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

--- 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 - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Inactivity rate by sex and rural / urban areas (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex and rural / urban areas (%) | Asia (ILOSTAT) 🌏 **3,222 observations** · **31 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **3,222 observations** of `Other measures of labour underutilization` data across **31 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_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_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 31 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 297 | 1990 | 2023 | | `PSE` | 276 | 2000 | 2022 | | `CYP` | 234 | 1999 | 2024 | | `KHM` | 180 | 1996 | 2023 | | `MNG` | 171 | 2003 | 2024 | | `ARM` | 162 | 2001 | 2023 | | `VNM` | 156 | 2007 | 2024 | | `PHL` | 156 | 2007 | 2023 | | `PAK` | 153 | 2005 | 2025 | | `GEO` | 144 | 2009 | 2024 | | `KOR` | 144 | 2000 | 2025 | | `IND` | 138 | 1994 | 2025 | | `THA` | 129 | 2007 | 2024 | | `LKA` | 126 | 2010 | 2024 | | `TUR` | 126 | 2000 | 2013 | | ... | _16 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_GEO_RT` — Inactivity rate by sex 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_GEO_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex and rural / ur…` | | `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.) | `GEO_COV_NAT` | | `classif1.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) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `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-eip-dwap-sex-geo-rt-inactivity-rate-by-sex-and-rural-urban-areas") 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_GEO_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_GEO_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_GEO_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_geo_rt_inactivity_rate_by_sex_and_rural_urban_areas_2025, title = {Inactivity rate by sex and rural / urban areas (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_GEO_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-geo-rt-inactivity-rate-by-sex-and-rural-urban-areas}} } ``` ## 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_GEO_RT_This dataset contains labor market statistics for Asia from the International Labour Organization (ILO) ILOSTAT database, specifically focusing on the Inactivity rate by sex and rural / urban areas (%) indicator under Other measures of labour underutilization. It includes 3,222 annual observations across 31 Asian countries from 1970 to 2025. The core indicator is EIP_DWAP_SEX_GEO_RT, which measures inactivity rates by sex (total, male, female) and rural/urban areas (national level). The schema comprises country codes, country names, data sources (e.g., Labour Force Survey), indicator codes, indicator labels, sex disaggregation, classification variables, observation years, observed values, observation status flags, and related notes. The dataset is normalized and repackaged by Electric Sheep Asia for machine learning tasks such as tabular classification, regression, and time-series forecasting.

搜集汇总

数据集介绍

构建方式

该数据集来源于国际劳工组织（ILO）的ILOSTAT统计数据库，聚焦于亚洲地区31个国家的劳动力未充分利用指标。数据通过调用ILOSTAT REST API直接获取，并依据亚洲ISO3国家代码进行筛选过滤。ILOSTAT采用国际劳工统计学家会议（ICLS）定义对原始调查微观数据进行统一协调处理，确保了跨国家与跨年度的数据可比性。数据集收录了1970年至2025年间共计3,222条观测记录，涵盖了按性别和城乡地域划分的经济不活跃率这一核心指标，并系统性地标注了数据来源（如劳动力调查）以供追溯。

特点

本数据集具备鲜明的地理与时间覆盖优势，横跨31个亚洲国家，时间跨度长达55年，为区域劳动力市场分析提供了宝贵的长期序列资料。数据经过专业的分层处理，包含性别（总、男、女）和地域（全国、城乡）等多维度划分，极大地增强了分析粒度。此外，数据集中精心设置了观测状态标志与详细注释列，用以标识潜在的数据断裂或方法论修订等质量信息，使研究者能够审慎评估数据可信度，从而进行更严谨的实证研究。

使用方法

数据集的调用极为便捷，研究人员可借助HuggingFace的`datasets`库，通过一行代码`load_dataset()`即可将数据加载为Pandas DataFrame进行后续分析。典型应用场景包括：按特定国家筛选子集开展国别研究；以时间序列形式绘制特定指标的变化趋势，直观揭示经济发展与失业动态；或利用透视功能构建国家×年份的矩阵面板数据。清晰的schema设计使研究者能迅速定位观测值、分类维度与质量注释列，是进行亚洲区域劳动力市场计量分析、政策评估与跨国家比较研究的理想起点。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计数据库ILOSTAT发布，经Electric Sheep Asia于2025年重新打包，聚焦亚太地区31个国家1970至2025年间基于性别和城乡划分的经济不活跃率。在劳动经济学与社会发展研究中，劳动力市场中的非经济活动（即既不就业也未失业）是衡量劳动力资源利用效率的关键维度，尤其对于处于快速城镇化与性别角色转型期的亚洲国家，不活跃率的空间与性别分布差异揭示了隐性劳动力储备与社会结构性病变。该数据集包含3,222条观测记录，以统一的ILO统计标准整合各国劳动力调查数据，为跨区域横向比较和时序分析提供了可信基准，成为研究亚洲就业模式演变、弱势群体就业障碍评估及可持续发展目标监测的重要工具。

当前挑战

该数据集面临的领域挑战在于：经济不活跃率涵盖家庭照料、教育、退休及丧失信心劳动者等异质性群体，单纯汇总比率难以揭示不活跃的内在驱动机制，导致政策干预方向模糊。针对性别和城乡维度的分解虽提供初步洞察，但跨文化背景下不活跃含义的差异与测量偏移可能削弱可比性。构建过程中，因各国劳动力抽样调查周期、统计口径及质量参差，ILO虽坚持采用‘最佳来源’方法，但序列断裂（break in series）与方法变更（如I11:264注释标识）频繁，时间序列一致性受损。此外，部分国家年份数据稀疏、观测状态标记为临时性（provisional）或不稳定信号，进一步限制了高精度建模与可靠的预测性分析。

常用场景

经典使用场景

该数据集汇聚了国际劳工组织ILOSTAT数据库中31个亚洲国家1970至2025年间的不活动率观测值，涵盖性别与城乡地域的细粒度划分。经典使用场景聚焦于时间序列分析与面板数据建模，研究者可借助Python的datasets库一键加载，随后按国家、性别或城乡区域进行筛选与可视化。例如，通过透视表构建国家×年份矩阵，追踪特定地区劳动力参与度的长期演变趋势，为劳动经济学中的结构性失业和隐性就业问题提供实证基础。

解决学术问题

在学术前沿，该数据集解决了劳动力市场研究中因性别与地理差异导致的统计偏误问题。它突破了传统失业率指标的局限，聚焦‘其他劳动力利用不足’指标，精准度量被正式失业统计忽略的潜在劳动力——如因家庭责任、教育资源匮乏或农村就业机会有限而退出市场的群体。这一数据支持研究者检验性别不平等与城乡发展鸿沟对劳动力供给的动态影响，推动劳动经济学与区域发展理论的融合创新。

衍生相关工作

该数据集衍生了诸多经典工作，包括构建亚洲劳动力不活动率的预测模型与面板回归分析。研究者利用其构建机器学习模型，预测社会经济冲击（如经济危机或疫情）对性别化劳动参与率的影响。此外，数据集的标准化格式推动了可复用分析管道的开发，如基于Pandas和Statsmodels的自动化时间序列分解与动态面板估计，成为劳动统计教学与跨学科研究的标准范例。