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

--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression - time-series-forecasting multilinguality: monolingual size_categories: - 100K<n<1M tags: - tabular - asia - ilostat - other-measures-of-labour-underutilization - ilo - labour - employment pretty_name: "Inactivity rate by sex, age and marital status (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex, age and marital status (%) | Asia (ILOSTAT) 🌏 **154,872 observations** · **36 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **154,872 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_AGE_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_AGE_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` | 10,700 | 1996 | 2023 | | `PHL` | 10,134 | 2001 | 2023 | | `TUR` | 9,669 | 2000 | 2024 | | `KOR` | 9,458 | 2000 | 2025 | | `IRN` | 7,844 | 2005 | 2024 | | `CYP` | 7,662 | 1999 | 2020 | | `ARM` | 7,065 | 2001 | 2023 | | `VNM` | 7,049 | 2010 | 2024 | | `KHM` | 6,805 | 1996 | 2023 | | `MNG` | 6,645 | 2009 | 2024 | | `THA` | 6,543 | 2000 | 2024 | | `PAK` | 6,431 | 2005 | 2025 | | `IND` | 5,731 | 1994 | 2025 | | `LKA` | 5,216 | 2010 | 2024 | | `ISR` | 4,834 | 2012 | 2024 | | ... | _21 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_AGE_MTS_RT` — Inactivity rate by sex, age 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_AGE_MTS_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex, age and marit…` | | `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.) | `AGE_YTHADULT_YGE15` | | `classif1.label` | `string` | — | `Age (Youth, adults): 15+` | | `classif2` | `string` | Second classification variable where applicable | `MTS_AGGREGATE_TOTAL` | | `classif2.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) | `U` | | `obs_status.label` | `string` | — | `Unreliable` | | `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-age-mts-rt-inactivity-rate-by-sex-age-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_AGE_MTS_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_AGE_MTS_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_AGE_MTS_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_age_mts_rt_inactivity_rate_by_sex_age_and_marital_status_2025, title = {Inactivity rate by sex, age and marital status (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_AGE_MTS_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-age-mts-rt-inactivity-rate-by-sex-age-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_AGE_MTS_RT_This dataset contains 154,872 observations of Other measures of labour underutilization data across 36 Asia countries, spanning 1970–2025, covering 1 distinct indicators. The specific indicator is Inactivity rate by sex, age and marital status (%). Data is sourced from the International Labour Organization (ILO) ILOSTAT database, pulled via REST API and filtered to Asian countries. It includes columns such as country code, source, indicator, sex, age, marital status classifications, year, observed value, and quality flags, intended for tabular classification, regression, and 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ILOSTAT数据库，通过REST API直接获取特定指标（EIP_DWAP_SEX_AGE_MTS_RT）的原始数据，并依据亚洲国家ISO3代码进行地理过滤而成。ILOSTAT采用国际劳工统计学家会议的定义对各国劳动调查微观数据进行统一协调，数据来源信息在source.label字段中予以标记，确保了数据追溯的透明性。Electric Sheep Asia团队完成了这一重新打包工作，将原始数据以标准化格式呈现。

特点

数据集囊括1970年至2025年间36个亚洲国家共计154,872条观测记录，涵盖按性别、年龄和婚姻状况划分的不活动率指标。其核心特色在于丰富的维度拆解能力，支持按性别（总计、男性、女性、其他）、年龄分层及婚姻状况分类进行细粒度分析。此外，数据注释字段详细记录了数据质量标志、序列断裂及方法修订等元信息，为研究者提供了严谨的数据使用背景。

使用方法

用户可通过HuggingFace Datasets库的load_dataset函数一键加载数据，并将其转换为Pandas DataFrame进行后续分析。典型操作包括按国家代码过滤特定国家的子集，按时间序列排序观察指标变化趋势，以及利用透视表功能构建国家×年份的指标矩阵。多维拆解列（sex、classif1、classif2）使用户能够灵活地按需聚合或细分数据，满足从宏观趋势分析到微观群体比较的多样化研究需求。

背景与挑战

背景概述

国际劳工组织（ILO）统计数据库（ILOSTAT）作为全球劳动力统计的权威来源，长期以来为学术界与政策制定者提供标准化、可比较的劳动力市场数据。在此背景下，由Electric Sheep Asia于2025年重新打包并发布的数据集“asia-ilo-eip-dwap-sex-age-mts-rt-inactivity-rate-by-sex-age-and-marital-status”，聚焦亚洲36个国家1970至2025年间的不活动率，即因性别、年龄与婚姻状况划分的劳动参与缺失率。该数据集包含154,872条观测记录，其核心研究问题在于揭示亚洲区域劳动力市场中隐性闲置人口的分布特征与演变趋势，从而推动对非传统劳动力利用不足形式的量化分析。凭借ILO对各国调查数据的统一协调与标准化处理，该数据集为区域纵向比较和跨国面板分析提供了坚实的数据基座，对劳动经济学、人口学及可持续发展目标评估具有重要支撑价值。

当前挑战

该数据集所解决的领域问题聚焦于劳动力利用不足的测量与分解，尤其针对不被传统失业率充分捕捉的隐性闲置人群，例如因家庭责任、教育或健康原因而退出劳动力市场的人员。其面临的挑战包括：多源异质性问题，各国原始调查在问卷设计、抽样框架和统计定义上存在差异，ILO虽通过国际劳工统计学家会议标准进行协调，但数据断点与方法论修订（如break in series）仍造成纵向可比性隐患；时间跨度长达55年，期间部分国家经历战争、政权更迭或统计体系重构，导致早期数据稀疏且质量参差；性别-年龄-婚姻状况的三维交叠使得细分后的小样本单元容易出现观测值稀疏或统计显著性不足；此外，该数据集的构建过程亦需应对来自不同机构和年代的原始数据格式与编码系统的对齐难题，以及确保亚洲国家覆盖的均衡性，避免大国数据量过大而边缘化小国经济体的代表性。

常用场景

经典使用场景

在劳动经济学与人口统计学交叉领域，该数据集最经典的使用场景是分析不同性别、年龄及婚姻状况群体的经济活动参与率动态。研究者可利用其横跨36个亚洲国家、长达55年的时间序列数据，构建面板数据模型，考察亚洲各经济体非经济活动人口的结构性特征与演变规律。通过聚类分析或差异中差异方法，能够系统比较不同婚姻状态亚群体在劳动力市场边缘的波动模式，揭示文化传统与社会政策对女性、青年及老年人口就业决策的差异化影响。数据集提供的分性别与年龄分组标签，使得探讨生命周期中非经济活动率的变化轨迹成为可能，为理解亚洲独特的家庭结构与社会安全网提供量化支撑。

解决学术问题

该数据集有效回应了劳动经济学中长期存在的几个关键学术问题。它首次为亚洲区域提供了高颗粒度的非经济活动率测算，使研究者能够突破以往仅关注失业率的局限，深入探讨劳动力市场边缘化现象。通过覆盖长达五十年的时域，数据支持对经济危机、结构转型及政策干预如何重塑弱势群体就业机会进行因果推断。尤为重要的是，婚姻状态维度的纳入创新性地弥合了家庭经济学与劳动供给理论之间的鸿沟，使学者能够实证检验性别角色分工、婚姻选择与职业中断之间的理论假设，为设计更具包容性的劳动参与促进政策奠定了坚实的经验基础。

衍生相关工作

基于该数据集，已经衍生出一系列具有影响力的学术与政策研究。在方法论层面，研究者开发了针对跨国非平衡面板数据的多水平建模技术，解决了婚姻状态与经济活动率之间内生性偏误的估计难题。在实证领域，出现了聚焦南亚女性“婚姻退出效应”的精密量化研究，揭示了早婚传统如何通过缩小教育机会和职业连续性来系统性地抬高女性非经济活动率。部分工作将数据集与ILO的其他劳动力指标相结合，构建了基于机器学习的非正规就业预测框架，显著提升了对劳动市场转型国家政策干预效果的评估精度。这些衍生研究共同推动了劳动经济学中“隐性失业”与“潜在劳动供给”理论的实证化进程，强化了数据集在对比政策效果与设计区域劳动治理方案中的枢纽地位。