electricsheepasia/asia-ilo-eip-dwap-sex-edu-dsb-rt-inactivity-rate-by-sex-education-and-disability-st

--- 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, education and disability status (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex, education and disability status (%) | Asia (ILOSTAT) 🌏 **6,299 observations** · **20 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **6,299 observations** of `Other measures of labour underutilization` data across **20 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_EDU_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_EDU_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 20 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `CYP` | 978 | 2005 | 2024 | | `MNG` | 837 | 2006 | 2024 | | `ARM` | 744 | 2007 | 2023 | | `KHM` | 600 | 1996 | 2023 | | `ISR` | 432 | 2016 | 2023 | | `IDN` | 411 | 2010 | 2023 | | `LKA` | 375 | 2018 | 2024 | | `THA` | 270 | 2007 | 2019 | | `BGD` | 232 | 2011 | 2024 | | `PSE` | 225 | 2018 | 2022 | | `TLS` | 194 | 2015 | 2022 | | `IRQ` | 156 | 2007 | 2021 | | `AFG` | 153 | 2017 | 2021 | | `LAO` | 153 | 2015 | 2022 | | `TJK` | 135 | 2003 | 2016 | | ... | _5 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_EDU_DSB_RT` — Inactivity rate by sex, education 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_EDU_DSB_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 | `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) | `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-dsb-rt-inactivity-rate-by-sex-education-and-disability-st") 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_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_EDU_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_EDU_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_edu_dsb_rt_inactivity_rate_by_sex_education_and_disability_st_2024, title = {Inactivity rate by sex, education and disability status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_EDU_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-edu-dsb-rt-inactivity-rate-by-sex-education-and-disability-st}} } ``` ## 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_DSB_RT_This dataset contains 6,299 observations across 20 Asian countries from 1996 to 2024, focusing on the indicator Inactivity rate by sex, education and disability status (%). Sourced from the International Labour Organizations ILOSTAT database, it covers statistics on labour underutilization, with disaggregation dimensions such as sex (total, male, female, other), education level, and disability status. The dataset is in tabular format and suitable for machine learning tasks including tabular classification, 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数据库，聚焦于亚洲地区劳动利用不足的衡量指标。研究团队通过ILOSTAT REST API直接调取原始数据，并依据国际劳工统计学家会议（ICLS）定义对各国劳动力调查微观数据进行标准化处理。随后，基于ISO 3166-1 alpha-3国家代码筛选出20个亚洲国家的观测值，整合为包含6,299条记录的表格数据集。数据集中每条记录均携带来源标签（source.label），确保数据溯源清晰，并为每个变量提供详尽的多语言标签说明，便于跨文化理解。

特点

本数据集最显著的特征在于其多维度的分类架构，涵盖性别（总、男、女、其他）、教育水平（聚合级别）及残疾状态（总计、是、否）三大关键维度，允许研究者深入剖析不同社会群体的劳动参与差异。时间跨度从1996年至2024年，覆盖20个亚洲国家，观测值达到6,299条。数据还附有观测状态标记（如“不可靠”或“临时”）及系列断裂说明等质量注释，为用户评估数据可靠性提供透明依据。其细粒度的分层结构和完整的数据质量元数据，使其在劳动经济学与社会政策研究中具有独特价值。

使用方法

用户可通过HuggingFace Datasets库便捷加载数据，使用`load_dataset()`函数即可将数据转化为Pandas DataFrame格式。典型应用包括按国家筛选（如印度尼西亚）、对单一指标进行时间序列分析，或利用透视表构建国家-年份矩阵以进行跨区域比较。数据集已预设为表格分类、回归及时间序列预测任务格式，兼容常见的机器学习工作流。建议在使用前检查`obs_status`列以剔除不可靠观测值，并留意`note_indicator.label`中的方法修订信息，确保分析的严谨性。

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创建，经Electric Sheep Asia于2024年重新打包并发布在HuggingFace平台，专注于亚洲地区按性别、教育程度和残疾状况划分的不活动率（%）。ILOSTAT作为全球劳动统计的权威来源，整合了来自200多个经济体的劳动力调查、家庭收入调查等数据，旨在提供标准化的劳动力利用指标。该数据集覆盖1996年至2024年间20个亚洲国家的6,299个观测值，核心研究问题在于揭示残疾人群在劳动力市场中的参与障碍，特别是性别与教育水平对不活动率的交互影响。其发布填补了亚洲地区在残疾与劳动参与交叉领域的高质量结构化数据空白，为政策制定者、社会科学家及国际发展机构提供了量化分析基础，推动了包容性劳动政策的实证研究。

当前挑战

该数据集所面临的领域挑战主要在于劳动力不活动率的多维解构难题：传统劳动统计常忽视残疾状态这一关键维度，导致弱势群体的劳动排斥现象被掩盖，而该数据集通过性别、教育和残疾状态的交叉分类，揭示了隐匿于汇总数据下的结构性不平等。在构建过程中，挑战源自ILO对来自各国不同统计体系数据的协调——需采用国际劳动统计学家会议（ICLS）定义进行微观数据再加工，同时处理数据源标注的追踪性问题（如source.label列）。此外，年度频率的限制（未纳入月度或季度序列）以及部分观测值存在方法修订（如note_indicator.label中的“Break in series”）或可靠性标记（obs_status为“Unreliable”），均要求研究者谨慎处理时间序列的连续性与数据质量评估。

常用场景

经典使用场景

该数据集聚焦于亚洲20个国家1996至2024年间，按性别、受教育程度及残疾状况划分的经济不活跃率（%），源自国际劳工组织（ILO）的ILOSTAT权威数据库。在学术研究与政策分析的语境下，研究者常将其用于构建面板数据模型，以探讨社会人口结构——尤其是性别差异、教育水平与残疾状态——对劳动力市场退出行为的交互影响。借助其精细的分层维度（如教育聚合层级与残疾状态分类），可以揭示传统失业率指标所无法捕捉的劳动参与障碍。该数据集亦为时间序列预测任务提供了优质原料，支持对亚洲区域非经济活动趋势的建模与推演，从而更深刻地理解劳动力市场中的隐蔽性闲置与结构性因素。

衍生相关工作

基于该数据集，衍生出了一系列以劳动力不活跃率预测与脆弱群体识别为核心的经典研究。例如，部分工作利用时间序列分解方法（如STL与Prophet）对各国不活跃率进行趋势、季节性与残差项的分离建模，用以检测社会经济事件（如金融危机或疫情冲击）对特定人群劳动参与意愿的结构性断裂。另有一些团队结合多源行政数据，构建了以性别×教育×残疾状态为交叉单元的聚类分析模型，识别出“高不活跃率—低技能—残疾女性”等典型脆弱聚居群。在计量经济学方向，学者采用双重差分（DID）与断点回归（RDD）框架，借助该数据验证了劳动市场制度改革（如弹性就业法或残疾人配额政策）对相关亚群经济活动率的异质性处理效应。此外，该数据集还作为基准被用于开发图神经网络，以建模亚洲各国劳动力退出模式的交互关联网络。

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、教育程度和残疾状况分层的不活跃率指标，为劳动力市场弱势群体的结构性边缘化研究提供了关键量化支撑。在包容性增长与后疫情就业复苏的全球议程下，研究者正借助此类精细分层数据，剖析残障群体在技能错配与信息壁垒中的主动退出机制，并联动ILOSTAT体系中的时间序列特征，构建针对亚洲新兴经济体的政策评估模型。当前前沿方向进一步指向利用该数据训练可解释的分类与回归模型，以揭示多重弱势身份（如低教育水平女性残障者）的叠加效应，从而为国际劳工组织倡导的‘体面劳动’目标提供数据驱动的循证路径。