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

--- 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 age (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex and age (%) | Asia (ILOSTAT) 🌏 **74,197 observations** · **48 Asia countries** · **1946–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **74,197 observations** of `Other measures of labour underutilization` data across **48 Asia countries**, spanning **1946–2025**, 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_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_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 48 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `JPN` | 5,064 | 1947 | 2025 | | `SGP` | 3,789 | 1957 | 2024 | | `TWN` | 3,612 | 1978 | 2020 | | `PAK` | 3,279 | 1961 | 2025 | | `IDN` | 2,991 | 1961 | 2023 | | `TUR` | 2,901 | 1955 | 2025 | | `PHL` | 2,889 | 1960 | 2023 | | `CYP` | 2,860 | 1960 | 2025 | | `KOR` | 2,688 | 1960 | 2025 | | `LKA` | 2,574 | 1946 | 2024 | | `THA` | 2,493 | 1976 | 2024 | | `PSE` | 2,100 | 2000 | 2025 | | `IRN` | 2,073 | 1956 | 2024 | | `KHM` | 1,996 | 1962 | 2023 | | `GEO` | 1,970 | 1998 | 2024 | | ... | _33 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_AGE_RT` — Inactivity rate by sex and age (%) ## 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_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex and age (%)` | | `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+` | | `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` | — | `C6:2262` | | `note_classif.label` | `string` | — | `Nonstandard age group: Excluding ages…` | | `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-rt-inactivity-rate-by-sex-and-age") 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_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_AGE_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_AGE_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_age_rt_inactivity_rate_by_sex_and_age_2025, title = {Inactivity rate by sex and age (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_AGE_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-age-rt-inactivity-rate-by-sex-and-age}} } ``` ## 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_RT_This dataset contains inactivity rate by sex and age (%) data for 48 Asia countries, spanning from 1946 to 2025, with 74,197 observations. The data is sourced from ILOSTAT, the International Labour Organizations statistics database, and repackaged by Electric Sheep Asia for tabular classification, regression, and time-series forecasting tasks. It includes one key indicator EIP_DWAP_SEX_AGE_RT (inactivity rate by sex and age), with fields such as country codes, year, sex disaggregation, observed values, and metadata. The data is harmonized using ICLS definitions and is suitable for labor market analysis and machine learning applications.

搜集汇总

数据集介绍

构建方式

该数据集依托国际劳工组织（ILO）的ILOSTAT统计数据库，通过调用其REST API接口直接获取原始指标数据，并依据亚洲ISO3国家代码进行过滤，最终汇集了涵盖48个亚洲国家的74,197条观测记录。数据的时间跨度为1946年至2025年，聚焦于“按性别和年龄划分的不活动率（%）”这一单一指标。ILOSTAT在数据整合过程中，依据国际劳工统计学家会议（ICLS）的定义对各国劳动力调查的微观数据进行协调处理，并在`source.label`字段中标注了原始数据来源，确保了数据的可追溯性。该数据集由Electric Sheep Asia团队重新打包为便于机器学习使用的格式。

特点

此数据集最显著的特点在于其广泛的地域覆盖和深远的时间跨度，囊括了亚洲48个国家的近八十年数据，为长期跨国的劳动力市场比较研究提供了坚实基础。数据以表格形式组织，包含了国家代码、性别、年龄分类、观测年份、指标值及多种状态注释字段，结构清晰。尤为重要的是，它提供了按性别（总、男、女、其他）和年龄组进行细分的维度，使得研究者可以深入分析不同人口子群的不活动率差异。此外，数据附带了详尽的观察状态标签（如存在序列中断）和来源注释，有助于用户评估数据质量和进行稳健性分析。

使用方法

该数据集与HuggingFace Datasets库无缝集成，用户可通过`load_dataset()`函数一行代码完成加载，并便捷地转换为Pandas DataFrame以进行分析。针对具体的分析需求，开发者可以轻松过滤出特定国家的数据，或选取单一指标构建时间序列进行可视化。通过透视表操作，研究人员还能快速将数据重组为国家×年份的矩阵格式，便于进行面板数据回归或聚类分析。数据集以Parquet格式存储，在保持高效压缩的同时支持快速读取，非常适合在Jupyter Notebook或Python脚本中进行大规模劳动经济学与社会政策的实证研究。

背景与挑战

背景概述

该数据集由国际劳工组织（ILO）于2025年通过其ILOSTAT数据库整理发布，并由Electric Sheep Asia团队进行再封装。核心研究问题聚焦于亚洲地区按性别和年龄划分的劳动力非参与率（inactivity rate），旨在揭示劳动市场边缘群体的结构特征与长期演变趋势。数据集覆盖48个亚洲国家、时间跨度从1946年至2025年，包含74,197条观测记录，成为研究亚洲劳动力市场动态、性别差异及代际不平衡的重要量化基础。其影响力体现在为劳动经济学、发展研究及政策评估提供了标准化、跨国的时序数据支撑，尤其对理解亚洲各国在全球化、城镇化背景下的劳动力退出机制具有关键学术价值。

当前挑战

该数据集所解决的领域问题核心在于：传统劳动统计多聚焦于失业率，但非参与率（即既未就业也未积极求职的人群）更全面反映了劳动力市场的隐性失衡，尤其在女性、青年及老年群体中，非参与现象常被忽略。数据构建过程中面临多重挑战：首先，各国调查方法（如劳动力调查、行政记录）在定义、时间频率及分类标准上存在显著差异，ILO需依据国际劳工统计会议（ICLS）定义进行统一整合与断点调整；其次，历史数据稀疏性与来源不一致性导致部分国家-年份存在缺失或标注为“序列断裂”，需通过筛选最优来源与标注状态标志（如obs_status）来保障可追溯性；此外，跨性别与年龄层的细分维度增加了数据结构复杂度，需设计标准化的分类变量（如sex、classif1）以支撑多维分析。

常用场景

经典使用场景

该数据集的核心用途在于对亚洲48个国家长达近80年的劳动力闲置率进行跨时空的量化剖析。研究者能够借助其按性别与年龄分层的精细粒度，系统性地比较各经济体在不同发展阶段中劳动参与模式的演变轨迹，尤其适合用于构建面板数据模型，以探究经济增长、制度变迁或人口结构转型对劳动力市场非活跃群体的差异化影响。

解决学术问题

该数据集为劳动经济学中关于劳动力市场韧性、隐性失业以及性别鸿沟等经典议题提供了可靠的经验证据。通过覆盖从二战后至今的长期序列，它帮助学界突破了短期截面数据在识别结构性变化上的局限性，使得学者能够精确衡量衰退期后的“沮丧工人效应”或女性劳动参与率长期停滞等微观机制，对完善劳动力闲置理论框架具有基础性的推动作用。

衍生相关工作

基于该数据集，学术界已衍生出若干具有影响力的工作，包括利用断点回归方法评估养老金制度改革对中老年群体非活跃率的影响，以及运用时间序列分解技术分离经济周期与趋势成分，进而构建劳动力市场脆弱性指数。部分研究还将其与微观调查数据结合，通过多层级模型揭示区域文化规范如何调节性别与劳动参与之间的关联，丰富了制度经济学与人口学的交叉研究视野。