electricsheepasia/asia-ilo-eip-dwap-sex-edu-geo-rt-inactivity-rate-by-sex-education-and-rural-urban-a

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 10K<n<100K tags: - tabular - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Inactivity rate by sex, education and rural / urban areas (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex, education and rural / urban areas (%) | Asia (ILOSTAT) 🌏 **41,104 observations** · **30 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **41,104 observations** of `Other measures of labour underutilization` data across **30 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_GEO_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_GEO_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 30 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `PSE` | 3,933 | 2000 | 2022 | | `IDN` | 3,718 | 1990 | 2023 | | `CYP` | 3,154 | 1999 | 2024 | | `KHM` | 2,445 | 1996 | 2023 | | `MNG` | 2,286 | 2003 | 2024 | | `VNM` | 2,192 | 2010 | 2024 | | `PAK` | 2,163 | 2005 | 2025 | | `THA` | 2,030 | 2007 | 2024 | | `GEO` | 2,013 | 2009 | 2024 | | `ARM` | 2,006 | 2001 | 2023 | | `LKA` | 1,837 | 2010 | 2024 | | `IND` | 1,827 | 1994 | 2025 | | `KOR` | 1,772 | 2000 | 2025 | | `BRN` | 1,260 | 2014 | 2024 | | `JOR` | 1,003 | 2017 | 2024 | | ... | _15 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_EDU_GEO_RT` — Inactivity rate by sex, education and rural / urban areas (%) ## 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_GEO_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 | `GEO_COV_NAT` | | `classif2.label` | `string` | — | `Area type: National` | | `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-geo-rt-inactivity-rate-by-sex-education-and-rural-urban-a") 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_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_EDU_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_EDU_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_edu_geo_rt_inactivity_rate_by_sex_education_and_rural_urban_a_2025, title = {Inactivity rate by sex, education and rural / urban areas (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_EDU_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-edu-geo-rt-inactivity-rate-by-sex-education-and-rural-urban-a}} } ``` ## 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_GEO_RT_This dataset contains 41,104 observations of Inactivity rate by sex, education and rural / urban areas (%) data across 30 Asia countries, spanning 1970–2025, sourced from the ILOSTAT database of the International Labour Organization (ILO). It covers other measures of labour underutilization, specifically the indicator EIP_DWAP_SEX_EDU_GEO_RT. The dataset provides annual data disaggregated by country, year, sex (total, male, female, other), education classification, and rural/urban area type. Each observation includes country code, country name, data source, indicator code, indicator label, sex classification, education classification, area classification, observation year, observed value, observation status, and related notes. The data is harmonized by ILO using International Conference of Labour Statisticians (ICLS) definitions and is suitable for tabular classification, regression, and time-series forecasting tasks.

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）旗下的ILOSTAT核心统计数据库，聚焦于亚洲地区劳动力利用不足的其他衡量指标。构建过程中，数据通过ILOSTAT REST API接口直接抽取，并依据ISO 3166-1 alpha-3标准筛选出30个亚洲国家的观测记录，时间跨度覆盖1970年至2025年。原始数据来自各国劳动力调查、家庭收入调查等行政记录，经ILO统计部门依据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，最终形成包含41,104条观测的规整数据集。

使用方法

使用该数据集极为便捷，研究者可直接通过HuggingFace的datasets库以一行代码完成加载：load_dataset("electricsheepasia/asia-ilo-eip-dwap-sex-edu-geo-rt-inactivity-rate-by-sex-education-and-rural-urban-a")。加载后，数据可轻松转换为Pandas DataFrame进行分析。支持按国家过滤、对特定指标进行时间序列可视化，以及通过数据透视表构建国家×年份的矩阵，便于进行跨区域、跨时期的比较研究与统计分析。

背景与挑战

背景概述

在全球劳动力市场研究中，非经济活动率的测度对于理解劳动力利用不足现象具有关键意义。国际劳工组织（ILO）作为全球劳工统计的权威机构，通过其ILOSTAT数据库整合了来自各国劳动力调查的标准化数据。该数据集由Electric Sheep Asia于2025年重新打包发布，聚焦亚洲30个国家1970至2025年间按性别、教育水平和城乡区域划分的非活动率指标，共计41,104条观测值。核心研究问题在于揭示劳动力市场中隐性闲置劳动力的结构性特征，为政策制定者提供性别平等、教育差异与地域分布等多维视角的决策依据。数据集基于国际劳工统计学家会议（ICLS）定义进行 harmonisation，通过ILOSTAT REST API直接获取并过滤亚洲国家代码，确保了数据的一致性与国际可比性。其影响力体现在为区域劳动力市场比较、可持续发展目标监测以及机器学习驱动的劳动经济预测提供了标准化的高质量时序数据基础。

当前挑战

该数据集所应对的领域挑战主要源于劳动力利用不足统计的复杂性与结构性。首先，经济活动人口统计中，非活动率作为隐性失业的重要指标，传统上难以通过单一维度充分刻画，而该数据集通过性别、教育和城乡三轴分解，揭示了不同群体间劳动力参与的结构性障碍。其次，构建过程中面临多重数据质量挑战：跨国家调查方法的异质性导致分类标准（如教育层级）存在非标准化标注（如note_classif列标明'Nonstandard education level'），需要通过源数据标志进行追溯。时间序列中因方法修订产生的断裂（如note_indicator列标注的'Break in series'）要求用户谨慎处理间断点。此外，部分观测值被标记为不可靠（obs_status = 'U'），且不同国家的时间覆盖范围差异显著（如阿富汗数据极少而巴勒斯坦数据密集），增加了跨国比较与机器学习建模的偏差风险。

常用场景

经典使用场景

该数据集广泛应用于劳动经济学与人口社会学研究领域，其核心价值在于刻画亚洲地区不同性别、教育背景及城乡地域的劳动市场非参与率波动。研究者可借助该数据进行面板数据分析、时间序列建模与结构性比较，深入探讨教育水平对女性及农村人口就业抑制效应的动态演变。其独特的交叉分层特征使学者能够识别并量化性别隔离、教育回报率差异与城乡二元结构对劳动参与意愿的复合影响，成为跨区域劳动力资源配置研究的基石性数据源。

解决学术问题

该数据集解决了亚洲发展中国家劳动市场低估问题中性别、教育与地理三重交互效应的量化难题。传统研究往往因缺乏统一口径的亚区域微观数据而陷入碎片化困境，而该数据通过ILO标准化定义整合30国数十年的观测值，克服了跨国可比性障碍。它助推学界系统揭示教育扩张是否真正缩小了劳动非参与率的性别鸿沟、城镇化进程是否加剧了农村女性的边缘化趋势等关键学术命题，为修正新古典劳动供给理论在亚洲语境下的适用性提供了经验证据。

实际应用

在实际应用层面，该数据集为国际组织与亚太各国制定包容性就业政策提供了定量依据。政策制定者可基于不同性别与教育层级的非参与率时序走势，精准定位社会保障体系与职业培训资源的投放优先区域。例如，通过对比城乡女性非参与率的变化拐点，评估产假制度与托育服务对女性重新进入劳动市场的激励效果。同时，私营企业亦可利用该数据洞察潜在劳动力池结构，优化其区域招聘与人才留存策略。

数据集最近研究

最新研究方向

本数据集聚焦于亚洲30国1970至2025年间劳动参与率缺失的时空异质性，通过性别、教育水平与城乡地域的交叉划分，为前沿劳动经济学与区域发展研究提供了高颗粒度面板数据。在亚洲劳动力市场结构转型与性别平等议题持续升温的背景下，结合ILOSTAT标准化统计口径，可精准捕捉非正规就业与潜在劳动力蓄积的动态演变。该数据有力支撑了关于教育回报率城乡差异、女性劳动抑制效应以及城镇化进程中隐性失业的区域比较分析，对推动联合国可持续发展目标中体面劳动指标的本土化实证研究具有显著方法论与政策价值。