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

--- 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 and marital status (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex and marital status (%) | Asia (ILOSTAT) 🌏 **10,272 observations** · **36 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **10,272 observations** of `Other measures of labour underutilization` data across **36 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_MTS_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_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 36 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 672 | 1996 | 2023 | | `PHL` | 658 | 2001 | 2023 | | `KOR` | 615 | 2000 | 2025 | | `TUR` | 612 | 2000 | 2024 | | `CYP` | 528 | 1999 | 2020 | | `ARM` | 519 | 2001 | 2023 | | `IRN` | 516 | 2005 | 2024 | | `KHM` | 478 | 1996 | 2023 | | `VNM` | 448 | 2010 | 2024 | | `THA` | 438 | 2000 | 2024 | | `MNG` | 434 | 2009 | 2024 | | `PAK` | 415 | 2005 | 2025 | | `IND` | 398 | 1994 | 2025 | | `LKA` | 336 | 2010 | 2024 | | `ISR` | 312 | 2012 | 2024 | | ... | _21 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_MTS_RT` — Inactivity rate by sex and marital 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_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex and marital st…` | | `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` | | `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_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-rt-inactivity-rate-by-sex-and-marital-status") 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_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_mts_rt_inactivity_rate_by_sex_and_marital_status_2025, title = {Inactivity rate by sex and marital status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-mts-rt-inactivity-rate-by-sex-and-marital-status}} } ``` ## 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_RT_This dataset contains 10,272 observations of inactivity rate by sex and marital status (%) across 36 Asia countries, spanning 1970–2025. The data is sourced from the International Labour Organizations ILOSTAT database under the topic Other measures of labour underutilization. The core indicator is EIP_DWAP_SEX_MTS_RT (Inactivity rate by sex and marital status). It includes fields such as country code, country name, data source, indicator code, sex disaggregation (total, male, female, etc.), marital status classification, observation year, observed value, and data status flags. The dataset is suitable for tabular classification, regression, and time-series forecasting tasks. Data is harmonized by ILO and includes quality notes, e.g., annual frequency, best-source selection.

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中央统计数据库，聚焦于亚洲区域劳动力闲置状况中的“按性别与婚姻状况划分的不活跃率”指标。数据通过调用ILOSTAT REST API，从标准化指标代码EIP_DWAP_SEX_MTS_RT对应的数据接口直接抽取，随后依据亚洲ISO 3166-1 alpha-3国家代码进行地理过滤与聚合。ILO依托国家劳动力调查、家庭收入调查及行政记录等多源数据，遵循国际劳工统计学家会议（ICLS）定义进行微观数据协调，确保跨时间与空间的可比性。最终生成覆盖36个亚洲国家、1970至2025年间共10,272条观测记录的表格型数据集，每条记录均附有来源标签以供追溯。

使用方法

该数据集以Hugging Face Datasets库为标准接口，研究者可通过`load_dataset()`函数一行代码加载至Python环境，并便捷转换为Pandas DataFrame进行后续操作。典型使用方法包括：依据国家代码（如`ref_area`列为'IDN'）筛选特定国家子集，以开展国别纵贯分析；基于时间列`time`与观测值列`obs_value`绘制单指标时间序列曲线；利用数据透视表功能将长格式数据重塑为国家×年份的矩阵，便于面板数据分析或构建预测模型。数据还支持按性别或婚姻状况等分类变量进行分组聚合，满足多元统计与机器学习任务需求。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）统计部门依托ILOSTAT数据库构建，并由Electric Sheep Asia于2025年重新打包发布，聚焦亚洲36个国家1970至2025年间按性别与婚姻状况划分的经济非活动率（%）。作为衡量劳动力未充分利用状况的关键指标，经济非活动率揭示了因家庭责任、教育或健康等原因未参与劳动力市场的群体分布特征，为分析亚洲地区结构性就业矛盾、性别差异与劳动力供给弹性提供了量化基础。该数据集通过整合各国劳动力调查、家庭收入调查及行政记录等多元来源，经国际劳工统计学家会议标准协调后形成纵向可比的时间序列，已成为研究亚洲劳动经济、性别平等与社会保障政策的重要数据资产，推动了亚太区域劳动力市场动态的实证分析。

当前挑战

该数据集面临的核心挑战包括：1）数据异质性与可比性难题——亚洲各国统计体系差异显著，调查频率、定义口径及抽样方法的不同导致跨国纵向比较存在偏倚，需依赖ILO的协调框架进行源数据标记与断点修正；2）缺失数据与稀疏性问题——部分国家年份跨度不均或存在长期缺口，例如阿富汗仅覆盖有限观测值，时间序列的不完整性限制了面板数据分析与预测模型的稳定性；3）细粒度维度离散化挑战——性别与婚姻状况的交互分类导致子组样本量锐减，在建模中易引发稀疏数据下的估计误差，需平衡聚合层级与分析精度；4）数据时效性与更新滞后——来源端调查数据发布周期通常滞后1至2年，最新观测值往往为初步估计或预测值，对实时劳动力监测形成制约。

常用场景

经典使用场景

在劳动经济学与人口统计学交叉研究中，该数据集被广泛用于分析亚洲地区劳动参与率的性别与婚姻状况差异。研究者可借助其包含的36个国家1970-2025年间逾万条观测记录，构建面板数据模型以探究婚姻状况（如已婚、未婚、丧偶等）与性别如何共同塑造个体退出劳动力市场的概率。数据集中细粒度的分类变量（如sex与classif1）使学者能够精确剥离婚姻状态对经济活动人口不活跃率的独立效应，进而评估文化规范、家庭分工与制度环境在其中的调节作用。

解决学术问题

该数据集有力回应了发展经济学中长期悬而未决的议题：亚洲女性劳动参与率为何呈现U型或平缓递减趋势？通过融合婚姻状况维度，研究得以揭示婚姻锁定效应——即已婚女性因家庭照料责任而退出劳动力市场的比例显著高于未婚女性，而这一现象在男性中近乎不存在。这一发现修正了传统人力资本模型对性别差异的简化假设，为理解亚洲特有的低度劳动利用（labour underutilization）模式提供了实证基础，并推动学界将婚姻制度纳为劳动供给函数的核心解释变量。

实际应用

在国际劳工组织（ILO）的决策支持体系中，该数据集被用以监测亚洲各国实现可持续发展目标（SDG）指标8.5.2（失业率）与8.6.1（青年非就业、非教育、非培训比例）的进展。政策制定者利用按性别与婚姻状态分层的不活跃率，识别出最脆弱的亚群体——例如中东国家的离异女性或南亚地区的守寡妇女，从而设计针对性职业技能培训或育儿补贴方案。一些亚洲国家的劳工部已将其作为年度劳动力调查报告的标杆数据源，校准本国生产调查与行政记录之间的统计口径差异。

数据集最近研究

最新研究方向

该数据集基于ILOSTAT官方数据源，聚焦亚洲地区1970至2025年间按性别与婚姻状况划分的劳动不充分利用测度，特别是经济活动人口中非活跃率的动态演变。这一方向为当前全球劳动力市场韧性研究注入了关键的区域性证据，尤其是在后疫情时代非正规就业激增、性别就业差距持续存在的背景下，学者可借助该时间序列数据深入探索婚姻状态如何中度调制劳动退出行为，并与亚洲各国日益收紧的家庭福利政策、人口老龄化浪潮形成交叉分析。此外，对于发展中国家普遍存在的隐性劳动闲置问题，该数据集提供了与传统失业率互补的测量框架，助力构建涵盖性别平等与社会保障制度交互效应的预测模型，推动可持续就业目标在区域政策设计中的量化落地。