electricsheepasia/asia-ilo-eip-dwap-sex-mts-dsb-rt-inactivity-rate-by-sex-marital-status-and-disabili

--- 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, marital status and disability status (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex, marital status and disability status (%) | Asia (ILOSTAT) 🌏 **9,112 observations** · **20 Asia countries** · **1996–2024** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **9,112 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_MTS_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_MTS_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` | 1,439 | 2005 | 2024 | | `ARM` | 1,401 | 2007 | 2023 | | `MNG` | 972 | 2013 | 2024 | | `KHM` | 936 | 1996 | 2023 | | `IDN` | 660 | 2010 | 2023 | | `ISR` | 576 | 2016 | 2023 | | `LKA` | 504 | 2018 | 2024 | | `THA` | 360 | 2007 | 2019 | | `BGD` | 320 | 2011 | 2024 | | `TLS` | 313 | 2015 | 2022 | | `AFG` | 250 | 2017 | 2021 | | `LAO` | 237 | 2015 | 2022 | | `IRQ` | 215 | 2007 | 2021 | | `TUR` | 174 | 2000 | 2024 | | `TJK` | 164 | 2003 | 2007 | | ... | _5 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_MTS_DSB_RT` — Inactivity rate by sex, marital status 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_MTS_DSB_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex, marital statu…` | | `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.) | `MTS_AGGREGATE_TOTAL` | | `classif1.label` | `string` | — | `Marital status (Aggregate): 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` | — | `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-mts-dsb-rt-inactivity-rate-by-sex-marital-status-and-disabili") 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_MTS_DSB_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_MTS_DSB_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_MTS_DSB_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_mts_dsb_rt_inactivity_rate_by_sex_marital_status_and_disabili_2024, title = {Inactivity rate by sex, marital status and disability status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2024}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_MTS_DSB_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-mts-dsb-rt-inactivity-rate-by-sex-marital-status-and-disabili}} } ``` ## 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_MTS_DSB_RT_This dataset contains 9,112 observations of inactivity rate (%) by sex, marital status and disability status across 20 Asia countries from 1996 to 2024. The data is sourced from the ILOSTAT database of the International Labour Organization (ILO), under the topic Other measures of labour underutilization, with the specific indicator EIP_DWAP_SEX_MTS_DSB_RT (Inactivity rate by sex, marital status and disability status). It includes columns such as country code, indicator label, sex disaggregation (total, male, female, etc.), marital status classification, disability status classification, observation year, observed value, and status flags, suitable for tabular classification, regression, or 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）的ILOSTAT数据库，聚焦于亚洲地区按性别、婚姻状况和残疾状态划分的不活动率指标。Electric Sheep Asia团队通过ILOSTAT的REST API接口直接采集原始数据，并依据亚洲国家的ISO3编码进行地理筛选，最终整理出包含20个亚洲国家、跨越1996年至2024年共9112条观测记录的结构化数据集。数据经过ILO依据国际劳工统计学家会议（ICLS）定义的标准进行协调处理，确保了指标的跨国家可比性，同时保留了来源标签以供追踪溯源性。

特点

该数据集的核心特点在于其多维度的精细分层设计，涵盖了性别（男性、女性、总计及其他）、婚姻状况聚合类别以及残疾状态等多个分类变量，能够支持深层次的社会经济特征分析。所有观测值均以年度频率呈现，并附有观测状态标记（如初步或不可靠）及详细的注释信息（包括方法论修订、数据断裂及非标准定义等），为使用者提供了充足的数据质量背景。此外，数据集中包含20个亚洲国家，时间跨度近三十年，为纵向时间序列分析与跨国横向比较奠定了坚实基础。

使用方法

使用者可通过HuggingFace Datasets库中的load_dataset函数直接加载该数据集，并将其便捷地转换为Pandas DataFrame格式进行后续操作。典型应用场景包括对单一国家进行过滤分析，利用时间序列绘图展示指标变化趋势，或通过数据透视表构建国家与年份的矩阵以便于横向对比。数据集结构清晰，包含国家编码、指标代码、分层变量、观测年份、数值及必要的质量注释字段，适合从事劳动经济学、人口统计学及社会政策研究的学者与数据分析师直接调用，显著降低了数据获取与预处理的成本。

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（Inactivity rate）作为衡量劳动参与不足的重要指标，对于理解不同人口群体的就业障碍具有关键意义。由国际劳工组织（ILO）统计司维护的ILOSTAT数据库，基于国际劳工统计学家会议（ICLS）标准统一处理各国劳动力调查微观数据，成为该领域最具权威性的数据来源。2024年，Electric Sheep Asia团队从ILOSTAT REST API中提取并重新打包了亚洲地区的相关数据，形成了包含20个亚洲国家、9112条观测记录、时间跨度覆盖1996至2024年的专门数据集。该数据集聚焦于按性别、婚姻状况和残疾状态分层的非经济活动率，为研究亚洲地区劳动力市场的结构性特征、社会不平等以及人口政策制定提供了精细化的数据支撑。

当前挑战

该数据集所应对的核心领域挑战在于，传统的总体就业率指标无法揭示不同人口亚群在劳动参与上的深层差异，尤其是残疾人群体的非经济活动状况长期被宏观统计所掩盖。构建过程中的挑战体现在多维度数据融合与质量管控上：首先，各国劳动力调查的样本设计、调查频率及残疾人定义标准存在显著差异，需通过ILOSTAT的元数据标记（如note_classif字段）对非标准定义进行追溯与处理；其次，数据按年度发布，与部分国家提供的月度或季度序列不兼容，迫使使用者必须接受时间粒度的降级；最后，同一国家同年份的多源数据冲突需要通过ILO选取的“最佳来源”进行决策，同时obs_status状态标志（如不可靠标记）的有效利用也成为确保分析严谨性的关键环节。

常用场景

经典使用场景

该数据集作为国际劳工组织ILOSTAT数据库在亚洲区域的精细子集，汇聚了1996年至2024年间20个亚洲经济体、超过9000条观测记录，核心指标为按性别、婚姻状况与残疾状态划分的不活动率。其经典使用场景集中于多维劳动利用不足问题的量化建模——研究者可基于此数据解析宏观劳动力市场中特定人群的参与壁垒，例如通过时间序列分析揭示残疾女性与已婚女性不活动率的演进轨迹，或借助分类与回归方法识别性别、婚姻状态及残疾状况对劳动力退出的交互效应。数据集中丰富的分类维度（如婚姻状态聚合指标、残疾状态离散标准）使得细粒度比较成为可能，尤其适合构建面板数据模型以探究社会经济政策对不同脆弱群体的差异化影响。

实际应用

在国际发展机构与劳动统计部门的实际运作中，该数据集可转化为决策支持的核心工具。例如，亚洲开发银行或各国劳工部可利用其时间序列数据监测残疾人不活动率的年度变动，识别某项残障融合政策实施前后的效果拐点。非政府组织可以借助性别与婚姻状况交叉分析，锁定农村已婚残疾女性等高危群体，设计定向职业培训与社区就业支持计划。企业社会责任部门亦能参考国家层面的不活动率数据，评估特定区域的潜在劳动力储备，指导包容性用工战略的制定。此外，该数据集为ILO自身的统计方法修正提供了回溯验证的基础——源数据中标注的断点与修订说明使得政策评估者能够区分真实趋势变动与方法论调整带来的影响。

衍生相关工作

该数据集沿袭ILOSTAT系列数据的基因密码，已催化出多项实证研究范式：其一为劳动参与率影响因素的多层模型分析，学者将不活动率作为因变量，结合国家层面的GDP增长率、社会保障支出等宏观变量，识别结构性与周期性的驱动因素；其二为基于性别和残疾状态的不平等指数构建，研究者通过摘取性别与残疾分类交叉项的观测值，创建衡量社会包容程度的强化版基尼系数。在可计算一般均衡（CGE）模型领域，该数据被嵌入劳动力模块以模拟不同残障支持政策对总体就业率的长周期影响。此外，HuggingFace平台上的数据集重封装工作本身即代表了数据民主化的方法论创新——标准化Parquet格式与简洁的API接口使得全球研究者能零门槛复现分析，形成了开放科学背景下的数据策展新范式。