electricsheepasia/asia-ilo-eip-dwap-sex-edu-mts-rt-inactivity-rate-by-sex-education-and-marital-statu

--- 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 - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Inactivity rate by sex, education and marital status (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex, education and marital status (%) | Asia (ILOSTAT) 🌏 **119,391 observations** · **35 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **119,391 observations** of `Other measures of labour underutilization` data across **35 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=EIP_DWAP_SEX_EDU_MTS_RT) - **Publisher:** International Labour Organization (ILO) - **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/) - **Topic:** Other measures of labour underutilization ## Methodology Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EIP_DWAP_SEX_EDU_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 35 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 8,336 | 1996 | 2023 | | `IRN` | 8,049 | 2005 | 2024 | | `KOR` | 7,806 | 2000 | 2025 | | `CYP` | 6,563 | 1999 | 2020 | | `THA` | 6,274 | 2000 | 2024 | | `VNM` | 6,027 | 2010 | 2024 | | `MNG` | 5,793 | 2009 | 2024 | | `KHM` | 5,633 | 1996 | 2023 | | `PAK` | 5,549 | 2005 | 2025 | | `ARM` | 5,392 | 2001 | 2023 | | `ISR` | 4,926 | 2012 | 2024 | | `IND` | 4,848 | 1994 | 2025 | | `LKA` | 4,503 | 2010 | 2024 | | `TUR` | 3,915 | 2000 | 2024 | | `PSE` | 3,670 | 2000 | 2025 | | ... | _20 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_EDU_MTS_RT` — Inactivity rate by sex, education 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 | `EIP_DWAP_SEX_EDU_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex, education and…` | | `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.) | `EDU_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Education (Aggregate levels): Total` | | `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) | `50.27` | | `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-eip-dwap-sex-edu-mts-rt-inactivity-rate-by-sex-education-and-marital-statu") 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"] == "EIP_DWAP_SEX_EDU_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_EDU_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_EDU_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_edu_mts_rt_inactivity_rate_by_sex_education_and_marital_statu_2025, title = {Inactivity rate by sex, education and marital status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_EDU_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-edu-mts-rt-inactivity-rate-by-sex-education-and-marital-statu}} } ``` ## 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 2026-05-27 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_EDU_MTS_RT_This dataset contains labor market data for 35 Asian countries from 1970 to 2025, focusing on the Inactivity rate by sex, education and marital status (%) indicator. It comprises 119,391 observations sourced from the International Labour Organizations (ILO) ILOSTAT database, retrieved via API and filtered specifically for Asian countries. The dataset includes multiple classification dimensions such as country codes, data sources, indicator codes, sex disaggregation (total, male, female, other), education classification, marital status classification, observation year, observed values, and status flags. Data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions to ensure comparability across countries. This dataset is suitable for machine learning tasks including tabular classification, tabular regression, and time-series forecasting.

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ILOSTAT统计数据库，聚焦于亚太地区劳动参与不足的衡量指标。数据通过REST API从ILOSTAT官方接口直接抓取，并依据ISO 3166-1 alpha-3标准过滤出35个亚洲国家的观测记录。原始数据来源于各国劳动力调查、家庭收入调查及行政记录等多元渠道，经ILO基于国际劳工统计学家会议（ICLS）定义进行统一协调与标准化处理，确保了跨国产出的可比性与学术严谨性。最终由Electric Sheep Asia进行重封装，形成包含119,391条观测的整洁表格数据集。

使用方法

使用者可通过HuggingFace Datasets库中的`load_dataset`函数便捷加载数据，并将其直接转换为Pandas DataFrame进行后续分析。典型应用场景包括按国家筛选特定时间序列，或针对单一指标进行趋势可视化。数据集支持以国家为列、时间为行的透视表重构，便于构建面板数据矩阵开展回归分析。对于涉及教育、婚姻与性别差异的劳动经济学、人口学及发展研究课题，该数据集提供了经过权威机构协调的高质量底层数据，降低了多国比较研究的预处理成本。

背景与挑战

背景概述

在劳动经济学与可持续发展目标（SDG）研究领域，劳动力利用不足的精确测量是评估区域经济韧性与社会包容性的关键基石。该数据集由国际劳工组织（ILO）旗下的ILOSTAT数据库与Electric Sheep Asia于2025年联合构建，聚焦亚洲35个国家1970至2025年间的不活动率，按性别、教育程度与婚姻状况进行分层统计。作为“其他劳动力利用不足指标”体系的核心组成部分，该数据集旨在揭示亚洲劳动力市场中未被传统失业指标捕获的隐性闲置状态，为区域劳动力结构转型、性别平等政策制定及教育回报率分析提供了跨时空、高细粒度的量化基础。其系统性整合了多次国际劳工统计学家会议（ICLS）定义下的调查微观数据，覆盖广泛地理范围，对亚洲发展研究具有深远影响。

当前挑战

该数据集面临的挑战首先体现在所解决的领域问题层面：传统失业率指标难以全面反映亚洲特有的劳动力隐性闲置现象，如因家庭责任、教育壁垒或社会规范导致的非自愿不活动状态，亟需通过细粒度分解揭示性别、教育与婚姻状况间的交互效应。在构建过程中，ILOSTAT数据源涉及多国劳动力调查、行政记录等异构来源，数据一致性受制于各国统计方法演变与调查频次差异，需借助“最佳来源”选择机制与标记系统处理方法论中断与可靠性标志。此外，年度观测频率限制了季节性波动与短期政策冲击的解析能力，而分类维度（如教育等级非标准定义）的编码差异增加了跨国产出的可比性挑战。

常用场景

经典使用场景

该数据集涵盖了1970年至2025年间亚洲35个国家的劳动不充分利用率指标，具体聚焦于性别、教育水平和婚姻状况三个维度的经济活动人口不活跃率。研究人员通常将其用于构建面板数据模型，以探究亚洲劳动力市场中不同人口亚群在长时间跨度内的参与行为演变规律。经典的使用范式包括按国家分组的时间序列可视化、跨区域横截面对比分析，以及基于多分类变量的细分群体波动追踪，从而揭示劳动参与模式随社会经济变迁的动态特征。

解决学术问题

数据集有效解决了劳动经济学中关于劳动不活跃群体异质性的量化难题，尤其是缺乏长期、细粒度、跨区域可比性数据的问题。学术研究中，该数据常被用于检验教育投资对女性劳动参与决策的边际效用、婚姻状态变化对劳动力退出概率的影响，以及性别差距在不同发展阶段的收敛趋势。其重要意义在于提供了标准化、可复用的数据基础，支撑了关于亚洲劳动市场结构转型、人力资本积累与性别平等议题的实证分析，为验证理论假设和修正政策模型提供了坚实依据。

实际应用

在实际应用层面，该数据集为国际组织与各国政策制定者评估劳动力市场健康度提供了关键工具。通过监测不同性别、教育背景及婚姻状况人群的不活跃率变动，政府可精准定位劳动参与障碍最严重的群体，从而设计针对性的职业培训计划和就业扶持政策。此外，数据分析平台和智库利用该数据生成劳动力报告和预测模型，为企业人力资源规划与区域发展投资决策提供情报支持，助力优化劳动资源配置与社会保障体系设计。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲地区劳动参与闲置率的性别、教育与婚姻状态多维交叉分析，基于ILOSTAT官方统计体系整合了1970至2025年间35个亚洲国家的近12万条观测记录。其前沿研究方向体现在两个层面：一是利用时间序列建模与面板数据分析方法，揭示亚洲女性因教育水平与非婚状态导致的劳动参与闲置率结构性特征，尤其关注东亚和东南亚经济体在后疫情时代劳动力市场复苏进程中的性别差异演变；二是结合婚姻状态与受教育程度的交叉分类变数，探究适龄劳动人口的非经济活动模式，为设计精准的劳动力激活政策与包容性增长战略提供实证依据。该数据集的发布恰逢国际劳工组织推动多维劳动力闲置指标替代单一失业率的全球共识浪潮，对亚洲各国优化性别平等就业目标监测体系、填补发展中国家细粒度劳动统计缺口具有重要参考价值。