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

--- 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, rural / urban areas and disability status (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex, rural / urban areas and disability status (%) | Asia (ILOSTAT) 🌏 **3,170 observations** · **17 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **3,170 observations** of `Other measures of labour underutilization` data across **17 Asia countries**, spanning **1996–2024**, 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_DSB_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_DSB_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 17 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 540 | 2005 | 2024 | | `MNG` | 486 | 2006 | 2024 | | `ARM` | 459 | 2007 | 2023 | | `KHM` | 351 | 1996 | 2023 | | `IDN` | 243 | 2010 | 2023 | | `LKA` | 189 | 2018 | 2024 | | `PSE` | 180 | 2018 | 2022 | | `THA` | 135 | 2007 | 2019 | | `TLS` | 107 | 2015 | 2022 | | `BGD` | 93 | 2011 | 2024 | | `AFG` | 90 | 2017 | 2021 | | `LAO` | 81 | 2015 | 2022 | | `IRQ` | 54 | 2012 | 2021 | | `TJK` | 54 | 2003 | 2007 | | `PAK` | 54 | 2020 | 2021 | | ... | _2 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_GEO_DSB_RT` — Inactivity rate by sex, rural / urban areas and disability 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_GEO_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex, rural / urban…` | | `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` | | `classif2` | `string` | Second classification variable where applicable | `DSB_STATUS_TOTAL` | | `classif2.label` | `string` | — | `Disability status: 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) | `B` | | `obs_status.label` | `string` | — | `Break in series` | | `note_classif` | `string` | — | `C14:6260` | | `note_classif.label` | `string` | — | `Nonstandard definition of disability:…` | | `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-dsb-rt-inactivity-rate-by-sex-rural-urban-areas-and-disab") 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_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_GEO_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_GEO_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_geo_dsb_rt_inactivity_rate_by_sex_rural_urban_areas_and_disab_2024, title = {Inactivity rate by sex, rural / urban areas and disability status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_GEO_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-geo-dsb-rt-inactivity-rate-by-sex-rural-urban-areas-and-disab}} } ``` ## 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_DSB_RT_This dataset contains inactivity rate (%) data by sex, rural/urban areas, and disability status for Asian countries, sourced from the International Labour Organization (ILO) ILOSTAT database. It covers 17 Asian countries from 1996 to 2024, with 3,170 observations and one core indicator (EIP_DWAP_SEX_GEO_DSB_RT). Data is retrieved via the ILOSTAT REST API and standardized, including dimensions such as country codes, sex disaggregation (total, male, female), geographic classification (e.g., national level), and disability status. The data is provided at annual frequency and includes information on sources, observation status, and notes, making it suitable for tabular classification, regression, and time-series forecasting tasks. The dataset is repackaged by Electric Sheep Asia and released under the CC-BY-4.0 license.

搜集汇总

数据集介绍

构建方式

该数据集由Electric Sheep Asia团队从ILOSTAT权威数据库中精心提取并重新封装而成。数据通过ILO提供的REST API接口直接获取，筛选出亚洲17个国家的观测记录，并依据国际劳工统计学家会议定义对原始调查微观数据进行统一协调处理。最终以Parquet格式存储，确保了数据加载的高效性与跨平台兼容性。

使用方法

用户可通过HuggingFace的`datasets`库便捷地加载该数据集，调用`load_dataset()`即可获取表格格式的训练集，并轻松转换为Pandas DataFrame进行后续分析。典型使用场景包括按国家筛选子集、绘制特定指标的时间序列趋势图，或通过数据透视表构建国家维度的面板数据结构。数据集的结构化字段设计使得跨维度的过滤与聚合操作极为直观，适用于分类、回归及时间序列预测等多种机器学习任务。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）的统计数据库ILOSTAT创建，经Electric Sheep Asia于2024年重新封装后发布在HuggingFace平台，旨在系统性地提供亚洲17个国家自1996年至2024年间按性别、城乡区域及残疾状况划分的经济不活跃率时间序列数据。核心研究问题聚焦于劳动市场边缘群体的参与壁垒，尤其是多重弱势身份（如残疾女性）在区域与时间维度上的不活跃率差异。作为ILOSTAT这一全球劳动统计权威数据库的衍生数据集，它填补了亚洲区域在劳动利用不足指标上的系统性数据空白，为发展经济学、劳动社会学及政策评估提供了细分至国家—年度—群体三层交互的量化基础，显著推动了关于包容性增长与可持续发展目标（SDG）中体面劳动议题的实证研究。

当前挑战

该数据集所解决的领域核心挑战在于：传统劳动统计往往忽略或间并残疾状态这一维度，导致对劳动利用不足的测量掩盖了边缘群体的真实困境，尤其当性别与城乡地理变量交互时，数据盲区进一步加剧了政策制定中的系统性偏差。在构建过程中，面临多重方法论与数据整合挑战：其一，ILOSTAT通过国际劳工统计学家会议（ICLS）定义协调来自家庭调查、机构调查及行政记录等多源异构数据，但不同国家的调查设计、残疾定义标准（如`note_classif.label`中标注的非标准定义）及质量标记（如`obs_status`中断序列）引入了复杂的可比性与时间序列一致性难题；其二，由于指标仅发布年度频率，且部分国家观测点稀疏（如塔吉克斯坦仅2003-2007年有数据），在时间序列建模中需处理不平衡面板数据与缺失值插补的统计挑战；其三，在多维分类结构（性别、地理、残疾状态）下，观测值分布高度非均匀，少数组合的样本量极小，易导致细分群体估计的方差膨胀和统计推断的可靠性下降。

常用场景

经典使用场景

该数据集收录了1996年至2024年间亚洲17个国家的劳动力不活跃率数据，并按性别、城乡区域和残疾状态进行了精细分层。其经典使用场景聚焦于劳动经济学中的多维弱势群体分析，研究者可借助这一结构化面板数据，系统挖掘残疾状态与性别、地理区位交互作用下劳动力参与率的动态演变规律。指标定义严格遵循国际劳工统计学家会议标准，确保了跨国比较的可靠性，特别适合用于评估不同发展水平国家在包容性就业政策上的差异与成效。

解决学术问题

数据集核心解决了劳动经济学中长期存在的弱势群体劳动参与测度难题。传统研究多聚焦于失业率这一单一维度，而该数据提供了'劳动力不活跃率'的替代性指标，能够更真实地反映边缘群体（如残疾人口、农村女性）在劳动市场中的隐形边缘化状态。通过引入性别×城乡×残疾的三维交互视角，突破了以往研究仅关注单一维度的局限，为探究多重社会身份叠加下的就业排斥机制提供了量化基础，对完善可持续就业发展目标的监测体系具有重要意义。

实际应用

在实际应用层面，该数据集为亚太地区国家的就业政策制定与评估提供了坚实的数据支撑。政策研究者可利用其按性别、城乡和残疾状态划分的粒度，精准识别不同亚群中劳动参与率最低的群体，从而设计更具靶向性的职业培训、无障碍就业扶持或社会保障方案。国际组织如国际劳工组织亦能借助这些跨国可比数据，监测各国在落实包容性就业承诺方面的进展，并据此调整技术援助与合作项目的优先次序。

数据集最近研究

最新研究方向

该数据集聚焦于亚洲地区按性别、城乡区域及残疾状况划分的非经济活动率，为劳动经济学与包容性发展研究提供了精细化时空数据支撑。当前前沿研究正深入探讨残疾群体在劳动力市场中的边缘化机制，结合ILOSTAT统一标准下的跨国面板数据，学者可量化分析政策干预（如无障碍就业计划）对非经济活动率的动态影响。尤其在可持续发展目标（SDGs）框架下，该数据助力揭示残疾歧视与性别、地域交织的复合脆弱性，推动从单一指标评估转向多维包容性评价体系构建，为亚洲新兴经济体制定精准化劳动参与策略提供实证依据。