以Parquet格式重新封装发布，覆盖1970至2025年间36个亚洲国家的9405条观测记录。

使用方法

研究者可通过HuggingFace datasets库以一行代码加载该数据集，利用pandas转换后按国家、指标或性别等字段进行筛选与聚合。典型应用包括：提取单一国家的失业率时间序列进行趋势分析，按指标代码筛选后绘制年度变化曲线，或通过透视表将数据重塑为国家与年份的矩阵形式，以支撑面板数据分析、跨国比较研究及机器学习建模等任务。数据集兼容表格分类、表格回归与时间序列预测等任务类型，适合经济学、社会学与公共政策领域的定量研究。

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可比性。其核心数据库ILOSTAT汇集了200余个经济体的劳动力调查数据，为监测就业与失业趋势提供了权威基准。在此背景下，Electric Sheep Asia于2025年对该数据库进行重新封装，构建了涵盖36个亚洲国家、1970至2025年间9,405条观测值的失业率数据集，按性别与婚姻状况细分。该数据集旨在回应亚洲劳动力市场中性别与婚姻状态如何交互影响失业风险这一核心问题，为劳动经济学、社会政策评估及机器学习建模提供了细粒度、长时序的跨国比较基础。

当前挑战

该数据集所应对的领域问题在于失业率预测与异质性分析：传统模型难以捕捉性别与婚姻状况交互下的非线性失业动态，尤其在经济波动期。构建过程中，数据调和面临显著挑战。各国劳动力调查的抽样设计、失业定义及婚姻状况分类存在系统性差异，ILO虽以国际劳工统计学家会议标准进行回溯调整，但序列中断、观测值可靠性标记及缺失分类维度仍普遍存在。此外，部分国家数据年度跨度不均衡，早期年份覆盖稀疏，且性别与婚姻状况的交叉细分在多数国家并未持续发布，限制了面板分析中固定效应模型的稳健估计。

常用场景

经典使用场景

在劳动经济学与性别研究的交叉领域，该数据集最为经典的应用在于刻画亚洲各国失业率在性别与婚姻状态维度上的分布特征与演化轨迹。研究者通常以国家为截面单位，以年份为时间轴，构建性别—婚姻状态的分组时间序列，进而比较已婚与未婚男性、女性在失业风险上的结构性差异。借助面板数据建模或分组趋势分析，可揭示婚姻状态对劳动力市场表现的调节效应，为理解亚洲地区性别不平等与家庭结构变迁提供定量基础。

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率性别差异形成机制的长期争论，尤其是婚姻状态如何与性别交互影响就业稳定性这一悬而未决的问题。通过提供1970至2025年间36个亚洲国家的标准化观测值，它使跨国比较研究与长时段趋势分析成为可能，弥补了以往研究多聚焦欧美、缺乏亚洲系统证据的不足。其数据溯源标注与质量标志亦有助于研究者评估测量误差，提升实证结论的稳健性，对推动区域劳动市场研究具有基础性意义。

实际应用

在政策实践层面，该数据集可服务于亚洲各国劳动与社会保障部门的失业监测与精准施策。通过识别已婚女性、未婚青年等特定群体的失业高发态势，政策制定者能够更有针对性地设计就业促进计划、家庭友好型劳动政策与社会保护措施。国际组织亦可借助该数据评估可持续发展目标中体面劳动指标的实现进度，为区域劳动力市场治理提供可比、可追踪的量化依据。

数据集最近研究

最新研究方向

在全球劳动力市场性别差距与家庭结构变迁的交叉议题下，该数据集成为探究婚姻状况如何塑造亚洲失业性别差异的关键实证基础。近期研究依托其1970至2025年长时序、36国覆盖以及性别与婚姻状态双重分解的粒度，结合时间序列预测与面板因果推断，揭示已婚与未婚群体在失业风险上的异质性响应，尤其关注经济周期、女性劳动参与率上升及婚姻模式转型的调节效应。该数据支撑了ILO体面劳动议程与联合国可持续发展目标8的监测评估，为亚洲各国制定精准就业政策、缓解脆弱群体失业冲击提供了可复用的量化依据。