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

--- 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 education (%) | Asia (ILOSTAT)" --- # Inactivity rate by sex, age and education (%) | Asia (ILOSTAT) 🌏 **286,170 observations** · **42 Asia countries** · **1970–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*      ## TL;DR This dataset contains **286,170 observations** of `Other measures of labour underutilization` data across **42 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_EDU_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_EDU_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 42 Asia countries · top rows shown below, sorted by row count: | Country | Rows | First year | Last year | |---------|-----:|-----------:|----------:| | `IDN` | 19,652 | 1990 | 2023 | | `PSE` | 16,796 | 2000 | 2025 | | `IRN` | 15,889 | 2005 | 2024 | | `CYP` | 15,856 | 1999 | 2025 | | `KOR` | 15,456 | 2000 | 2025 | | `TUR` | 13,700 | 2000 | 2025 | | `THA` | 12,561 | 2000 | 2024 | | `KHM` | 12,216 | 1996 | 2023 | | `MNG` | 11,927 | 2003 | 2024 | | `PAK` | 11,317 | 2005 | 2025 | | `VNM` | 11,217 | 2010 | 2024 | | `ISR` | 10,532 | 2009 | 2024 | | `GEO` | 9,855 | 2009 | 2024 | | `ARM` | 9,745 | 2001 | 2023 | | `IND` | 9,414 | 1994 | 2025 | | ... | _27 more countries_ | | | ## Indicators (sample) - `EIP_DWAP_SEX_AGE_EDU_RT` — Inactivity rate by sex, age and education (%) ## 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_EDU_RT` | | `indicator.label` | `string` | Indicator name in English | `Inactivity rate by sex, age and educa…` | | `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 | `EDU_AGGREGATE_TOTAL` | | `classif2.label` | `string` | — | `Education (Aggregate levels): 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-age-edu-rt-inactivity-rate-by-sex-age-and-education") 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_EDU_RT"] .sort_values("time")) sample.plot(x="time", y="obs_value", title="EIP_DWAP_SEX_AGE_EDU_RT") ``` ### Pivot to country × year matrix ```python matrix = (df[df["indicator"] == "EIP_DWAP_SEX_AGE_EDU_RT"] .pivot_table(index="time", columns="ref_area", values="obs_value")) print(matrix.tail()) ``` ## Citation ```bibtex @misc{asia_ilo_eip_dwap_sex_age_edu_rt_inactivity_rate_by_sex_age_and_education_2025, title = {Inactivity rate by sex, age and education (%) | Asia (ILOSTAT)}, author = {International Labour Organization (ILO)}, year = {2025}, url = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EIP_DWAP_SEX_AGE_EDU_RT}, publisher = {HuggingFace Datasets, repackaged by Electric Sheep Asia}, howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-eip-dwap-sex-age-edu-rt-inactivity-rate-by-sex-age-and-education}} } ``` ## 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_EDU_RT_This dataset contains statistics on the inactivity rate by sex, age, and education (%) in Asia, sourced from the International Labour Organization (ILO) ILOSTAT database. It covers 42 Asian countries from 1970 to 2025, with 286,170 observations focusing on a single indicator: EIP_DWAP_SEX_AGE_EDU_RT (Inactivity rate by sex, age and education). The data is retrieved via the ILOSTAT REST API and filtered to include only Asian countries. The dataset provides detailed columns such as country codes, sources, indicator codes, sex disaggregation (total, male, female, etc.), age and education classifications, observation year, observed values, and data status flags. The data is published at an annual frequency and adheres to ILO statistical standards, suitable for tasks like tabular classification, regression, and time-series forecasting. It is repackaged by Electric Sheep Asia for ease of use in machine learning research and applications.

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统计数据库，聚焦于亚洲地区劳动力利用不足的衡量指标。通过调用ILOSTAT的REST API，提取指标“EIP_DWAP_SEX_AGE_EDU_RT”（按性别、年龄和教育程度划分的不活动率）的原始数据，并依据亚洲ISO3国家代码进行筛选与过滤。ILOSTAT采用国际劳工统计学家会议（ICLS）定义对各国劳动力调查的微观数据进行协调统一，数据来源在“source.label”字段中加以标注，以确保可追溯性。最终，Electric Sheep Asia团队将清洗后的数据重新封装为Parquet格式，提供286,170条观测记录，覆盖42个亚洲国家，时间跨度从1970年至2025年。

特点

该数据集包含单一但高度细化的指标，即按性别、年龄和教育程度细分的不活动率，并提供了丰富的维度拆解字段，包括性别（共4个类别）、年龄分组及教育水平分类。每条记录还附带了观测状态标记、方法变更或数据中断的注释信息，提升了数据使用的透明度。数据集为年度频率，整合了来自各国劳动力调查等多源数据，并优先选用ILO认定的‘最佳来源’，确保了跨国家、跨时间序列的一致性。其地理覆盖广泛，囊括了从印度、印度尼西亚到韩国、土耳其等多样化的亚洲经济体，为区域比较和纵向研究提供了坚实的数据基础。

使用方法

用户可通过HuggingFace Datasets库直接加载该数据集，使用`load_dataset("electricsheepasia/asia-ilo-eip-dwap-sex-age-edu-rt-inactivity-rate-by-sex-age-and-education")`即可获取训练集并转换为Pandas DataFrame进行后续分析。数据集结构清晰，包含国家代码、时间、观测值及各类分类维度列，便于按国家筛选（如`df[df["ref_area"] == "IDN"]`），或对单一指标进行时间序列可视化。此外，用户还可通过透视表操作构建国家×年份的矩阵形式，以支持面板数据分析或计量模型应用。数据以MIT许可证发布，鼓励在学术研究中引用原始ILO来源及Electric Sheep Asia的重打包版本。

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重新打包为HuggingFace格式，聚焦亚洲42个国家1970至2025年间按性别、年龄与教育程度划分的经济不活跃率（%）。作为劳动力市场研究的重要指标，经济不活跃率揭示了未参与就业或失业人群的结构性特征，对理解亚洲地区劳动力市场的动态变化、性别差异以及教育回报率具有关键价值。该数据集提供了超过28万条观测记录，为跨国家、跨时间段的比较分析奠定了坚实基础，在劳动经济学、发展经济学及公共政策研究领域产生了广泛影响。

当前挑战

该数据集所解决的领域问题在于量化亚洲地区劳动力市场中因性别、年龄与教育程度导致的不活跃群体分布，为政策制定者识别结构性失业与非参与模式提供依据。构建过程中面临的核心挑战包括：1) 数据源的异构性与标准化问题，ILOSTAT整合了各国劳动力调查、家庭收入调查等多元来源，需通过国际劳工统计学家会议定义进行协调整合，以确保跨国家可比性；2) 数据质量标注与缺失值处理，观测值中包含‘不可靠’状态标记及方法修订导致的序列中断，需要在分析时谨慎过滤；3) 分类维度复杂，需同时处理性别、年龄分组与教育水平的交叉细分，增加了数据清洗与建模的难度。

常用场景

经典使用场景

该数据集覆盖了42个亚洲国家1970年至2025年间按性别、年龄和教育程度划分的不活动率数据，是劳动经济学与人口统计学交叉研究的宝贵资源。经典使用场景包括利用时间序列特征对亚洲劳动力市场的结构性变化进行建模，预测不同群体长期脱离劳动参与的趋势，并通过分类或回归任务辨识影响不活动率的关键社会经济变量。数据集中包含的`sex`、`classif1`与`classif2`等维度信息，使得研究者能够精细剖析女性、青年及低教育群体等脆弱人群的劳动参与缺失现象，为政策评估提供量化依据。

实际应用

在现实中，该数据集为国际组织与各国决策者评估劳动政策成效、部署积极就业干预提供了重要工具。应用场景涵盖监测可持续发展目标中体面劳动指标的进展，识别急待提升教育投资回报率或缩减性别参与鸿沟的重点区域。政府劳动部门可利用这些数据设计针对性的技能培训计划，而发展机构则能据此优化资金投放策略，使对弱势群体支持资源精准配置于不活动率持续高企的社群，切实缓解亚洲区域的人力资本闲置困局。

衍生相关工作

该数据集的问世催生了一系列关注亚洲劳动力市场不活跃群体研究范式的前沿工作。相关经典工作包括构建多国贝叶斯层次模型以估算不活动率的动态阈值及其与文化规范指标的交互效应；也有研究将其与教育成就分布数据结合，训练深度学习网络模拟教育扩张对女性劳动供给的延迟反馈。此外，基于该数据衍生的可解释机器学习框架被用于识别各国不活动率异常的年份，从而辅助国际对比中对政策冲击效应的因果推断，推动了发展中国家劳动统计方法论的革新。